From 63724279a465ae46959a9feb0fd8c93e21aa4761 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 24 Nov 2021 16:01:58 +0100
Subject: [PATCH 001/243] Snakefile: export conda environment

---
 Snakefile | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/Snakefile b/Snakefile
index 76dca9ab..7d7acb70 100644
--- a/Snakefile
+++ b/Snakefile
@@ -411,6 +411,14 @@ rule copy_config:
     script: "scripts/copy_config.py"
 
 
+rule copy_conda_env:
+    output: SDIR + '/configs/environment.yaml'
+    threads: 1
+    resources: mem_mb=500
+    benchmark: SDIR + "/benchmarks/copy_conda_env"
+    shell: "conda env export -f {output} --no-builds"
+
+
 rule make_summary:
     input:
         overrides="data/override_component_attrs",
@@ -468,6 +476,7 @@ if config["foresight"] == "overnight":
             network=RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
             costs=CDIR + "costs_{planning_horizons}.csv",
             config=SDIR + '/configs/config.yaml'
+            env=SDIR + '/configs/environment.yaml'
         output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
         shadow: "shallow"
         log:

From c273eb7f2cc6b1cb87512c5e0a602175e85e3d6b Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 23 Jan 2022 14:20:18 +0100
Subject: [PATCH 002/243] plot_network: legends, EqualEarth projection, w/wo
 retrofit compatibility

---
 scripts/plot_network.py | 407 ++++++++++++++++++++++++++--------------
 1 file changed, 268 insertions(+), 139 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 9b8cddc3..62ac5ad6 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -6,13 +6,14 @@ import matplotlib.pyplot as plt
 import cartopy.crs as ccrs
 
 from matplotlib.legend_handler import HandlerPatch
-from matplotlib.patches import Circle, Ellipse
+from matplotlib.patches import Circle, Patch
+from pypsa.plot import projected_area_factor
 
 from make_summary import assign_carriers
 from plot_summary import rename_techs, preferred_order
 from helper import override_component_attrs
 
-plt.style.use('ggplot')
+plt.style.use(['ggplot', "../matplotlibrc"])
 
 
 def rename_techs_tyndp(tech):
@@ -36,31 +37,65 @@ def rename_techs_tyndp(tech):
     else:
         return tech
 
+class HandlerCircle(HandlerPatch):
+    """
+    Legend Handler used to create circles for legend entries. 
+    
+    This handler resizes the circles in order to match the same dimensional 
+    scaling as in the applied axis.
+    """
+    def create_artists(
+        self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans
+    ):
+        fig = legend.get_figure()
+        ax = legend.axes
 
-def make_handler_map_to_scale_circles_as_in(ax, dont_resize_actively=False):
-    fig = ax.get_figure()
+        unit = np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[0][1]
+        radius = orig_handle.get_radius() * unit * (72 / fig.dpi)
+        center = 5 - xdescent, 3 - ydescent
+        p = plt.Circle(center, radius)
+        self.update_prop(p, orig_handle, legend)
+        p.set_transform(trans)
+        return [p]
 
-    def axes2pt():
-        return np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[0] * (72. / fig.dpi)
 
-    ellipses = []
-    if not dont_resize_actively:
-        def update_width_height(event):
-            dist = axes2pt()
-            for e, radius in ellipses:
-                e.width, e.height = 2. * radius * dist
-        fig.canvas.mpl_connect('resize_event', update_width_height)
-        ax.callbacks.connect('xlim_changed', update_width_height)
-        ax.callbacks.connect('ylim_changed', update_width_height)
+def add_legend_circles(ax, sizes, labels, scale=1, srid=None, patch_kw={}, legend_kw={}):
+    
+    if srid is not None:
+        area_correction = projected_area_factor(ax, n.srid)**2 
+        print(area_correction)
+        sizes = [s * area_correction for s in sizes]
+    
+    handles = make_legend_circles_for(sizes, scale, **patch_kw)
+    
+    legend = ax.legend(
+        handles, labels,
+        handler_map={Circle: HandlerCircle()},
+        **legend_kw
+    )
 
-    def legend_circle_handler(legend, orig_handle, xdescent, ydescent,
-                              width, height, fontsize):
-        w, h = 2. * orig_handle.get_radius() * axes2pt()
-        e = Ellipse(xy=(0.5 * width - 0.5 * xdescent, 0.5 *
-                        height - 0.5 * ydescent), width=w, height=w)
-        ellipses.append((e, orig_handle.get_radius()))
-        return e
-    return {Circle: HandlerPatch(patch_func=legend_circle_handler)}
+    ax.add_artist(legend)
+    
+
+def add_legend_lines(ax, sizes, labels, scale=1, patch_kw={}, legend_kw={}):
+    
+    handles = [plt.Line2D([0], [0], linewidth=s/scale, **patch_kw) for s in sizes]
+    
+    legend = ax.legend(
+        handles, labels,
+        **legend_kw
+    )
+    
+    ax.add_artist(legend)
+
+
+def add_legend_patches(ax, colors, labels, patch_kw={}, legend_kw={}):
+    
+    handles = [Patch(facecolor=c, **patch_kw) for c in colors]
+    
+    legend = ax.legend(handles, labels, **legend_kw)
+
+    ax.add_artist(legend)
 
 
 def make_legend_circles_for(sizes, scale=1.0, **kw):
@@ -80,6 +115,8 @@ def assign_location(n):
 def plot_map(network, components=["links", "stores", "storage_units", "generators"],
              bus_size_factor=1.7e10, transmission=False):
 
+    tech_colors = snakemake.config['plotting']['tech_colors']
+
     n = network.copy()
     assign_location(n)
     # Drop non-electric buses so they don't clutter the plot
@@ -109,7 +146,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     costs = costs[new_columns]
 
     for item in new_columns:
-        if item not in snakemake.config['plotting']['tech_colors']:
+        if item not in tech_colors:
             print("Warning!",item,"not in config/plotting/tech_colors")
 
     costs = costs.stack()  # .sort_index()
@@ -129,6 +166,11 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     # make sure they are removed from index
     costs.index = pd.MultiIndex.from_tuples(costs.index.values)
 
+    threshold = 100e6 # 100 mEUR/a
+    carriers = costs.sum(level=1)
+    carriers = carriers.where(carriers > threshold).dropna()
+    carriers = list(carriers.index)
+
     # PDF has minimum width, so set these to zero
     line_lower_threshold = 500.
     line_upper_threshold = 1e4
@@ -140,23 +182,23 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         # should be zero
         line_widths = n.lines.s_nom_opt - n.lines.s_nom
         link_widths = n.links.p_nom_opt - n.links.p_nom
-        title = "Transmission reinforcement"
+        title = "Added grid"
 
         if transmission:
             line_widths = n.lines.s_nom_opt
             link_widths = n.links.p_nom_opt
             linewidth_factor = 2e3
             line_lower_threshold = 0.
-            title = "Today's transmission"
+            title = "Today's grid"
     else:
         line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
         link_widths = n.links.p_nom_opt - n.links.p_nom_min
-        title = "Transmission reinforcement"
+        title = "Added grid"
 
         if transmission:
             line_widths = n.lines.s_nom_opt
             link_widths = n.links.p_nom_opt
-            title = "Total transmission"
+            title = "Total grid"
 
     line_widths[line_widths < line_lower_threshold] = 0.
     link_widths[link_widths < line_lower_threshold] = 0.
@@ -164,12 +206,12 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     line_widths[line_widths > line_upper_threshold] = line_upper_threshold
     link_widths[link_widths > line_upper_threshold] = line_upper_threshold
 
-    fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
+    fig, ax = plt.subplots(subplot_kw={"projection": ccrs.EqualEarth()})
     fig.set_size_inches(7, 6)
 
     n.plot(
         bus_sizes=costs / bus_size_factor,
-        bus_colors=snakemake.config['plotting']['tech_colors'],
+        bus_colors=tech_colors,
         line_colors=ac_color,
         link_colors=dc_color,
         line_widths=line_widths / linewidth_factor,
@@ -177,45 +219,63 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         ax=ax,  **map_opts
     )
 
-    handles = make_legend_circles_for(
-        [5e9, 1e9],
-        scale=bus_size_factor,
-        facecolor="gray"
-    )
+    sizes = [20, 10, 5]
+    labels = [f"{s} bEUR/a" for s in sizes]
 
-    labels = ["{} bEUR/a".format(s) for s in (5, 1)]
-
-    l2 = ax.legend(
-        handles, labels,
+    legend_kw = dict(
         loc="upper left",
-        bbox_to_anchor=(0.01, 1.01),
-        labelspacing=1.0,
+        bbox_to_anchor=(0.01, 1.06),
+        labelspacing=0.8,
         frameon=False,
+        handletextpad=0,
         title='System cost',
-        handler_map=make_handler_map_to_scale_circles_as_in(ax)
     )
 
-    ax.add_artist(l2)
+    add_legend_circles(
+        ax,
+        sizes,
+        labels,
+        scale=bus_size_factor/1e9,
+        srid=n.srid,
+        patch_kw=dict(facecolor="lightgrey"),
+        legend_kw=legend_kw
+    )
 
-    handles = []
-    labels = []
+    sizes = [10, 5]
+    labels = [f"{s} GW" for s in sizes]
 
-    for s in (10, 5):
-        handles.append(plt.Line2D([0], [0], color=ac_color,
-                                  linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
-
-    l1_1 = ax.legend(
-        handles, labels,
+    legend_kw = dict(
         loc="upper left",
-        bbox_to_anchor=(0.22, 1.01),
+        bbox_to_anchor=(0.27, 1.06),
         frameon=False,
         labelspacing=0.8,
-        handletextpad=1.5,
+        handletextpad=1,
         title=title
     )
 
-    ax.add_artist(l1_1)
+    add_legend_lines(
+        ax,
+        sizes,
+        labels,
+        scale=linewidth_factor/1e3,
+        patch_kw=dict(color='lightgrey'),
+        legend_kw=legend_kw
+    )
+    
+    legend_kw = dict(
+        bbox_to_anchor=(1.55, 1.04),
+        frameon=False,
+    )
+
+    colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
+    labels = carriers + ["HVAC line", "HVDC link"]
+
+    add_legend_patches(
+        ax,
+        colors,
+        labels,
+        legend_kw=legend_kw,
+    )
 
     fig.savefig(
         snakemake.output.map,
@@ -226,6 +286,8 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
 
 def plot_h2_map(network):
 
+    tech_colors = snakemake.config['plotting']['tech_colors']
+
     n = network.copy()
     if "H2 pipeline" not in n.links.carrier.unique():
         return
@@ -240,7 +302,9 @@ def plot_h2_map(network):
     # Drop non-electric buses so they don't clutter the plot
     n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
 
-    elec = n.links[n.links.carrier.isin(["H2 Electrolysis", "H2 Fuel Cell"])].index
+    carriers = ["H2 Electrolysis", "H2 Fuel Cell"]
+
+    elec = n.links[n.links.carrier.isin(carriers)].index
 
     bus_sizes = n.links.loc[elec,"p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum() / bus_size_factor
 
@@ -253,20 +317,28 @@ def plot_h2_map(network):
 
     h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
 
-    positive_order = h2_retro.bus0 < h2_retro.bus1
-    h2_retro_p = h2_retro[positive_order]
-    swap_buses = {"bus0": "bus1", "bus1": "bus0"}
-    h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
-    h2_retro = pd.concat([h2_retro_p, h2_retro_n])
+    if not h2_retro.empty:
 
-    h2_retro.index = h2_retro.apply(
-        lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
-        axis=1
-    )
+        positive_order = h2_retro.bus0 < h2_retro.bus1
+        h2_retro_p = h2_retro[positive_order]
+        swap_buses = {"bus0": "bus1", "bus1": "bus0"}
+        h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
+        h2_retro = pd.concat([h2_retro_p, h2_retro_n])
 
-    h2_retro = h2_retro["p_nom_opt"]
+        h2_retro.index = h2_retro.apply(
+            lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
+            axis=1
+        )
 
-    link_widths_total = (h2_new + h2_retro) / linewidth_factor
+        h2_retro = h2_retro["p_nom_opt"]
+
+        h2_total = h2_new + h2_retro
+
+    else:
+
+        h2_total = h2_new
+
+    link_widths_total = h2_total / linewidth_factor
     link_widths_total = link_widths_total.groupby(level=0).sum().reindex(n.links.index).fillna(0.)
     link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
 
@@ -279,13 +351,17 @@ def plot_h2_map(network):
 
     fig, ax = plt.subplots(
         figsize=(7, 6),
-        subplot_kw={"projection": ccrs.PlateCarree()}
+        subplot_kw={"projection": ccrs.EqualEarth()}
     )
+
+    color_h2_pipe = '#a2f0f2'
+    color_retrofit = '#72d3d6'
     
     n.plot(
+        geomap=True,
         bus_sizes=bus_sizes,
-        bus_colors=snakemake.config['plotting']['tech_colors'],
-        link_colors='#a2f0f2',
+        bus_colors=tech_colors,
+        link_colors=color_h2_pipe,
         link_widths=link_widths_total,
         branch_components=["Link"],
         ax=ax,
@@ -293,54 +369,70 @@ def plot_h2_map(network):
     )
 
     n.plot(
-        geomap=False,
+        geomap=True, # set False in PyPSA 0.19
         bus_sizes=0,
-        link_colors='#72d3d6',
+        link_colors=color_retrofit,
         link_widths=link_widths_retro,
         branch_components=["Link"],
         ax=ax,
-        **map_opts
+        # color_geomap=False, # needs PyPSA 0.19
+        boundaries=map_opts["boundaries"]
     )
 
-    handles = make_legend_circles_for(
-        [50000, 10000],
-        scale=bus_size_factor,
-        facecolor='grey'
-    )
+    sizes = [50, 10]
+    labels = [f"{s} GW" for s in sizes]
 
-    labels = ["{} GW".format(s) for s in (50, 10)]
-
-    l2 = ax.legend(
-        handles, labels,
+    legend_kw = dict(
         loc="upper left",
-        bbox_to_anchor=(-0.03, 1.01),
-        labelspacing=1.0,
+        bbox_to_anchor=(0, 1),
+        labelspacing=0.8,
+        handletextpad=0,
         frameon=False,
-        title='Electrolyzer capacity',
-        handler_map=make_handler_map_to_scale_circles_as_in(ax)
     )
 
-    ax.add_artist(l2)
+    add_legend_circles(ax, sizes, labels,
+        scale=bus_size_factor/1e3,
+        srid=n.srid,
+        patch_kw=dict(facecolor='lightgrey'),
+        legend_kw=legend_kw
+    )
 
-    handles = []
-    labels = []
+    sizes = [50, 10]
+    labels = [f"{s} GW" for s in sizes]
 
-    for s in (50, 10):
-        handles.append(plt.Line2D([0], [0], color="grey",
-                                  linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
-
-    l1_1 = ax.legend(
-        handles, labels,
+    legend_kw = dict(
         loc="upper left",
-        bbox_to_anchor=(0.28, 1.01),
+        bbox_to_anchor=(0.23, 1),
         frameon=False,
         labelspacing=0.8,
-        handletextpad=1.5,
-        title='H2 pipeline capacity'
+        handletextpad=1,
     )
 
-    ax.add_artist(l1_1)
+    add_legend_lines(
+        ax,
+        sizes,
+        labels,
+        scale=linewidth_factor/1e3,
+        patch_kw=dict(color='lightgrey'),
+        legend_kw=legend_kw,
+    )
+
+    colors = [tech_colors[c] for c in carriers] + [color_h2_pipe, color_retrofit]
+    labels = carriers + ["H2 pipeline (total)", "H2 pipeline (repurposed)"]
+
+    legend_kw = dict(
+        loc="upper left",
+        bbox_to_anchor=(0, 1.13),
+        ncol=2,
+        frameon=False,
+    )
+
+    add_legend_patches(
+        ax,
+        colors,
+        labels,
+        legend_kw=legend_kw
+    )
 
     fig.savefig(
         snakemake.output.map.replace("-costs-all","-h2_network"),
@@ -400,89 +492,126 @@ def plot_ch4_map(network):
     link_widths_used =  max_usage / linewidth_factor
     link_widths_used[max_usage < line_lower_threshold] = 0.
 
-    link_color_used = n.links.carrier.map({"gas pipeline": "#f08080",
-                                           "gas pipeline new": "#c46868"})
+    tech_colors = snakemake.config['plotting']['tech_colors']
+
+    pipe_colors = {
+        "gas pipeline": "#f08080",
+        "gas pipeline new": "#c46868",
+        "gas pipeline (2020)": 'lightgrey',
+        "gas pipeline (available)": '#e8d1d1',
+    }
+
+    link_color_used = n.links.carrier.map(pipe_colors)
 
     n.links.bus0 = n.links.bus0.str.replace(" gas", "")
     n.links.bus1 = n.links.bus1.str.replace(" gas", "")
 
-    tech_colors = snakemake.config['plotting']['tech_colors']
-
     bus_colors = {
         "fossil gas": tech_colors["fossil gas"],
         "methanation": tech_colors["methanation"],
         "biogas": "seagreen"
     }
 
-    fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.PlateCarree()})
+    fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.EqualEarth()})
 
     n.plot(
         bus_sizes=bus_sizes,
         bus_colors=bus_colors,
-        link_colors='lightgrey',
+        link_colors=pipe_colors['gas pipeline (2020)'],
         link_widths=link_widths_orig,
         branch_components=["Link"],
         ax=ax, 
+        geomap=True,
         **map_opts
     )
 
     n.plot(
-        geomap=False,
+        geomap=True, # set False in PyPSA 0.19
         ax=ax,
         bus_sizes=0.,
-        link_colors='#e8d1d1',
+        link_colors=pipe_colors['gas pipeline (available)'],
         link_widths=link_widths_rem,
         branch_components=["Link"],
-        **map_opts
+        # color_geomap=False, # needs PyPSA 0.19
+        boundaries=map_opts["boundaries"]
     )
 
     n.plot(
-        geomap=False,
+        geomap=True, # set False in PyPSA 0.19
         ax=ax,
         bus_sizes=0.,
         link_colors=link_color_used,
         link_widths=link_widths_used,
         branch_components=["Link"],
-        **map_opts
+        # color_geomap=False, # needs PyPSA 0.19
+        boundaries=map_opts["boundaries"]
     )
 
-    handles = make_legend_circles_for(
-        [10e6, 100e6],
-        scale=bus_size_factor,
-        facecolor='grey'
-    )
-    labels = ["{} TWh".format(s) for s in (10, 100)]
+    sizes = [100, 10]
+    labels = [f"{s} TWh" for s in sizes]
     
-    l2 = ax.legend(
-        handles, labels,
+    legend_kw = dict(
         loc="upper left",
-        bbox_to_anchor=(-0.03, 1.01),
-        labelspacing=1.0,
+        bbox_to_anchor=(0, 1.03),
+        labelspacing=0.8,
         frameon=False,
-        title='gas generation',
-        handler_map=make_handler_map_to_scale_circles_as_in(ax)
+        handletextpad=1,
+        title='Gas Sources',
     )
     
-    ax.add_artist(l2)
+    add_legend_circles(
+        ax,
+        sizes,
+        labels,
+        scale=bus_size_factor/1e6,
+        srid=n.srid,
+        patch_kw=dict(facecolor='lightgrey'),
+        legend_kw=legend_kw,
+    )
 
-    handles = []
-    labels = []
-
-    for s in (50, 10):
-        handles.append(plt.Line2D([0], [0], color="grey", linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
+    sizes = [50, 10]
+    labels = [f"{s} GW" for s in sizes]
     
-    l1_1 = ax.legend(
-        handles, labels,
+    legend_kw = dict(
         loc="upper left",
-        bbox_to_anchor=(0.28, 1.01),
+        bbox_to_anchor=(0.25, 1.03),
         frameon=False,
         labelspacing=0.8,
-        handletextpad=1.5,
-        title='gas pipeline used capacity'
+        handletextpad=1,
+        title='Gas Pipeline'
     )
     
-    ax.add_artist(l1_1)
+    add_legend_lines(
+        ax,
+        sizes,
+        labels,
+        scale=linewidth_factor/1e3,
+        patch_kw=dict(color='lightgrey'),
+        legend_kw=legend_kw,
+    )
+
+    colors = list(pipe_colors.values()) + list(bus_colors.values())
+    labels = list(pipe_colors.keys()) + list(bus_colors.keys())
+  
+    # legend on the side
+    # legend_kw = dict(
+    #     bbox_to_anchor=(1.47, 1.04),
+    #     frameon=False,
+    # )
+
+    legend_kw = dict(
+        loc='upper left',
+        bbox_to_anchor=(0, 1.24),
+        ncol=2,
+        frameon=False,
+    )
+
+    add_legend_patches(
+        ax,
+        colors,
+        labels,
+        legend_kw=legend_kw,
+    )
 
     fig.savefig(
         snakemake.output.map.replace("-costs-all","-ch4_network"),
@@ -500,13 +629,13 @@ def plot_map_without(network):
 
     fig, ax = plt.subplots(
         figsize=(7, 6),
-        subplot_kw={"projection": ccrs.PlateCarree()}
+        subplot_kw={"projection": ccrs.EqualEarth()}
     )
 
     # PDF has minimum width, so set these to zero
     line_lower_threshold = 200.
     line_upper_threshold = 1e4
-    linewidth_factor = 2e3
+    linewidth_factor = 3e3
     ac_color = "gray"
     dc_color = "m"
 
@@ -709,7 +838,7 @@ if __name__ == "__main__":
 
     plot_map(n,
         components=["generators", "links", "stores", "storage_units"],
-        bus_size_factor=1.5e10,
+        bus_size_factor=2e10,
         transmission=False
     )
 

From 3323f348965794fc4e54a4c17abf10327897454b Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 4 Feb 2022 12:26:58 +0100
Subject: [PATCH 003/243] bugfix: also plot h2 corridors where no retrofit

---
 scripts/plot_network.py | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 62ac5ad6..3a4859ce 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -13,7 +13,7 @@ from make_summary import assign_carriers
 from plot_summary import rename_techs, preferred_order
 from helper import override_component_attrs
 
-plt.style.use(['ggplot', "../matplotlibrc"])
+plt.style.use(['ggplot', "matplotlibrc"])
 
 
 def rename_techs_tyndp(tech):
@@ -313,7 +313,7 @@ def plot_h2_map(network):
 
     n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
 
-    h2_new = n.links.loc[n.links.carrier=="H2 pipeline", "p_nom_opt"]
+    h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
 
     h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
 
@@ -325,21 +325,28 @@ def plot_h2_map(network):
         h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
         h2_retro = pd.concat([h2_retro_p, h2_retro_n])
 
+        h2_retro["index_orig"] = h2_retro.index
         h2_retro.index = h2_retro.apply(
             lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
             axis=1
         )
 
-        h2_retro = h2_retro["p_nom_opt"]
+        retro_w_new_i = h2_retro.index.intersection(h2_new.index)
+        h2_retro_w_new = h2_retro.loc[retro_w_new_i]
 
-        h2_total = h2_new + h2_retro
+        retro_wo_new_i = h2_retro.index.difference(h2_new.index)
+        h2_retro_wo_new = h2_retro.loc[retro_wo_new_i]
+        h2_retro_wo_new.index = h2_retro_wo_new.index_orig
+
+        to_concat = [h2_new, h2_retro_w_new, h2_retro_wo_new]
+        h2_total = pd.concat(to_concat).p_nom_opt.groupby(level=0).sum()
 
     else:
 
         h2_total = h2_new
 
     link_widths_total = h2_total / linewidth_factor
-    link_widths_total = link_widths_total.groupby(level=0).sum().reindex(n.links.index).fillna(0.)
+    link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.)
     link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
 
     retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)

From 4b1360c3f0946238a41de75a66582bc1c71a48e2 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 20 Feb 2022 15:59:51 +0100
Subject: [PATCH 004/243] do not aggregate rooftop and solar PV in plots

---
 scripts/plot_network.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 3a4859ce..e2409a99 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -26,8 +26,8 @@ def rename_techs_tyndp(tech):
         return "H2 storage"
     elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
         return "gas-to-power/heat"
-    elif "solar" in tech:
-        return "solar"
+    # elif "solar" in tech:
+    #     return "solar"
     elif tech == "Fischer-Tropsch":
         return "power-to-liquid"
     elif "offshore wind" in tech:

From c754f253bc21afb264aa9697d19ec42d4203944d Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 20 Feb 2022 16:13:11 +0100
Subject: [PATCH 005/243] plotting adjustments for pypsa 0.19.1

---
 config.default.yaml     |  2 +-
 scripts/plot_network.py | 27 ++++++++++++++++-----------
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 5340682b..cb0b70ff 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -387,7 +387,7 @@ plotting:
     boundaries: [-11, 30, 34, 71]
     color_geomap:
       ocean: white
-      land: whitesmoke
+      land: white
   costs_max: 1000
   costs_threshold: 1
   energy_max: 20000
diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index e2409a99..0f4abcee 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -263,7 +263,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     )
     
     legend_kw = dict(
-        bbox_to_anchor=(1.55, 1.04),
+        bbox_to_anchor=(1.52, 1.04),
         frameon=False,
     )
 
@@ -361,13 +361,18 @@ def plot_h2_map(network):
         subplot_kw={"projection": ccrs.EqualEarth()}
     )
 
-    color_h2_pipe = '#a2f0f2'
-    color_retrofit = '#72d3d6'
+    color_h2_pipe = '#b3f3f4'
+    color_retrofit = '#54cacd'
     
+    bus_colors = {
+        "H2 Electrolysis": "#ff29d9",
+        "H2 Fuel Cell": "#6b3161",
+    }
+
     n.plot(
         geomap=True,
         bus_sizes=bus_sizes,
-        bus_colors=tech_colors,
+        bus_colors=bus_colors,
         link_colors=color_h2_pipe,
         link_widths=link_widths_total,
         branch_components=["Link"],
@@ -376,13 +381,13 @@ def plot_h2_map(network):
     )
 
     n.plot(
-        geomap=True, # set False in PyPSA 0.19
+        geomap=True,
         bus_sizes=0,
         link_colors=color_retrofit,
         link_widths=link_widths_retro,
         branch_components=["Link"],
         ax=ax,
-        # color_geomap=False, # needs PyPSA 0.19
+        color_geomap=False,
         boundaries=map_opts["boundaries"]
     )
 
@@ -424,7 +429,7 @@ def plot_h2_map(network):
         legend_kw=legend_kw,
     )
 
-    colors = [tech_colors[c] for c in carriers] + [color_h2_pipe, color_retrofit]
+    colors = [bus_colors[c] for c in carriers] + [color_h2_pipe, color_retrofit]
     labels = carriers + ["H2 pipeline (total)", "H2 pipeline (repurposed)"]
 
     legend_kw = dict(
@@ -533,24 +538,24 @@ def plot_ch4_map(network):
     )
 
     n.plot(
-        geomap=True, # set False in PyPSA 0.19
+        geomap=True,
         ax=ax,
         bus_sizes=0.,
         link_colors=pipe_colors['gas pipeline (available)'],
         link_widths=link_widths_rem,
         branch_components=["Link"],
-        # color_geomap=False, # needs PyPSA 0.19
+        color_geomap=False,
         boundaries=map_opts["boundaries"]
     )
 
     n.plot(
-        geomap=True, # set False in PyPSA 0.19
+        geomap=True,
         ax=ax,
         bus_sizes=0.,
         link_colors=link_color_used,
         link_widths=link_widths_used,
         branch_components=["Link"],
-        # color_geomap=False, # needs PyPSA 0.19
+        color_geomap=False,
         boundaries=map_opts["boundaries"]
     )
 

From 9573f33860d9e0981ad73b1a07bfaf8d45181bb5 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 14:44:36 +0200
Subject: [PATCH 006/243] add ammonia as carrier: with Haber-Bosch, crackers,
 store, load

---
 config.default.yaml                                   |  2 ++
 ...uild_industrial_energy_demand_per_country_today.py | 11 ++++++++---
 scripts/build_industrial_energy_demand_per_node.py    |  1 +
 scripts/build_industry_sector_ratios.py               |  8 ++++++--
 4 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index d48879c6..ae8f90c8 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -244,6 +244,7 @@ sector:
     # - onshore  # more than 50 km from sea
     - nearshore  # within 50 km of sea
     # - offshore
+  ammonia: false
   use_fischer_tropsch_waste_heat: true
   use_fuel_cell_waste_heat: true
   electricity_distribution_grid: true
@@ -291,6 +292,7 @@ industry:
     # 2040: 0.3
     # 2045: 0.25
     # 2050: 0.2
+  MWh_NH3_per_tNH3: 5.166 # LHV
   MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
   MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
   MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
diff --git a/scripts/build_industrial_energy_demand_per_country_today.py b/scripts/build_industrial_energy_demand_per_country_today.py
index 0adf84e7..a3fbf466 100644
--- a/scripts/build_industrial_energy_demand_per_country_today.py
+++ b/scripts/build_industrial_energy_demand_per_country_today.py
@@ -65,6 +65,8 @@ def industrial_energy_demand_per_country(country):
 
         df = df_dict[sheet][year].groupby(fuels).sum()
 
+        df["ammonia"] = 0.
+
         df['other'] = df['all'] - df.loc[df.index != 'all'].sum()
 
         return df
@@ -89,18 +91,21 @@ def add_ammonia_energy_demand(demand):
     fn = snakemake.input.ammonia_production
     ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3
 
-    def ammonia_by_fuel(x):
+    def get_ammonia_by_fuel(x):
 
         fuels = {'gas': config['MWh_CH4_per_tNH3_SMR'],
                  'electricity': config['MWh_elec_per_tNH3_SMR']}
 
         return pd.Series({k: x*v for k,v in fuels.items()})
 
-    ammonia = ammonia.apply(ammonia_by_fuel).T
+    ammonia_by_fuel = ammonia.apply(get_ammonia_by_fuel).T
+    ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(index=demand.index, fill_value=0.)
+
+    ammonia = pd.DataFrame({"ammonia": ammonia * config['MWh_NH3_per_tNH3']}).T
 
     demand['Ammonia'] = ammonia.unstack().reindex(index=demand.index, fill_value=0.)
 
-    demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"]
+    demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - ammonia_by_fuel
 
     demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
 
diff --git a/scripts/build_industrial_energy_demand_per_node.py b/scripts/build_industrial_energy_demand_per_node.py
index cb085ad1..d665f18e 100644
--- a/scripts/build_industrial_energy_demand_per_node.py
+++ b/scripts/build_industrial_energy_demand_per_node.py
@@ -9,6 +9,7 @@ if __name__ == '__main__':
             'build_industrial_energy_demand_per_node',
             simpl='',
             clusters=48,
+            planning_horizons=2030,
         )
         
     # import EU ratios df as csv
diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py
index c8cac055..d1dbe9d8 100644
--- a/scripts/build_industry_sector_ratios.py
+++ b/scripts/build_industry_sector_ratios.py
@@ -60,6 +60,7 @@ index = [
     "hydrogen",
     "heat",
     "naphtha",
+    "ammonia",
     "process emission",
     "process emission from feedstock",
 ]
@@ -432,8 +433,11 @@ def chemicals_industry():
 
     sector = "Ammonia"
     df[sector] = 0.0
-    df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
-    df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
+    if snakemake.config["sector"].get("ammonia", False):
+        df.loc["ammonia", sector] = config["MWh_NH3_per_tNH3"]
+    else:
+        df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
+        df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
 
     # Chlorine
 

From 6cfee1f98a647509f2e2cab0d36348e8cbcbe08d Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 14:44:36 +0200
Subject: [PATCH 007/243] add ammonia as carrier: with Haber-Bosch, crackers,
 store, load

---
 config.default.yaml                           |  2 +
 ...ustrial_energy_demand_per_country_today.py | 11 +++-
 ...build_industrial_energy_demand_per_node.py |  1 +
 scripts/build_industry_sector_ratios.py       |  8 ++-
 scripts/prepare_sector_network.py             | 65 +++++++++++++++++++
 5 files changed, 82 insertions(+), 5 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index d48879c6..ae8f90c8 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -244,6 +244,7 @@ sector:
     # - onshore  # more than 50 km from sea
     - nearshore  # within 50 km of sea
     # - offshore
+  ammonia: false
   use_fischer_tropsch_waste_heat: true
   use_fuel_cell_waste_heat: true
   electricity_distribution_grid: true
@@ -291,6 +292,7 @@ industry:
     # 2040: 0.3
     # 2045: 0.25
     # 2050: 0.2
+  MWh_NH3_per_tNH3: 5.166 # LHV
   MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
   MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
   MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
diff --git a/scripts/build_industrial_energy_demand_per_country_today.py b/scripts/build_industrial_energy_demand_per_country_today.py
index 0adf84e7..a3fbf466 100644
--- a/scripts/build_industrial_energy_demand_per_country_today.py
+++ b/scripts/build_industrial_energy_demand_per_country_today.py
@@ -65,6 +65,8 @@ def industrial_energy_demand_per_country(country):
 
         df = df_dict[sheet][year].groupby(fuels).sum()
 
+        df["ammonia"] = 0.
+
         df['other'] = df['all'] - df.loc[df.index != 'all'].sum()
 
         return df
@@ -89,18 +91,21 @@ def add_ammonia_energy_demand(demand):
     fn = snakemake.input.ammonia_production
     ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3
 
-    def ammonia_by_fuel(x):
+    def get_ammonia_by_fuel(x):
 
         fuels = {'gas': config['MWh_CH4_per_tNH3_SMR'],
                  'electricity': config['MWh_elec_per_tNH3_SMR']}
 
         return pd.Series({k: x*v for k,v in fuels.items()})
 
-    ammonia = ammonia.apply(ammonia_by_fuel).T
+    ammonia_by_fuel = ammonia.apply(get_ammonia_by_fuel).T
+    ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(index=demand.index, fill_value=0.)
+
+    ammonia = pd.DataFrame({"ammonia": ammonia * config['MWh_NH3_per_tNH3']}).T
 
     demand['Ammonia'] = ammonia.unstack().reindex(index=demand.index, fill_value=0.)
 
-    demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"]
+    demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - ammonia_by_fuel
 
     demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)
 
diff --git a/scripts/build_industrial_energy_demand_per_node.py b/scripts/build_industrial_energy_demand_per_node.py
index cb085ad1..d665f18e 100644
--- a/scripts/build_industrial_energy_demand_per_node.py
+++ b/scripts/build_industrial_energy_demand_per_node.py
@@ -9,6 +9,7 @@ if __name__ == '__main__':
             'build_industrial_energy_demand_per_node',
             simpl='',
             clusters=48,
+            planning_horizons=2030,
         )
         
     # import EU ratios df as csv
diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py
index c8cac055..d1dbe9d8 100644
--- a/scripts/build_industry_sector_ratios.py
+++ b/scripts/build_industry_sector_ratios.py
@@ -60,6 +60,7 @@ index = [
     "hydrogen",
     "heat",
     "naphtha",
+    "ammonia",
     "process emission",
     "process emission from feedstock",
 ]
@@ -432,8 +433,11 @@ def chemicals_industry():
 
     sector = "Ammonia"
     df[sector] = 0.0
-    df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
-    df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
+    if snakemake.config["sector"].get("ammonia", False):
+        df.loc["ammonia", sector] = config["MWh_NH3_per_tNH3"]
+    else:
+        df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
+        df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
 
     # Chlorine
 
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index f0f60934..a524e882 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -654,6 +654,59 @@ def add_generation(n, costs):
         )
 
 
+def add_ammonia(n, costs):
+
+    logger.info("adding ammonia carrier")
+
+    nodes = pop_layout.index
+
+    n.add("Carrier", "NH3")
+
+    n.madd("Bus",
+        nodes + " NH3",
+        location=nodes,
+        carrier="NH3"
+    )
+
+    n.madd("Link",
+        nodes,
+        suffix=" Haber-Bosch",
+        bus0=nodes,
+        bus1=nodes + " NH3",
+        bus2=nodes + " H2",
+        p_nom_extendable=True,
+        carrier="Haber-Bosch",
+        efficiency=+0.221, #MWh_e/MWh_NH3 0.247 https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
+        efficiency2=-1.226, #MWh_H2/MWh_NH3 1.148 https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
+        capital_cost=costs.at["Haber-Bosch synthesis", "fixed"],
+        lifetime=costs.at["Haber-Bosch synthesis", 'lifetime']
+    )
+
+    n.madd("Link",
+        nodes,
+        suffix=" ammonia cracker",
+        bus0=nodes + " NH3",
+        bus1=nodes + " H2",
+        p_nom_extendable=True,
+        carrier ="ammonia cracker",
+        efficiency=0.685, #MWh_H2/MWh_NH3 https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
+        capital_cost=costs.at["Ammonia cracker", "fixed"] * 0.685, # given per MWh_H2
+        lifetime=costs.at['Ammonia cracker', 'lifetime']
+    )
+
+    # Ammonia Storage
+    n.madd("Store",
+        nodes,
+        suffix=" ammonia store",
+        bus=nodes + " NH3",
+        e_nom_extendable=True,
+        e_cyclic=True,
+        carrier="ammonia store",
+        capital_cost=costs.at["NH3 (l) storage tank incl. liquefaction", "fixed"],
+        lifetime=costs.at['NH3 (l) storage tank incl. liquefaction', 'lifetime']
+    )
+
+
 def add_wave(n, wave_cost_factor):
 
     # TODO: handle in Snakefile
@@ -2148,6 +2201,15 @@ def add_industry(n, costs):
         lifetime=costs.at['cement capture', 'lifetime']
     )
 
+    if options["ammonia"]:
+        n.madd("Load",
+            nodes,
+            suffix=" NH3",
+            bus=nodes + " NH3",
+            carrier="NH3",
+            p_set=industrial_demand.loc[nodes, "ammonia"] / 8760
+        )
+
 
 def add_waste_heat(n):
     # TODO options?
@@ -2377,6 +2439,9 @@ if __name__ == "__main__":
     if options['dac']:
         add_dac(n, costs)
 
+    if options['ammonia']:
+        add_ammonia(n, costs)
+
     if "decentral" in opts:
         decentral(n)
 

From 2d562c13495a21cbba504ceb09f9614b2365d58a Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 14:57:03 +0200
Subject: [PATCH 008/243] add coloring for ammonia

---
 config.default.yaml     | 5 +++++
 scripts/plot_network.py | 2 ++
 scripts/plot_summary.py | 2 ++
 3 files changed, 9 insertions(+)

diff --git a/config.default.yaml b/config.default.yaml
index ae8f90c8..b510780e 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -573,6 +573,11 @@ plotting:
     H2 pipeline retrofitted: '#ba99b5'
     H2 Fuel Cell: '#c251ae'
     H2 Electrolysis: '#ff29d9'
+    # ammonia
+    NH3: '#46caf0'
+    ammonia store: '#00ace0'
+    ammonia cracker: '#87d0e6'
+    Haber-Bosch: '#076987'
     # syngas
     Sabatier: '#9850ad'
     methanation: '#c44ce6'
diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 4a1bc6d0..61f91df1 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -23,6 +23,8 @@ def rename_techs_tyndp(tech):
         return "power-to-gas"
     elif tech == "H2":
         return "H2 storage"
+    elif tech in ["NH3", "Haber-Bosch", "ammonia cracker", "ammonia store"]:
+        return "ammonia"
     elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
         return "gas-to-power/heat"
     elif "solar" in tech:
diff --git a/scripts/plot_summary.py b/scripts/plot_summary.py
index 8b073b17..97166f54 100644
--- a/scripts/plot_summary.py
+++ b/scripts/plot_summary.py
@@ -51,6 +51,7 @@ def rename_techs(label):
         "ror": "hydroelectricity",
         "hydro": "hydroelectricity",
         "PHS": "hydroelectricity",
+        "NH3": "ammonia"
         "co2 Store": "DAC",
         "co2 stored": "CO2 sequestration",
         "AC": "transmission lines",
@@ -106,6 +107,7 @@ preferred_order = pd.Index([
     "natural gas",
     "helmeth",
     "methanation",
+    "ammonia",
     "hydrogen storage",
     "power-to-gas",
     "power-to-liquid",

From a2a4cf7c023eccdbb5d5de2043fac2036a16347f Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 16:43:29 +0200
Subject: [PATCH 009/243] use config to manage conversion efficiencies

---
 config.default.yaml               |  1 +
 scripts/prepare_sector_network.py | 12 +++++++-----
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index b510780e..d2291f58 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -297,6 +297,7 @@ industry:
   MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
   MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
   MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
+  MWh_NH3_per_MWh_H2_cracker: 1.46 # https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
   NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
   petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
   HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index a524e882..dd4e6e39 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -660,6 +660,8 @@ def add_ammonia(n, costs):
 
     nodes = pop_layout.index
 
+    cf_industry = snakemake.config["industry"]
+
     n.add("Carrier", "NH3")
 
     n.madd("Bus",
@@ -676,8 +678,8 @@ def add_ammonia(n, costs):
         bus2=nodes + " H2",
         p_nom_extendable=True,
         carrier="Haber-Bosch",
-        efficiency=+0.221, #MWh_e/MWh_NH3 0.247 https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
-        efficiency2=-1.226, #MWh_H2/MWh_NH3 1.148 https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
+        efficiency=1 / (cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"]) # output: MW_NH3 per MW_elec
+        efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"] # input: MW_H2 per MW_elec
         capital_cost=costs.at["Haber-Bosch synthesis", "fixed"],
         lifetime=costs.at["Haber-Bosch synthesis", 'lifetime']
     )
@@ -688,9 +690,9 @@ def add_ammonia(n, costs):
         bus0=nodes + " NH3",
         bus1=nodes + " H2",
         p_nom_extendable=True,
-        carrier ="ammonia cracker",
-        efficiency=0.685, #MWh_H2/MWh_NH3 https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
-        capital_cost=costs.at["Ammonia cracker", "fixed"] * 0.685, # given per MWh_H2
+        carrier="ammonia cracker",
+        efficiency=1 / cf_industry["MWh_NH3_per_MWh_H2_cracker"]
+        capital_cost=costs.at["Ammonia cracker", "fixed"] / cf_industry["MWh_NH3_per_MWh_H2_cracker"], # given per MW_H2
         lifetime=costs.at['Ammonia cracker', 'lifetime']
     )
 

From 4984ba199e2e2a41bf8183cbd7832b5262521b53 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 16:53:37 +0200
Subject: [PATCH 010/243] use spatial namespace to manage ammonia resolution

---
 config.default.yaml               |  2 +-
 scripts/prepare_sector_network.py | 26 +++++++++++++++++++-------
 2 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index d2291f58..d0887bc8 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -244,7 +244,7 @@ sector:
     # - onshore  # more than 50 km from sea
     - nearshore  # within 50 km of sea
     # - offshore
-  ammonia: false
+  ammonia: false # can be false (no NH3 carrier), true (copperplated NH3), "regional" (regionalised NH3 without network)
   use_fischer_tropsch_waste_heat: true
   use_fuel_cell_waste_heat: true
   electricity_distribution_grid: true
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index dd4e6e39..9411a282 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -93,6 +93,18 @@ def define_spatial(nodes, options):
 
     spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
 
+    # ammonia
+
+    spatial.ammonia = SimpleNamespace()
+    if options["ammonia"] == "regional":
+        spatial.ammonia.nodes = nodes + " NH3"
+        spatial.ammonia.locations = nodes
+    else:
+        spatial.ammonia.nodes = ["EU ammonia"]
+        spatial.ammonia.locations = ["EU"]
+
+    spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
+
     # oil
     spatial.oil = SimpleNamespace()
     spatial.oil.nodes = ["EU oil"]
@@ -656,7 +668,7 @@ def add_generation(n, costs):
 
 def add_ammonia(n, costs):
 
-    logger.info("adding ammonia carrier")
+    logger.info("adding ammonia carrier with synthesis, cracking and storage")
 
     nodes = pop_layout.index
 
@@ -665,8 +677,8 @@ def add_ammonia(n, costs):
     n.add("Carrier", "NH3")
 
     n.madd("Bus",
-        nodes + " NH3",
-        location=nodes,
+        spatial.ammonia.nodes,
+        location=spatial.ammonia.locations,
         carrier="NH3"
     )
 
@@ -674,7 +686,7 @@ def add_ammonia(n, costs):
         nodes,
         suffix=" Haber-Bosch",
         bus0=nodes,
-        bus1=nodes + " NH3",
+        bus1=spatial.ammonia.nodes,
         bus2=nodes + " H2",
         p_nom_extendable=True,
         carrier="Haber-Bosch",
@@ -687,7 +699,7 @@ def add_ammonia(n, costs):
     n.madd("Link",
         nodes,
         suffix=" ammonia cracker",
-        bus0=nodes + " NH3",
+        bus0=spatial.ammonia.nodes,
         bus1=nodes + " H2",
         p_nom_extendable=True,
         carrier="ammonia cracker",
@@ -698,9 +710,9 @@ def add_ammonia(n, costs):
 
     # Ammonia Storage
     n.madd("Store",
-        nodes,
+        spatial.ammonia.nodes,
         suffix=" ammonia store",
-        bus=nodes + " NH3",
+        bus=spatial.ammonia.nodes,
         e_nom_extendable=True,
         e_cyclic=True,
         carrier="ammonia store",

From 27ac40d2eaa1308830b862391c69a6c2e8961922 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 16:53:37 +0200
Subject: [PATCH 011/243] use spatial namespace to manage ammonia resolution

---
 config.default.yaml               |  2 +-
 scripts/prepare_sector_network.py | 31 +++++++++++++++++++++----------
 2 files changed, 22 insertions(+), 11 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index d2291f58..d0887bc8 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -244,7 +244,7 @@ sector:
     # - onshore  # more than 50 km from sea
     - nearshore  # within 50 km of sea
     # - offshore
-  ammonia: false
+  ammonia: false # can be false (no NH3 carrier), true (copperplated NH3), "regional" (regionalised NH3 without network)
   use_fischer_tropsch_waste_heat: true
   use_fuel_cell_waste_heat: true
   electricity_distribution_grid: true
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index dd4e6e39..f0e94c19 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -93,6 +93,18 @@ def define_spatial(nodes, options):
 
     spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
 
+    # ammonia
+
+    spatial.ammonia = SimpleNamespace()
+    if options["ammonia"] == "regional":
+        spatial.ammonia.nodes = nodes + " NH3"
+        spatial.ammonia.locations = nodes
+    else:
+        spatial.ammonia.nodes = ["EU ammonia"]
+        spatial.ammonia.locations = ["EU"]
+
+    spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
+
     # oil
     spatial.oil = SimpleNamespace()
     spatial.oil.nodes = ["EU oil"]
@@ -656,7 +668,7 @@ def add_generation(n, costs):
 
 def add_ammonia(n, costs):
 
-    logger.info("adding ammonia carrier")
+    logger.info("adding ammonia carrier with synthesis, cracking and storage")
 
     nodes = pop_layout.index
 
@@ -665,8 +677,8 @@ def add_ammonia(n, costs):
     n.add("Carrier", "NH3")
 
     n.madd("Bus",
-        nodes + " NH3",
-        location=nodes,
+        spatial.ammonia.nodes,
+        location=spatial.ammonia.locations,
         carrier="NH3"
     )
 
@@ -674,7 +686,7 @@ def add_ammonia(n, costs):
         nodes,
         suffix=" Haber-Bosch",
         bus0=nodes,
-        bus1=nodes + " NH3",
+        bus1=spatial.ammonia.nodes,
         bus2=nodes + " H2",
         p_nom_extendable=True,
         carrier="Haber-Bosch",
@@ -687,7 +699,7 @@ def add_ammonia(n, costs):
     n.madd("Link",
         nodes,
         suffix=" ammonia cracker",
-        bus0=nodes + " NH3",
+        bus0=spatial.ammonia.nodes,
         bus1=nodes + " H2",
         p_nom_extendable=True,
         carrier="ammonia cracker",
@@ -698,9 +710,9 @@ def add_ammonia(n, costs):
 
     # Ammonia Storage
     n.madd("Store",
-        nodes,
+        spatial.ammonia.nodes,
         suffix=" ammonia store",
-        bus=nodes + " NH3",
+        bus=spatial.ammonia.nodes,
         e_nom_extendable=True,
         e_cyclic=True,
         carrier="ammonia store",
@@ -2205,9 +2217,8 @@ def add_industry(n, costs):
 
     if options["ammonia"]:
         n.madd("Load",
-            nodes,
-            suffix=" NH3",
-            bus=nodes + " NH3",
+            spatial.ammonia.nodes,
+            bus=spatial.ammonia.nodes,
             carrier="NH3",
             p_set=industrial_demand.loc[nodes, "ammonia"] / 8760
         )

From 4ecfccea6cce75e39507c3f95dae0102dd4706aa Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 10 Jun 2022 17:07:48 +0200
Subject: [PATCH 012/243] handle ammonia demand both regionalised and
 copperplated

---
 scripts/prepare_sector_network.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index f0e94c19..64c1868f 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2216,11 +2216,17 @@ def add_industry(n, costs):
     )
 
     if options["ammonia"]:
+
+        if options["ammonia"] == 'regional':
+            p_set = industrial_demand.loc[spatial.ammonia.locations, "ammonia"].rename(index=lambda x: x + " NH3") / 8760
+        else:
+            p_set = industrial_demand["ammonia"].sum() / 8760
+
         n.madd("Load",
             spatial.ammonia.nodes,
             bus=spatial.ammonia.nodes,
             carrier="NH3",
-            p_set=industrial_demand.loc[nodes, "ammonia"] / 8760
+            p_set=p_set
         )
 
 

From 37c052667a78c1cd473575c9f2eacd70252997c5 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 22 Jun 2022 15:50:32 +0200
Subject: [PATCH 013/243] handle absent ammonia config flag

---
 scripts/prepare_sector_network.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 64c1868f..64207f0c 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -96,7 +96,7 @@ def define_spatial(nodes, options):
     # ammonia
 
     spatial.ammonia = SimpleNamespace()
-    if options["ammonia"] == "regional":
+    if options.get("ammonia") == "regional":
         spatial.ammonia.nodes = nodes + " NH3"
         spatial.ammonia.locations = nodes
     else:
@@ -2215,7 +2215,7 @@ def add_industry(n, costs):
         lifetime=costs.at['cement capture', 'lifetime']
     )
 
-    if options["ammonia"]:
+    if options.get("ammonia"):
 
         if options["ammonia"] == 'regional':
             p_set = industrial_demand.loc[spatial.ammonia.locations, "ammonia"].rename(index=lambda x: x + " NH3") / 8760

From 9f91af28e727e77285c9761876b6e5657dd71ed4 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 23 Jun 2022 15:17:41 +0200
Subject: [PATCH 014/243] fix syntax errors

---
 scripts/plot_network.py           | 6 +++---
 scripts/plot_summary.py           | 4 ++--
 scripts/prepare_sector_network.py | 8 ++++----
 3 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 61f91df1..3f7c6960 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -309,7 +309,7 @@ def plot_h2_map(network):
     )
 
     n.plot(
-        geomap=False,
+        # geomap=False,
         bus_sizes=0,
         link_colors='#72d3d6',
         link_widths=link_widths_retro,
@@ -443,7 +443,7 @@ def plot_ch4_map(network):
     )
 
     n.plot(
-        geomap=False,
+        # geomap=False,
         ax=ax,
         bus_sizes=0.,
         link_colors='#e8d1d1',
@@ -453,7 +453,7 @@ def plot_ch4_map(network):
     )
 
     n.plot(
-        geomap=False,
+        # geomap=False,
         ax=ax,
         bus_sizes=0.,
         link_colors=link_color_used,
diff --git a/scripts/plot_summary.py b/scripts/plot_summary.py
index 97166f54..f58c81af 100644
--- a/scripts/plot_summary.py
+++ b/scripts/plot_summary.py
@@ -51,7 +51,7 @@ def rename_techs(label):
         "ror": "hydroelectricity",
         "hydro": "hydroelectricity",
         "PHS": "hydroelectricity",
-        "NH3": "ammonia"
+        "NH3": "ammonia",
         "co2 Store": "DAC",
         "co2 stored": "CO2 sequestration",
         "AC": "transmission lines",
@@ -256,7 +256,7 @@ def plot_balances():
         df = df / 1e6
 
         #remove trailing link ports
-        df.index = [i[:-1] if ((i != "co2") and (i[-1:] in ["0","1","2","3"])) else i for i in df.index]
+        df.index = [i[:-1] if ((i not in ["co2", "NH3"]) and (i[-1:] in ["0","1","2","3"])) else i for i in df.index]
 
         df = df.groupby(df.index.map(rename_techs)).sum()
 
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 64207f0c..f56b3606 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -100,7 +100,7 @@ def define_spatial(nodes, options):
         spatial.ammonia.nodes = nodes + " NH3"
         spatial.ammonia.locations = nodes
     else:
-        spatial.ammonia.nodes = ["EU ammonia"]
+        spatial.ammonia.nodes = ["EU NH3"]
         spatial.ammonia.locations = ["EU"]
 
     spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
@@ -690,8 +690,8 @@ def add_ammonia(n, costs):
         bus2=nodes + " H2",
         p_nom_extendable=True,
         carrier="Haber-Bosch",
-        efficiency=1 / (cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"]) # output: MW_NH3 per MW_elec
-        efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"] # input: MW_H2 per MW_elec
+        efficiency=1 / (cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"]), # output: MW_NH3 per MW_elec
+        efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"], # input: MW_H2 per MW_elec
         capital_cost=costs.at["Haber-Bosch synthesis", "fixed"],
         lifetime=costs.at["Haber-Bosch synthesis", 'lifetime']
     )
@@ -703,7 +703,7 @@ def add_ammonia(n, costs):
         bus1=nodes + " H2",
         p_nom_extendable=True,
         carrier="ammonia cracker",
-        efficiency=1 / cf_industry["MWh_NH3_per_MWh_H2_cracker"]
+        efficiency=1 / cf_industry["MWh_NH3_per_MWh_H2_cracker"],
         capital_cost=costs.at["Ammonia cracker", "fixed"] / cf_industry["MWh_NH3_per_MWh_H2_cracker"], # given per MW_H2
         lifetime=costs.at['Ammonia cracker', 'lifetime']
     )

From d69efe3d1fc817f0709e4b8d892cde8f3381acb4 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 23 Jun 2022 15:22:21 +0200
Subject: [PATCH 015/243] add ammonia color to config.default.yaml

---
 config.default.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/config.default.yaml b/config.default.yaml
index d0887bc8..3621b072 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -576,6 +576,7 @@ plotting:
     H2 Electrolysis: '#ff29d9'
     # ammonia
     NH3: '#46caf0'
+    ammonia: '#46caf0'
     ammonia store: '#00ace0'
     ammonia cracker: '#87d0e6'
     Haber-Bosch: '#076987'

From 203753455770d2b82b56c7d621a4a7e189b34128 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 23 Jun 2022 15:24:44 +0200
Subject: [PATCH 016/243] prepare: only add ammonia to spatial if config
 selected

---
 scripts/prepare_sector_network.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index f56b3606..d6269413 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -95,15 +95,16 @@ def define_spatial(nodes, options):
 
     # ammonia
 
-    spatial.ammonia = SimpleNamespace()
-    if options.get("ammonia") == "regional":
-        spatial.ammonia.nodes = nodes + " NH3"
-        spatial.ammonia.locations = nodes
-    else:
-        spatial.ammonia.nodes = ["EU NH3"]
-        spatial.ammonia.locations = ["EU"]
+    if options.get('ammonia'):
+        spatial.ammonia = SimpleNamespace()
+        if options.get("ammonia") == "regional":
+            spatial.ammonia.nodes = nodes + " NH3"
+            spatial.ammonia.locations = nodes
+        else:
+            spatial.ammonia.nodes = ["EU NH3"]
+            spatial.ammonia.locations = ["EU"]
 
-    spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
+        spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
 
     # oil
     spatial.oil = SimpleNamespace()

From e1a6e13f750e84e7b0630d7303c675ab423a024e Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 23 Jun 2022 15:28:00 +0200
Subject: [PATCH 017/243] add release note [no ci]

---
 doc/release_notes.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 7808d2ba..552ffccb 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -62,6 +62,11 @@ incorporates retrofitting options to hydrogen.
 
 * Add option to sweep the global CO2 sequestration potentials with keyword ``seq200`` in the ``{sector_opts}`` wildcard (for limit of 200 Mt CO2).
 
+* Add option to resolve ammonia as separate energy carrier with Haber-Bosch
+  synthesis, ammonia cracking, storage and industrial demand. The ammonia
+  carrier can be nodally resolved or copperplated across Europe. This feature is
+  controlled by ``sector: ammonia:``.
+
 * Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials.
 
 **Bugfixes**

From a8171ec9f3e6b9c6a04edb6635420098f4c260e0 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Tue, 28 Jun 2022 18:03:29 +0200
Subject: [PATCH 018/243] color adaptations, new features

---
 Snakefile               |  3 +-
 scripts/helper.py       |  2 --
 scripts/plot_network.py | 77 +++++++++++++++++++++++++++++------------
 3 files changed, 56 insertions(+), 26 deletions(-)

diff --git a/Snakefile b/Snakefile
index d428db45..b5756646 100644
--- a/Snakefile
+++ b/Snakefile
@@ -478,7 +478,8 @@ rule prepare_sector_network:
 rule plot_network:
     input:
         overrides="data/override_component_attrs",
-        network=RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
+        network=RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
+        regions='../pypsa-eur/resources/regions_onshore_elec_s{simpl}_{clusters}.geojson'
     output:
         map=RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
         today=RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}-today.pdf"
diff --git a/scripts/helper.py b/scripts/helper.py
index b176ccee..ca35c019 100644
--- a/scripts/helper.py
+++ b/scripts/helper.py
@@ -42,9 +42,7 @@ def mock_snakemake(rulename, **wildcards):
     This function is expected to be executed from the 'scripts'-directory of '
     the snakemake project. It returns a snakemake.script.Snakemake object,
     based on the Snakefile.
-
     If a rule has wildcards, you have to specify them in **wildcards.
-
     Parameters
     ----------
     rulename: str
diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 0f4abcee..fc252229 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -2,6 +2,7 @@ import pypsa
 
 import numpy as np
 import pandas as pd
+import geopandas as gpd
 import matplotlib.pyplot as plt
 import cartopy.crs as ccrs
 
@@ -63,7 +64,6 @@ def add_legend_circles(ax, sizes, labels, scale=1, srid=None, patch_kw={}, legen
     
     if srid is not None:
         area_correction = projected_area_factor(ax, n.srid)**2 
-        print(area_correction)
         sizes = [s * area_correction for s in sizes]
     
     handles = make_legend_circles_for(sizes, scale, **patch_kw)
@@ -113,7 +113,9 @@ def assign_location(n):
 
 
 def plot_map(network, components=["links", "stores", "storage_units", "generators"],
-             bus_size_factor=1.7e10, transmission=False):
+             bus_size_factor=1.7e10, transmission=False, with_legend=True):
+
+    tech_colors = snakemake.config['plotting']['tech_colors']
 
     tech_colors = snakemake.config['plotting']['tech_colors']
 
@@ -174,9 +176,9 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     # PDF has minimum width, so set these to zero
     line_lower_threshold = 500.
     line_upper_threshold = 1e4
-    linewidth_factor = 2e3
-    ac_color = "gray"
-    dc_color = "m"
+    linewidth_factor = 4e3
+    ac_color = "rosybrown"
+    dc_color = "darkseagreen"
 
     if snakemake.wildcards["lv"] == "1.0":
         # should be zero
@@ -221,6 +223,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
 
     sizes = [20, 10, 5]
     labels = [f"{s} bEUR/a" for s in sizes]
+    sizes = [s/bus_size_factor*1e9 for s in sizes]
 
     legend_kw = dict(
         loc="upper left",
@@ -235,7 +238,6 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         ax,
         sizes,
         labels,
-        scale=bus_size_factor/1e9,
         srid=n.srid,
         patch_kw=dict(facecolor="lightgrey"),
         legend_kw=legend_kw
@@ -267,15 +269,17 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         frameon=False,
     )
 
-    colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
-    labels = carriers + ["HVAC line", "HVDC link"]
+    if with_legend:
 
-    add_legend_patches(
-        ax,
-        colors,
-        labels,
-        legend_kw=legend_kw,
-    )
+        colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
+        labels = carriers + ["HVAC line", "HVDC link"]
+
+        add_legend_patches(
+            ax,
+            colors,
+            labels,
+            legend_kw=legend_kw,
+        )
 
     fig.savefig(
         snakemake.output.map,
@@ -284,7 +288,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     )
 
 
-def plot_h2_map(network):
+def plot_h2_map(network, regions):
 
     tech_colors = snakemake.config['plotting']['tech_colors']
 
@@ -294,6 +298,10 @@ def plot_h2_map(network):
 
     assign_location(n)
 
+    h2_storage = n.stores.query("carrier == 'H2'")
+    regions["H2"] = h2_storage.rename(index=h2_storage.bus.map(n.buses.location)).e_nom_opt.div(1e6) # TWh
+    regions["H2"] = regions["H2"].where(regions["H2"] > 0.1)
+
     bus_size_factor = 1e5
     linewidth_factor = 1e4
     # MW below which not drawn
@@ -356,17 +364,20 @@ def plot_h2_map(network):
     n.links.bus0 = n.links.bus0.str.replace(" H2", "")
     n.links.bus1 = n.links.bus1.str.replace(" H2", "")
 
+    proj = ccrs.EqualEarth()
+    regions = regions.to_crs(proj.proj4_init)
+
     fig, ax = plt.subplots(
         figsize=(7, 6),
-        subplot_kw={"projection": ccrs.EqualEarth()}
+        subplot_kw={"projection": proj}
     )
 
     color_h2_pipe = '#b3f3f4'
-    color_retrofit = '#54cacd'
+    color_retrofit = '#499a9c'
     
     bus_colors = {
         "H2 Electrolysis": "#ff29d9",
-        "H2 Fuel Cell": "#6b3161",
+        "H2 Fuel Cell": '#805394'
     }
 
     n.plot(
@@ -391,8 +402,24 @@ def plot_h2_map(network):
         boundaries=map_opts["boundaries"]
     )
 
+    regions.plot(
+        ax=ax,
+        column="H2",
+        cmap='Blues',
+        linewidths=0,
+        legend=True,
+        vmax=10,
+        vmin=0,
+        legend_kwds={
+            "label": "Hydrogen Storage [TWh]",
+            "shrink": 0.7,
+            "extend": "max",
+        },
+    )
+
     sizes = [50, 10]
     labels = [f"{s} GW" for s in sizes]
+    sizes = [s/bus_size_factor*1e3 for s in sizes]
 
     legend_kw = dict(
         loc="upper left",
@@ -403,7 +430,6 @@ def plot_h2_map(network):
     )
 
     add_legend_circles(ax, sizes, labels,
-        scale=bus_size_factor/1e3,
         srid=n.srid,
         patch_kw=dict(facecolor='lightgrey'),
         legend_kw=legend_kw
@@ -446,6 +472,9 @@ def plot_h2_map(network):
         legend_kw=legend_kw
     )
 
+    plt.gca().outline_patch.set_visible(False)
+    ax.set_facecolor("white")
+
     fig.savefig(
         snakemake.output.map.replace("-costs-all","-h2_network"),
         bbox_inches="tight"
@@ -561,6 +590,7 @@ def plot_ch4_map(network):
 
     sizes = [100, 10]
     labels = [f"{s} TWh" for s in sizes]
+    sizes = [s/bus_size_factor*1e6 for s in sizes]
     
     legend_kw = dict(
         loc="upper left",
@@ -575,7 +605,6 @@ def plot_ch4_map(network):
         ax,
         sizes,
         labels,
-        scale=bus_size_factor/1e6,
         srid=n.srid,
         patch_kw=dict(facecolor='lightgrey'),
         legend_kw=legend_kw,
@@ -648,8 +677,8 @@ def plot_map_without(network):
     line_lower_threshold = 200.
     line_upper_threshold = 1e4
     linewidth_factor = 3e3
-    ac_color = "gray"
-    dc_color = "m"
+    ac_color = "rosybrown"
+    dc_color = "darkseagreen"
 
     # hack because impossible to drop buses...
     if "EU gas" in n.buses.index:
@@ -846,6 +875,8 @@ if __name__ == "__main__":
     overrides = override_component_attrs(snakemake.input.overrides)
     n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
 
+    regions = gpd.read_file(snakemake.input.regions).set_index("name")
+
     map_opts = snakemake.config['plotting']['map']
 
     plot_map(n,
@@ -854,7 +885,7 @@ if __name__ == "__main__":
         transmission=False
     )
 
-    plot_h2_map(n)
+    plot_h2_map(n, regions)
     plot_ch4_map(n)
     plot_map_without(n)
 

From b96207c73e900f235eb8df5db7f89928057a1034 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 30 Jun 2022 08:42:18 +0200
Subject: [PATCH 019/243] store config and wildcards in n.meta

---
 scripts/add_brownfield.py         |   1 +
 scripts/add_existing_baseyear.py  |   1 +
 scripts/matplotlibrc              |   4 +
 scripts/plot_network.py.bak       | 737 ++++++++++++++++++++++++++++++
 scripts/prepare_sector_network.py |   1 +
 scripts/solve_network.py          |   1 +
 6 files changed, 745 insertions(+)
 create mode 100644 scripts/matplotlibrc
 create mode 100644 scripts/plot_network.py.bak

diff --git a/scripts/add_brownfield.py b/scripts/add_brownfield.py
index d7418a79..9f13da1f 100644
--- a/scripts/add_brownfield.py
+++ b/scripts/add_brownfield.py
@@ -137,4 +137,5 @@ if __name__ == "__main__":
 
     add_brownfield(n, n_p, year)
 
+    n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output[0])
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 7bb88a00..b4f9dc6e 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -493,4 +493,5 @@ if __name__ == "__main__":
         default_lifetime = snakemake.config['costs']['lifetime']
         add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
 
+    n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output[0])
diff --git a/scripts/matplotlibrc b/scripts/matplotlibrc
new file mode 100644
index 00000000..db5e7ce8
--- /dev/null
+++ b/scripts/matplotlibrc
@@ -0,0 +1,4 @@
+backend: Agg
+font.family: sans-serif
+font.sans-serif: Ubuntu, DejaVu Sans
+image.cmap: viridis
\ No newline at end of file
diff --git a/scripts/plot_network.py.bak b/scripts/plot_network.py.bak
new file mode 100644
index 00000000..4a1bc6d0
--- /dev/null
+++ b/scripts/plot_network.py.bak
@@ -0,0 +1,737 @@
+import pypsa
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+
+from matplotlib.legend_handler import HandlerPatch
+from matplotlib.patches import Circle, Ellipse
+
+from make_summary import assign_carriers
+from plot_summary import rename_techs, preferred_order
+from helper import override_component_attrs
+
+plt.style.use('ggplot')
+
+
+def rename_techs_tyndp(tech):
+    tech = rename_techs(tech)
+    if "heat pump" in tech or "resistive heater" in tech:
+        return "power-to-heat"
+    elif tech in ["H2 Electrolysis", "methanation", "helmeth", "H2 liquefaction"]:
+        return "power-to-gas"
+    elif tech == "H2":
+        return "H2 storage"
+    elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
+        return "gas-to-power/heat"
+    elif "solar" in tech:
+        return "solar"
+    elif tech == "Fischer-Tropsch":
+        return "power-to-liquid"
+    elif "offshore wind" in tech:
+        return "offshore wind"
+    elif "CC" in tech or "sequestration" in tech:
+        return "CCS"
+    else:
+        return tech
+
+
+def make_handler_map_to_scale_circles_as_in(ax, dont_resize_actively=False):
+    fig = ax.get_figure()
+
+    def axes2pt():
+        return np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[0] * (72. / fig.dpi)
+
+    ellipses = []
+    if not dont_resize_actively:
+        def update_width_height(event):
+            dist = axes2pt()
+            for e, radius in ellipses:
+                e.width, e.height = 2. * radius * dist
+        fig.canvas.mpl_connect('resize_event', update_width_height)
+        ax.callbacks.connect('xlim_changed', update_width_height)
+        ax.callbacks.connect('ylim_changed', update_width_height)
+
+    def legend_circle_handler(legend, orig_handle, xdescent, ydescent,
+                              width, height, fontsize):
+        w, h = 2. * orig_handle.get_radius() * axes2pt()
+        e = Ellipse(xy=(0.5 * width - 0.5 * xdescent, 0.5 *
+                        height - 0.5 * ydescent), width=w, height=w)
+        ellipses.append((e, orig_handle.get_radius()))
+        return e
+    return {Circle: HandlerPatch(patch_func=legend_circle_handler)}
+
+
+def make_legend_circles_for(sizes, scale=1.0, **kw):
+    return [Circle((0, 0), radius=(s / scale)**0.5, **kw) for s in sizes]
+
+
+def assign_location(n):
+    for c in n.iterate_components(n.one_port_components | n.branch_components):
+        ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
+        for i in ifind.value_counts().index:
+            # these have already been assigned defaults
+            if i == -1: continue
+            names = ifind.index[ifind == i]
+            c.df.loc[names, 'location'] = names.str[:i]
+
+
+def plot_map(network, components=["links", "stores", "storage_units", "generators"],
+             bus_size_factor=1.7e10, transmission=False):
+
+    n = network.copy()
+    assign_location(n)
+    # Drop non-electric buses so they don't clutter the plot
+    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
+
+    costs = pd.DataFrame(index=n.buses.index)
+
+    for comp in components:
+        df_c = getattr(n, comp)
+        df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
+
+        attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
+
+        costs_c = ((df_c.capital_cost * df_c[attr])
+                   .groupby([df_c.location, df_c.nice_group]).sum()
+                   .unstack().fillna(0.))
+        costs = pd.concat([costs, costs_c], axis=1)
+
+        print(comp, costs)
+
+    costs = costs.groupby(costs.columns, axis=1).sum()
+
+    costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
+
+    new_columns = (preferred_order.intersection(costs.columns)
+                   .append(costs.columns.difference(preferred_order)))
+    costs = costs[new_columns]
+
+    for item in new_columns:
+        if item not in snakemake.config['plotting']['tech_colors']:
+            print("Warning!",item,"not in config/plotting/tech_colors")
+
+    costs = costs.stack()  # .sort_index()
+
+    # hack because impossible to drop buses...
+    eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
+    n.buses.loc["EU gas", "x"] = eu_location["x"]
+    n.buses.loc["EU gas", "y"] = eu_location["y"]
+
+    n.links.drop(n.links.index[(n.links.carrier != "DC") & (
+        n.links.carrier != "B2B")], inplace=True)
+
+    # drop non-bus
+    to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
+    if len(to_drop) != 0:
+        print("dropping non-buses", to_drop)
+        costs.drop(to_drop, level=0, inplace=True, axis=0, errors="ignore")
+
+    # make sure they are removed from index
+    costs.index = pd.MultiIndex.from_tuples(costs.index.values)
+
+    # PDF has minimum width, so set these to zero
+    line_lower_threshold = 500.
+    line_upper_threshold = 1e4
+    linewidth_factor = 2e3
+    ac_color = "gray"
+    dc_color = "m"
+
+    if snakemake.wildcards["lv"] == "1.0":
+        # should be zero
+        line_widths = n.lines.s_nom_opt - n.lines.s_nom
+        link_widths = n.links.p_nom_opt - n.links.p_nom
+        title = "Transmission reinforcement"
+
+        if transmission:
+            line_widths = n.lines.s_nom_opt
+            link_widths = n.links.p_nom_opt
+            linewidth_factor = 2e3
+            line_lower_threshold = 0.
+            title = "Today's transmission"
+    else:
+        line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
+        link_widths = n.links.p_nom_opt - n.links.p_nom_min
+        title = "Transmission reinforcement"
+
+        if transmission:
+            line_widths = n.lines.s_nom_opt
+            link_widths = n.links.p_nom_opt
+            title = "Total transmission"
+
+    line_widths[line_widths < line_lower_threshold] = 0.
+    link_widths[link_widths < line_lower_threshold] = 0.
+
+    line_widths[line_widths > line_upper_threshold] = line_upper_threshold
+    link_widths[link_widths > line_upper_threshold] = line_upper_threshold
+
+    fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
+    fig.set_size_inches(7, 6)
+
+    n.plot(
+        bus_sizes=costs / bus_size_factor,
+        bus_colors=snakemake.config['plotting']['tech_colors'],
+        line_colors=ac_color,
+        link_colors=dc_color,
+        line_widths=line_widths / linewidth_factor,
+        link_widths=link_widths / linewidth_factor,
+        ax=ax,  **map_opts
+    )
+
+    handles = make_legend_circles_for(
+        [5e9, 1e9],
+        scale=bus_size_factor,
+        facecolor="gray"
+    )
+
+    labels = ["{} bEUR/a".format(s) for s in (5, 1)]
+
+    l2 = ax.legend(
+        handles, labels,
+        loc="upper left",
+        bbox_to_anchor=(0.01, 1.01),
+        labelspacing=1.0,
+        frameon=False,
+        title='System cost',
+        handler_map=make_handler_map_to_scale_circles_as_in(ax)
+    )
+
+    ax.add_artist(l2)
+
+    handles = []
+    labels = []
+
+    for s in (10, 5):
+        handles.append(plt.Line2D([0], [0], color=ac_color,
+                                  linewidth=s * 1e3 / linewidth_factor))
+        labels.append("{} GW".format(s))
+
+    l1_1 = ax.legend(
+        handles, labels,
+        loc="upper left",
+        bbox_to_anchor=(0.22, 1.01),
+        frameon=False,
+        labelspacing=0.8,
+        handletextpad=1.5,
+        title=title
+    )
+
+    ax.add_artist(l1_1)
+
+    fig.savefig(
+        snakemake.output.map,
+        transparent=True,
+        bbox_inches="tight"
+    )
+
+def group_pipes(df, drop_direction=False):
+    """Group pipes which connect same buses and return overall capacity.
+    """
+    if drop_direction:
+        positive_order = df.bus0 < df.bus1
+        df_p = df[positive_order]
+        swap_buses = {"bus0": "bus1", "bus1": "bus0"}
+        df_n = df[~positive_order].rename(columns=swap_buses)
+        df = pd.concat([df_p, df_n])
+
+    # there are pipes for each investment period rename to AC buses name for plotting
+    df.index = df.apply(
+        lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
+        axis=1
+    )
+    # group pipe lines connecting the same buses and rename them for plotting
+    pipe_capacity = df["p_nom_opt"].groupby(level=0).sum()
+
+    return pipe_capacity
+
+
+def plot_h2_map(network):
+
+    n = network.copy()
+    if "H2 pipeline" not in n.links.carrier.unique():
+        return
+
+    assign_location(n)
+
+    bus_size_factor = 1e5
+    linewidth_factor = 1e4
+    # MW below which not drawn
+    line_lower_threshold = 1e2
+
+    # Drop non-electric buses so they don't clutter the plot
+    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
+
+    elec = n.links[n.links.carrier.isin(["H2 Electrolysis", "H2 Fuel Cell"])].index
+
+    bus_sizes = n.links.loc[elec,"p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum() / bus_size_factor
+
+    # make a fake MultiIndex so that area is correct for legend
+    bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
+    # drop all links which are not H2 pipelines
+    n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
+
+    h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
+    h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
+    # sum capacitiy for pipelines from different investment periods
+    h2_new = group_pipes(h2_new)
+    h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
+
+
+    n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
+    n.links = n.links.groupby(level=0).first()
+    link_widths_total = (h2_new + h2_retro) / linewidth_factor
+    link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.)
+    link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
+
+    retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
+    link_widths_retro = retro / linewidth_factor
+    link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.
+
+    n.links.bus0 = n.links.bus0.str.replace(" H2", "")
+    n.links.bus1 = n.links.bus1.str.replace(" H2", "")
+
+    fig, ax = plt.subplots(
+        figsize=(7, 6),
+        subplot_kw={"projection": ccrs.PlateCarree()}
+    )
+
+    n.plot(
+        bus_sizes=bus_sizes,
+        bus_colors=snakemake.config['plotting']['tech_colors'],
+        link_colors='#a2f0f2',
+        link_widths=link_widths_total,
+        branch_components=["Link"],
+        ax=ax,
+        **map_opts
+    )
+
+    n.plot(
+        geomap=False,
+        bus_sizes=0,
+        link_colors='#72d3d6',
+        link_widths=link_widths_retro,
+        branch_components=["Link"],
+        ax=ax,
+        **map_opts
+    )
+
+    handles = make_legend_circles_for(
+        [50000, 10000],
+        scale=bus_size_factor,
+        facecolor='grey'
+    )
+
+    labels = ["{} GW".format(s) for s in (50, 10)]
+
+    l2 = ax.legend(
+        handles, labels,
+        loc="upper left",
+        bbox_to_anchor=(-0.03, 1.01),
+        labelspacing=1.0,
+        frameon=False,
+        title='Electrolyzer capacity',
+        handler_map=make_handler_map_to_scale_circles_as_in(ax)
+    )
+
+    ax.add_artist(l2)
+
+    handles = []
+    labels = []
+
+    for s in (50, 10):
+        handles.append(plt.Line2D([0], [0], color="grey",
+                                  linewidth=s * 1e3 / linewidth_factor))
+        labels.append("{} GW".format(s))
+
+    l1_1 = ax.legend(
+        handles, labels,
+        loc="upper left",
+        bbox_to_anchor=(0.28, 1.01),
+        frameon=False,
+        labelspacing=0.8,
+        handletextpad=1.5,
+        title='H2 pipeline capacity'
+    )
+
+    ax.add_artist(l1_1)
+
+    fig.savefig(
+        snakemake.output.map.replace("-costs-all","-h2_network"),
+        bbox_inches="tight"
+    )
+
+
+def plot_ch4_map(network):
+
+    n = network.copy()
+
+    if "gas pipeline" not in n.links.carrier.unique():
+        return
+
+    assign_location(n)
+
+    bus_size_factor = 8e7
+    linewidth_factor = 1e4
+    # MW below which not drawn
+    line_lower_threshold = 500
+
+    # Drop non-electric buses so they don't clutter the plot
+    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
+
+    fossil_gas_i = n.generators[n.generators.carrier=="gas"].index
+    fossil_gas = n.generators_t.p.loc[:,fossil_gas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.generators.loc[fossil_gas_i,"bus"]).sum() / bus_size_factor
+    fossil_gas.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
+    fossil_gas = fossil_gas.reindex(n.buses.index).fillna(0)
+    # make a fake MultiIndex so that area is correct for legend
+    fossil_gas.index = pd.MultiIndex.from_product([fossil_gas.index, ["fossil gas"]])
+
+    methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
+    methanation = abs(n.links_t.p1.loc[:,methanation_i].mul(n.snapshot_weightings.generators, axis=0)).sum().groupby(n.links.loc[methanation_i,"bus1"]).sum() / bus_size_factor
+    methanation = methanation.groupby(methanation.index).sum().rename(index=lambda x: x.replace(" gas", ""))
+    # make a fake MultiIndex so that area is correct for legend
+    methanation.index = pd.MultiIndex.from_product([methanation.index, ["methanation"]])
+
+    biogas_i = n.stores[n.stores.carrier=="biogas"].index
+    biogas = n.stores_t.p.loc[:,biogas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.stores.loc[biogas_i,"bus"]).sum() / bus_size_factor
+    biogas = biogas.groupby(biogas.index).sum().rename(index=lambda x: x.replace(" biogas", ""))
+    # make a fake MultiIndex so that area is correct for legend
+    biogas.index = pd.MultiIndex.from_product([biogas.index, ["biogas"]])
+
+    bus_sizes = pd.concat([fossil_gas, methanation, biogas])
+    bus_sizes.sort_index(inplace=True)
+
+    to_remove = n.links.index[~n.links.carrier.str.contains("gas pipeline")]
+    n.links.drop(to_remove, inplace=True)
+
+    link_widths_rem = n.links.p_nom_opt / linewidth_factor
+    link_widths_rem[n.links.p_nom_opt < line_lower_threshold] = 0.
+
+    link_widths_orig = n.links.p_nom / linewidth_factor
+    link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.
+
+    max_usage = n.links_t.p0.abs().max(axis=0)
+    link_widths_used =  max_usage / linewidth_factor
+    link_widths_used[max_usage < line_lower_threshold] = 0.
+
+    link_color_used = n.links.carrier.map({"gas pipeline": "#f08080",
+                                           "gas pipeline new": "#c46868"})
+
+    n.links.bus0 = n.links.bus0.str.replace(" gas", "")
+    n.links.bus1 = n.links.bus1.str.replace(" gas", "")
+
+    tech_colors = snakemake.config['plotting']['tech_colors']
+
+    bus_colors = {
+        "fossil gas": tech_colors["fossil gas"],
+        "methanation": tech_colors["methanation"],
+        "biogas": "seagreen"
+    }
+
+    fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.PlateCarree()})
+
+    n.plot(
+        bus_sizes=bus_sizes,
+        bus_colors=bus_colors,
+        link_colors='lightgrey',
+        link_widths=link_widths_orig,
+        branch_components=["Link"],
+        ax=ax,
+        **map_opts
+    )
+
+    n.plot(
+        geomap=False,
+        ax=ax,
+        bus_sizes=0.,
+        link_colors='#e8d1d1',
+        link_widths=link_widths_rem,
+        branch_components=["Link"],
+        **map_opts
+    )
+
+    n.plot(
+        geomap=False,
+        ax=ax,
+        bus_sizes=0.,
+        link_colors=link_color_used,
+        link_widths=link_widths_used,
+        branch_components=["Link"],
+        **map_opts
+    )
+
+    handles = make_legend_circles_for(
+        [10e6, 100e6],
+        scale=bus_size_factor,
+        facecolor='grey'
+    )
+    labels = ["{} TWh".format(s) for s in (10, 100)]
+
+    l2 = ax.legend(
+        handles, labels,
+        loc="upper left",
+        bbox_to_anchor=(-0.03, 1.01),
+        labelspacing=1.0,
+        frameon=False,
+        title='gas generation',
+        handler_map=make_handler_map_to_scale_circles_as_in(ax)
+    )
+
+    ax.add_artist(l2)
+
+    handles = []
+    labels = []
+
+    for s in (50, 10):
+        handles.append(plt.Line2D([0], [0], color="grey", linewidth=s * 1e3 / linewidth_factor))
+        labels.append("{} GW".format(s))
+
+    l1_1 = ax.legend(
+        handles, labels,
+        loc="upper left",
+        bbox_to_anchor=(0.28, 1.01),
+        frameon=False,
+        labelspacing=0.8,
+        handletextpad=1.5,
+        title='gas pipeline used capacity'
+    )
+
+    ax.add_artist(l1_1)
+
+    fig.savefig(
+        snakemake.output.map.replace("-costs-all","-ch4_network"),
+        bbox_inches="tight"
+    )
+
+
+def plot_map_without(network):
+
+    n = network.copy()
+    assign_location(n)
+
+    # Drop non-electric buses so they don't clutter the plot
+    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
+
+    fig, ax = plt.subplots(
+        figsize=(7, 6),
+        subplot_kw={"projection": ccrs.PlateCarree()}
+    )
+
+    # PDF has minimum width, so set these to zero
+    line_lower_threshold = 200.
+    line_upper_threshold = 1e4
+    linewidth_factor = 2e3
+    ac_color = "gray"
+    dc_color = "m"
+
+    # hack because impossible to drop buses...
+    if "EU gas" in n.buses.index:
+            eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
+            n.buses.loc["EU gas", "x"] = eu_location["x"]
+            n.buses.loc["EU gas", "y"] = eu_location["y"]
+
+    to_drop = n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")]
+    n.links.drop(to_drop, inplace=True)
+
+    if snakemake.wildcards["lv"] == "1.0":
+        line_widths = n.lines.s_nom
+        link_widths = n.links.p_nom
+    else:
+        line_widths = n.lines.s_nom_min
+        link_widths = n.links.p_nom_min
+
+    line_widths[line_widths < line_lower_threshold] = 0.
+    link_widths[link_widths < line_lower_threshold] = 0.
+
+    line_widths[line_widths > line_upper_threshold] = line_upper_threshold
+    link_widths[link_widths > line_upper_threshold] = line_upper_threshold
+
+    n.plot(
+        bus_colors="k",
+        line_colors=ac_color,
+        link_colors=dc_color,
+        line_widths=line_widths / linewidth_factor,
+        link_widths=link_widths / linewidth_factor,
+        ax=ax, **map_opts
+    )
+
+    handles = []
+    labels = []
+
+    for s in (10, 5):
+        handles.append(plt.Line2D([0], [0], color=ac_color,
+                                  linewidth=s * 1e3 / linewidth_factor))
+        labels.append("{} GW".format(s))
+    l1_1 = ax.legend(handles, labels,
+                     loc="upper left", bbox_to_anchor=(0.05, 1.01),
+                     frameon=False,
+                     labelspacing=0.8, handletextpad=1.5,
+                     title='Today\'s transmission')
+    ax.add_artist(l1_1)
+
+    fig.savefig(
+        snakemake.output.today,
+        transparent=True,
+        bbox_inches="tight"
+    )
+
+
+def plot_series(network, carrier="AC", name="test"):
+
+    n = network.copy()
+    assign_location(n)
+    assign_carriers(n)
+
+    buses = n.buses.index[n.buses.carrier.str.contains(carrier)]
+
+    supply = pd.DataFrame(index=n.snapshots)
+    for c in n.iterate_components(n.branch_components):
+        n_port = 4 if c.name=='Link' else 2
+        for i in range(n_port):
+            supply = pd.concat((supply,
+                                (-1) * c.pnl["p" + str(i)].loc[:,
+                                                               c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(c.df.carrier,
+                                                                                                                     axis=1).sum()),
+                               axis=1)
+
+    for c in n.iterate_components(n.one_port_components):
+        comps = c.df.index[c.df.bus.isin(buses)]
+        supply = pd.concat((supply, ((c.pnl["p"].loc[:, comps]).multiply(
+            c.df.loc[comps, "sign"])).groupby(c.df.carrier, axis=1).sum()), axis=1)
+
+    supply = supply.groupby(rename_techs_tyndp, axis=1).sum()
+
+    both = supply.columns[(supply < 0.).any() & (supply > 0.).any()]
+
+    positive_supply = supply[both]
+    negative_supply = supply[both]
+
+    positive_supply[positive_supply < 0.] = 0.
+    negative_supply[negative_supply > 0.] = 0.
+
+    supply[both] = positive_supply
+
+    suffix = " charging"
+
+    negative_supply.columns = negative_supply.columns + suffix
+
+    supply = pd.concat((supply, negative_supply), axis=1)
+
+    # 14-21.2 for flaute
+    # 19-26.1 for flaute
+
+    start = "2013-02-19"
+    stop = "2013-02-26"
+
+    threshold = 10e3
+
+    to_drop = supply.columns[(abs(supply) < threshold).all()]
+
+    if len(to_drop) != 0:
+        print("dropping", to_drop)
+        supply.drop(columns=to_drop, inplace=True)
+
+    supply.index.name = None
+
+    supply = supply / 1e3
+
+    supply.rename(columns={"electricity": "electric demand",
+                           "heat": "heat demand"},
+                  inplace=True)
+    supply.columns = supply.columns.str.replace("residential ", "")
+    supply.columns = supply.columns.str.replace("services ", "")
+    supply.columns = supply.columns.str.replace("urban decentral ", "decentral ")
+
+    preferred_order = pd.Index(["electric demand",
+                                "transmission lines",
+                                "hydroelectricity",
+                                "hydro reservoir",
+                                "run of river",
+                                "pumped hydro storage",
+                                "CHP",
+                                "onshore wind",
+                                "offshore wind",
+                                "solar PV",
+                                "solar thermal",
+                                "building retrofitting",
+                                "ground heat pump",
+                                "air heat pump",
+                                "resistive heater",
+                                "OCGT",
+                                "gas boiler",
+                                "gas",
+                                "natural gas",
+                                "methanation",
+                                "hydrogen storage",
+                                "battery storage",
+                                "hot water storage"])
+
+    new_columns = (preferred_order.intersection(supply.columns)
+                   .append(supply.columns.difference(preferred_order)))
+
+    supply =  supply.groupby(supply.columns, axis=1).sum()
+    fig, ax = plt.subplots()
+    fig.set_size_inches((8, 5))
+
+    (supply.loc[start:stop, new_columns]
+     .plot(ax=ax, kind="area", stacked=True, linewidth=0.,
+           color=[snakemake.config['plotting']['tech_colors'][i.replace(suffix, "")]
+                  for i in new_columns]))
+
+    handles, labels = ax.get_legend_handles_labels()
+
+    handles.reverse()
+    labels.reverse()
+
+    new_handles = []
+    new_labels = []
+
+    for i, item in enumerate(labels):
+        if "charging" not in item:
+            new_handles.append(handles[i])
+            new_labels.append(labels[i])
+
+    ax.legend(new_handles, new_labels, ncol=3, loc="upper left", frameon=False)
+    ax.set_xlim([start, stop])
+    ax.set_ylim([-1300, 1900])
+    ax.grid(True)
+    ax.set_ylabel("Power [GW]")
+    fig.tight_layout()
+
+    fig.savefig("{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
+        snakemake.config['results_dir'], snakemake.config['run'],
+        snakemake.wildcards["lv"],
+        carrier, start, stop, name),
+        transparent=True)
+
+
+if __name__ == "__main__":
+    if 'snakemake' not in globals():
+        from helper import mock_snakemake
+        snakemake = mock_snakemake(
+            'plot_network',
+            simpl='',
+            clusters="45",
+            lv=1.0,
+            opts='',
+            sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
+            planning_horizons="2050",
+        )
+
+    overrides = override_component_attrs(snakemake.input.overrides)
+    n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
+
+    map_opts = snakemake.config['plotting']['map']
+
+    plot_map(n,
+        components=["generators", "links", "stores", "storage_units"],
+        bus_size_factor=1.5e10,
+        transmission=False
+    )
+
+    plot_h2_map(n)
+    plot_ch4_map(n)
+    plot_map_without(n)
+
+    #plot_series(n, carrier="AC", name=suffix)
+    #plot_series(n, carrier="heat", name=suffix)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 5f75b1db..a1916c05 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2447,4 +2447,5 @@ if __name__ == "__main__":
     if options['electricity_grid_connection']:
         add_electricity_grid_connection(n, costs)
 
+    n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
     n.export_to_netcdf(snakemake.output[0])
diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index 4f6cc2c4..3c815e12 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -313,6 +313,7 @@ if __name__ == "__main__":
             n.line_volume_limit = n.global_constraints.at["lv_limit", "constant"]
             n.line_volume_limit_dual = n.global_constraints.at["lv_limit", "mu"]
 
+        n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
         n.export_to_netcdf(snakemake.output[0])
 
     logger.info("Maximum memory usage: {}".format(mem.mem_usage))

From 3134bbb5c5da97b43b3e1039846c669ed76b7a97 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 30 Jun 2022 08:43:46 +0200
Subject: [PATCH 020/243] remove accidental file additions

---
 scripts/matplotlibrc        |   4 -
 scripts/plot_network.py.bak | 737 ------------------------------------
 2 files changed, 741 deletions(-)
 delete mode 100644 scripts/matplotlibrc
 delete mode 100644 scripts/plot_network.py.bak

diff --git a/scripts/matplotlibrc b/scripts/matplotlibrc
deleted file mode 100644
index db5e7ce8..00000000
--- a/scripts/matplotlibrc
+++ /dev/null
@@ -1,4 +0,0 @@
-backend: Agg
-font.family: sans-serif
-font.sans-serif: Ubuntu, DejaVu Sans
-image.cmap: viridis
\ No newline at end of file
diff --git a/scripts/plot_network.py.bak b/scripts/plot_network.py.bak
deleted file mode 100644
index 4a1bc6d0..00000000
--- a/scripts/plot_network.py.bak
+++ /dev/null
@@ -1,737 +0,0 @@
-import pypsa
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import cartopy.crs as ccrs
-
-from matplotlib.legend_handler import HandlerPatch
-from matplotlib.patches import Circle, Ellipse
-
-from make_summary import assign_carriers
-from plot_summary import rename_techs, preferred_order
-from helper import override_component_attrs
-
-plt.style.use('ggplot')
-
-
-def rename_techs_tyndp(tech):
-    tech = rename_techs(tech)
-    if "heat pump" in tech or "resistive heater" in tech:
-        return "power-to-heat"
-    elif tech in ["H2 Electrolysis", "methanation", "helmeth", "H2 liquefaction"]:
-        return "power-to-gas"
-    elif tech == "H2":
-        return "H2 storage"
-    elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
-        return "gas-to-power/heat"
-    elif "solar" in tech:
-        return "solar"
-    elif tech == "Fischer-Tropsch":
-        return "power-to-liquid"
-    elif "offshore wind" in tech:
-        return "offshore wind"
-    elif "CC" in tech or "sequestration" in tech:
-        return "CCS"
-    else:
-        return tech
-
-
-def make_handler_map_to_scale_circles_as_in(ax, dont_resize_actively=False):
-    fig = ax.get_figure()
-
-    def axes2pt():
-        return np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[0] * (72. / fig.dpi)
-
-    ellipses = []
-    if not dont_resize_actively:
-        def update_width_height(event):
-            dist = axes2pt()
-            for e, radius in ellipses:
-                e.width, e.height = 2. * radius * dist
-        fig.canvas.mpl_connect('resize_event', update_width_height)
-        ax.callbacks.connect('xlim_changed', update_width_height)
-        ax.callbacks.connect('ylim_changed', update_width_height)
-
-    def legend_circle_handler(legend, orig_handle, xdescent, ydescent,
-                              width, height, fontsize):
-        w, h = 2. * orig_handle.get_radius() * axes2pt()
-        e = Ellipse(xy=(0.5 * width - 0.5 * xdescent, 0.5 *
-                        height - 0.5 * ydescent), width=w, height=w)
-        ellipses.append((e, orig_handle.get_radius()))
-        return e
-    return {Circle: HandlerPatch(patch_func=legend_circle_handler)}
-
-
-def make_legend_circles_for(sizes, scale=1.0, **kw):
-    return [Circle((0, 0), radius=(s / scale)**0.5, **kw) for s in sizes]
-
-
-def assign_location(n):
-    for c in n.iterate_components(n.one_port_components | n.branch_components):
-        ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
-        for i in ifind.value_counts().index:
-            # these have already been assigned defaults
-            if i == -1: continue
-            names = ifind.index[ifind == i]
-            c.df.loc[names, 'location'] = names.str[:i]
-
-
-def plot_map(network, components=["links", "stores", "storage_units", "generators"],
-             bus_size_factor=1.7e10, transmission=False):
-
-    n = network.copy()
-    assign_location(n)
-    # Drop non-electric buses so they don't clutter the plot
-    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
-
-    costs = pd.DataFrame(index=n.buses.index)
-
-    for comp in components:
-        df_c = getattr(n, comp)
-        df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
-
-        attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
-
-        costs_c = ((df_c.capital_cost * df_c[attr])
-                   .groupby([df_c.location, df_c.nice_group]).sum()
-                   .unstack().fillna(0.))
-        costs = pd.concat([costs, costs_c], axis=1)
-
-        print(comp, costs)
-
-    costs = costs.groupby(costs.columns, axis=1).sum()
-
-    costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
-
-    new_columns = (preferred_order.intersection(costs.columns)
-                   .append(costs.columns.difference(preferred_order)))
-    costs = costs[new_columns]
-
-    for item in new_columns:
-        if item not in snakemake.config['plotting']['tech_colors']:
-            print("Warning!",item,"not in config/plotting/tech_colors")
-
-    costs = costs.stack()  # .sort_index()
-
-    # hack because impossible to drop buses...
-    eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
-    n.buses.loc["EU gas", "x"] = eu_location["x"]
-    n.buses.loc["EU gas", "y"] = eu_location["y"]
-
-    n.links.drop(n.links.index[(n.links.carrier != "DC") & (
-        n.links.carrier != "B2B")], inplace=True)
-
-    # drop non-bus
-    to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
-    if len(to_drop) != 0:
-        print("dropping non-buses", to_drop)
-        costs.drop(to_drop, level=0, inplace=True, axis=0, errors="ignore")
-
-    # make sure they are removed from index
-    costs.index = pd.MultiIndex.from_tuples(costs.index.values)
-
-    # PDF has minimum width, so set these to zero
-    line_lower_threshold = 500.
-    line_upper_threshold = 1e4
-    linewidth_factor = 2e3
-    ac_color = "gray"
-    dc_color = "m"
-
-    if snakemake.wildcards["lv"] == "1.0":
-        # should be zero
-        line_widths = n.lines.s_nom_opt - n.lines.s_nom
-        link_widths = n.links.p_nom_opt - n.links.p_nom
-        title = "Transmission reinforcement"
-
-        if transmission:
-            line_widths = n.lines.s_nom_opt
-            link_widths = n.links.p_nom_opt
-            linewidth_factor = 2e3
-            line_lower_threshold = 0.
-            title = "Today's transmission"
-    else:
-        line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
-        link_widths = n.links.p_nom_opt - n.links.p_nom_min
-        title = "Transmission reinforcement"
-
-        if transmission:
-            line_widths = n.lines.s_nom_opt
-            link_widths = n.links.p_nom_opt
-            title = "Total transmission"
-
-    line_widths[line_widths < line_lower_threshold] = 0.
-    link_widths[link_widths < line_lower_threshold] = 0.
-
-    line_widths[line_widths > line_upper_threshold] = line_upper_threshold
-    link_widths[link_widths > line_upper_threshold] = line_upper_threshold
-
-    fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
-    fig.set_size_inches(7, 6)
-
-    n.plot(
-        bus_sizes=costs / bus_size_factor,
-        bus_colors=snakemake.config['plotting']['tech_colors'],
-        line_colors=ac_color,
-        link_colors=dc_color,
-        line_widths=line_widths / linewidth_factor,
-        link_widths=link_widths / linewidth_factor,
-        ax=ax,  **map_opts
-    )
-
-    handles = make_legend_circles_for(
-        [5e9, 1e9],
-        scale=bus_size_factor,
-        facecolor="gray"
-    )
-
-    labels = ["{} bEUR/a".format(s) for s in (5, 1)]
-
-    l2 = ax.legend(
-        handles, labels,
-        loc="upper left",
-        bbox_to_anchor=(0.01, 1.01),
-        labelspacing=1.0,
-        frameon=False,
-        title='System cost',
-        handler_map=make_handler_map_to_scale_circles_as_in(ax)
-    )
-
-    ax.add_artist(l2)
-
-    handles = []
-    labels = []
-
-    for s in (10, 5):
-        handles.append(plt.Line2D([0], [0], color=ac_color,
-                                  linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
-
-    l1_1 = ax.legend(
-        handles, labels,
-        loc="upper left",
-        bbox_to_anchor=(0.22, 1.01),
-        frameon=False,
-        labelspacing=0.8,
-        handletextpad=1.5,
-        title=title
-    )
-
-    ax.add_artist(l1_1)
-
-    fig.savefig(
-        snakemake.output.map,
-        transparent=True,
-        bbox_inches="tight"
-    )
-
-def group_pipes(df, drop_direction=False):
-    """Group pipes which connect same buses and return overall capacity.
-    """
-    if drop_direction:
-        positive_order = df.bus0 < df.bus1
-        df_p = df[positive_order]
-        swap_buses = {"bus0": "bus1", "bus1": "bus0"}
-        df_n = df[~positive_order].rename(columns=swap_buses)
-        df = pd.concat([df_p, df_n])
-
-    # there are pipes for each investment period rename to AC buses name for plotting
-    df.index = df.apply(
-        lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
-        axis=1
-    )
-    # group pipe lines connecting the same buses and rename them for plotting
-    pipe_capacity = df["p_nom_opt"].groupby(level=0).sum()
-
-    return pipe_capacity
-
-
-def plot_h2_map(network):
-
-    n = network.copy()
-    if "H2 pipeline" not in n.links.carrier.unique():
-        return
-
-    assign_location(n)
-
-    bus_size_factor = 1e5
-    linewidth_factor = 1e4
-    # MW below which not drawn
-    line_lower_threshold = 1e2
-
-    # Drop non-electric buses so they don't clutter the plot
-    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
-
-    elec = n.links[n.links.carrier.isin(["H2 Electrolysis", "H2 Fuel Cell"])].index
-
-    bus_sizes = n.links.loc[elec,"p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum() / bus_size_factor
-
-    # make a fake MultiIndex so that area is correct for legend
-    bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
-    # drop all links which are not H2 pipelines
-    n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
-
-    h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
-    h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
-    # sum capacitiy for pipelines from different investment periods
-    h2_new = group_pipes(h2_new)
-    h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
-
-
-    n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
-    n.links = n.links.groupby(level=0).first()
-    link_widths_total = (h2_new + h2_retro) / linewidth_factor
-    link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.)
-    link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
-
-    retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
-    link_widths_retro = retro / linewidth_factor
-    link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.
-
-    n.links.bus0 = n.links.bus0.str.replace(" H2", "")
-    n.links.bus1 = n.links.bus1.str.replace(" H2", "")
-
-    fig, ax = plt.subplots(
-        figsize=(7, 6),
-        subplot_kw={"projection": ccrs.PlateCarree()}
-    )
-
-    n.plot(
-        bus_sizes=bus_sizes,
-        bus_colors=snakemake.config['plotting']['tech_colors'],
-        link_colors='#a2f0f2',
-        link_widths=link_widths_total,
-        branch_components=["Link"],
-        ax=ax,
-        **map_opts
-    )
-
-    n.plot(
-        geomap=False,
-        bus_sizes=0,
-        link_colors='#72d3d6',
-        link_widths=link_widths_retro,
-        branch_components=["Link"],
-        ax=ax,
-        **map_opts
-    )
-
-    handles = make_legend_circles_for(
-        [50000, 10000],
-        scale=bus_size_factor,
-        facecolor='grey'
-    )
-
-    labels = ["{} GW".format(s) for s in (50, 10)]
-
-    l2 = ax.legend(
-        handles, labels,
-        loc="upper left",
-        bbox_to_anchor=(-0.03, 1.01),
-        labelspacing=1.0,
-        frameon=False,
-        title='Electrolyzer capacity',
-        handler_map=make_handler_map_to_scale_circles_as_in(ax)
-    )
-
-    ax.add_artist(l2)
-
-    handles = []
-    labels = []
-
-    for s in (50, 10):
-        handles.append(plt.Line2D([0], [0], color="grey",
-                                  linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
-
-    l1_1 = ax.legend(
-        handles, labels,
-        loc="upper left",
-        bbox_to_anchor=(0.28, 1.01),
-        frameon=False,
-        labelspacing=0.8,
-        handletextpad=1.5,
-        title='H2 pipeline capacity'
-    )
-
-    ax.add_artist(l1_1)
-
-    fig.savefig(
-        snakemake.output.map.replace("-costs-all","-h2_network"),
-        bbox_inches="tight"
-    )
-
-
-def plot_ch4_map(network):
-
-    n = network.copy()
-
-    if "gas pipeline" not in n.links.carrier.unique():
-        return
-
-    assign_location(n)
-
-    bus_size_factor = 8e7
-    linewidth_factor = 1e4
-    # MW below which not drawn
-    line_lower_threshold = 500
-
-    # Drop non-electric buses so they don't clutter the plot
-    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
-
-    fossil_gas_i = n.generators[n.generators.carrier=="gas"].index
-    fossil_gas = n.generators_t.p.loc[:,fossil_gas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.generators.loc[fossil_gas_i,"bus"]).sum() / bus_size_factor
-    fossil_gas.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
-    fossil_gas = fossil_gas.reindex(n.buses.index).fillna(0)
-    # make a fake MultiIndex so that area is correct for legend
-    fossil_gas.index = pd.MultiIndex.from_product([fossil_gas.index, ["fossil gas"]])
-
-    methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
-    methanation = abs(n.links_t.p1.loc[:,methanation_i].mul(n.snapshot_weightings.generators, axis=0)).sum().groupby(n.links.loc[methanation_i,"bus1"]).sum() / bus_size_factor
-    methanation = methanation.groupby(methanation.index).sum().rename(index=lambda x: x.replace(" gas", ""))
-    # make a fake MultiIndex so that area is correct for legend
-    methanation.index = pd.MultiIndex.from_product([methanation.index, ["methanation"]])
-
-    biogas_i = n.stores[n.stores.carrier=="biogas"].index
-    biogas = n.stores_t.p.loc[:,biogas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.stores.loc[biogas_i,"bus"]).sum() / bus_size_factor
-    biogas = biogas.groupby(biogas.index).sum().rename(index=lambda x: x.replace(" biogas", ""))
-    # make a fake MultiIndex so that area is correct for legend
-    biogas.index = pd.MultiIndex.from_product([biogas.index, ["biogas"]])
-
-    bus_sizes = pd.concat([fossil_gas, methanation, biogas])
-    bus_sizes.sort_index(inplace=True)
-
-    to_remove = n.links.index[~n.links.carrier.str.contains("gas pipeline")]
-    n.links.drop(to_remove, inplace=True)
-
-    link_widths_rem = n.links.p_nom_opt / linewidth_factor
-    link_widths_rem[n.links.p_nom_opt < line_lower_threshold] = 0.
-
-    link_widths_orig = n.links.p_nom / linewidth_factor
-    link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.
-
-    max_usage = n.links_t.p0.abs().max(axis=0)
-    link_widths_used =  max_usage / linewidth_factor
-    link_widths_used[max_usage < line_lower_threshold] = 0.
-
-    link_color_used = n.links.carrier.map({"gas pipeline": "#f08080",
-                                           "gas pipeline new": "#c46868"})
-
-    n.links.bus0 = n.links.bus0.str.replace(" gas", "")
-    n.links.bus1 = n.links.bus1.str.replace(" gas", "")
-
-    tech_colors = snakemake.config['plotting']['tech_colors']
-
-    bus_colors = {
-        "fossil gas": tech_colors["fossil gas"],
-        "methanation": tech_colors["methanation"],
-        "biogas": "seagreen"
-    }
-
-    fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.PlateCarree()})
-
-    n.plot(
-        bus_sizes=bus_sizes,
-        bus_colors=bus_colors,
-        link_colors='lightgrey',
-        link_widths=link_widths_orig,
-        branch_components=["Link"],
-        ax=ax,
-        **map_opts
-    )
-
-    n.plot(
-        geomap=False,
-        ax=ax,
-        bus_sizes=0.,
-        link_colors='#e8d1d1',
-        link_widths=link_widths_rem,
-        branch_components=["Link"],
-        **map_opts
-    )
-
-    n.plot(
-        geomap=False,
-        ax=ax,
-        bus_sizes=0.,
-        link_colors=link_color_used,
-        link_widths=link_widths_used,
-        branch_components=["Link"],
-        **map_opts
-    )
-
-    handles = make_legend_circles_for(
-        [10e6, 100e6],
-        scale=bus_size_factor,
-        facecolor='grey'
-    )
-    labels = ["{} TWh".format(s) for s in (10, 100)]
-
-    l2 = ax.legend(
-        handles, labels,
-        loc="upper left",
-        bbox_to_anchor=(-0.03, 1.01),
-        labelspacing=1.0,
-        frameon=False,
-        title='gas generation',
-        handler_map=make_handler_map_to_scale_circles_as_in(ax)
-    )
-
-    ax.add_artist(l2)
-
-    handles = []
-    labels = []
-
-    for s in (50, 10):
-        handles.append(plt.Line2D([0], [0], color="grey", linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
-
-    l1_1 = ax.legend(
-        handles, labels,
-        loc="upper left",
-        bbox_to_anchor=(0.28, 1.01),
-        frameon=False,
-        labelspacing=0.8,
-        handletextpad=1.5,
-        title='gas pipeline used capacity'
-    )
-
-    ax.add_artist(l1_1)
-
-    fig.savefig(
-        snakemake.output.map.replace("-costs-all","-ch4_network"),
-        bbox_inches="tight"
-    )
-
-
-def plot_map_without(network):
-
-    n = network.copy()
-    assign_location(n)
-
-    # Drop non-electric buses so they don't clutter the plot
-    n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
-
-    fig, ax = plt.subplots(
-        figsize=(7, 6),
-        subplot_kw={"projection": ccrs.PlateCarree()}
-    )
-
-    # PDF has minimum width, so set these to zero
-    line_lower_threshold = 200.
-    line_upper_threshold = 1e4
-    linewidth_factor = 2e3
-    ac_color = "gray"
-    dc_color = "m"
-
-    # hack because impossible to drop buses...
-    if "EU gas" in n.buses.index:
-            eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
-            n.buses.loc["EU gas", "x"] = eu_location["x"]
-            n.buses.loc["EU gas", "y"] = eu_location["y"]
-
-    to_drop = n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")]
-    n.links.drop(to_drop, inplace=True)
-
-    if snakemake.wildcards["lv"] == "1.0":
-        line_widths = n.lines.s_nom
-        link_widths = n.links.p_nom
-    else:
-        line_widths = n.lines.s_nom_min
-        link_widths = n.links.p_nom_min
-
-    line_widths[line_widths < line_lower_threshold] = 0.
-    link_widths[link_widths < line_lower_threshold] = 0.
-
-    line_widths[line_widths > line_upper_threshold] = line_upper_threshold
-    link_widths[link_widths > line_upper_threshold] = line_upper_threshold
-
-    n.plot(
-        bus_colors="k",
-        line_colors=ac_color,
-        link_colors=dc_color,
-        line_widths=line_widths / linewidth_factor,
-        link_widths=link_widths / linewidth_factor,
-        ax=ax, **map_opts
-    )
-
-    handles = []
-    labels = []
-
-    for s in (10, 5):
-        handles.append(plt.Line2D([0], [0], color=ac_color,
-                                  linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
-    l1_1 = ax.legend(handles, labels,
-                     loc="upper left", bbox_to_anchor=(0.05, 1.01),
-                     frameon=False,
-                     labelspacing=0.8, handletextpad=1.5,
-                     title='Today\'s transmission')
-    ax.add_artist(l1_1)
-
-    fig.savefig(
-        snakemake.output.today,
-        transparent=True,
-        bbox_inches="tight"
-    )
-
-
-def plot_series(network, carrier="AC", name="test"):
-
-    n = network.copy()
-    assign_location(n)
-    assign_carriers(n)
-
-    buses = n.buses.index[n.buses.carrier.str.contains(carrier)]
-
-    supply = pd.DataFrame(index=n.snapshots)
-    for c in n.iterate_components(n.branch_components):
-        n_port = 4 if c.name=='Link' else 2
-        for i in range(n_port):
-            supply = pd.concat((supply,
-                                (-1) * c.pnl["p" + str(i)].loc[:,
-                                                               c.df.index[c.df["bus" + str(i)].isin(buses)]].groupby(c.df.carrier,
-                                                                                                                     axis=1).sum()),
-                               axis=1)
-
-    for c in n.iterate_components(n.one_port_components):
-        comps = c.df.index[c.df.bus.isin(buses)]
-        supply = pd.concat((supply, ((c.pnl["p"].loc[:, comps]).multiply(
-            c.df.loc[comps, "sign"])).groupby(c.df.carrier, axis=1).sum()), axis=1)
-
-    supply = supply.groupby(rename_techs_tyndp, axis=1).sum()
-
-    both = supply.columns[(supply < 0.).any() & (supply > 0.).any()]
-
-    positive_supply = supply[both]
-    negative_supply = supply[both]
-
-    positive_supply[positive_supply < 0.] = 0.
-    negative_supply[negative_supply > 0.] = 0.
-
-    supply[both] = positive_supply
-
-    suffix = " charging"
-
-    negative_supply.columns = negative_supply.columns + suffix
-
-    supply = pd.concat((supply, negative_supply), axis=1)
-
-    # 14-21.2 for flaute
-    # 19-26.1 for flaute
-
-    start = "2013-02-19"
-    stop = "2013-02-26"
-
-    threshold = 10e3
-
-    to_drop = supply.columns[(abs(supply) < threshold).all()]
-
-    if len(to_drop) != 0:
-        print("dropping", to_drop)
-        supply.drop(columns=to_drop, inplace=True)
-
-    supply.index.name = None
-
-    supply = supply / 1e3
-
-    supply.rename(columns={"electricity": "electric demand",
-                           "heat": "heat demand"},
-                  inplace=True)
-    supply.columns = supply.columns.str.replace("residential ", "")
-    supply.columns = supply.columns.str.replace("services ", "")
-    supply.columns = supply.columns.str.replace("urban decentral ", "decentral ")
-
-    preferred_order = pd.Index(["electric demand",
-                                "transmission lines",
-                                "hydroelectricity",
-                                "hydro reservoir",
-                                "run of river",
-                                "pumped hydro storage",
-                                "CHP",
-                                "onshore wind",
-                                "offshore wind",
-                                "solar PV",
-                                "solar thermal",
-                                "building retrofitting",
-                                "ground heat pump",
-                                "air heat pump",
-                                "resistive heater",
-                                "OCGT",
-                                "gas boiler",
-                                "gas",
-                                "natural gas",
-                                "methanation",
-                                "hydrogen storage",
-                                "battery storage",
-                                "hot water storage"])
-
-    new_columns = (preferred_order.intersection(supply.columns)
-                   .append(supply.columns.difference(preferred_order)))
-
-    supply =  supply.groupby(supply.columns, axis=1).sum()
-    fig, ax = plt.subplots()
-    fig.set_size_inches((8, 5))
-
-    (supply.loc[start:stop, new_columns]
-     .plot(ax=ax, kind="area", stacked=True, linewidth=0.,
-           color=[snakemake.config['plotting']['tech_colors'][i.replace(suffix, "")]
-                  for i in new_columns]))
-
-    handles, labels = ax.get_legend_handles_labels()
-
-    handles.reverse()
-    labels.reverse()
-
-    new_handles = []
-    new_labels = []
-
-    for i, item in enumerate(labels):
-        if "charging" not in item:
-            new_handles.append(handles[i])
-            new_labels.append(labels[i])
-
-    ax.legend(new_handles, new_labels, ncol=3, loc="upper left", frameon=False)
-    ax.set_xlim([start, stop])
-    ax.set_ylim([-1300, 1900])
-    ax.grid(True)
-    ax.set_ylabel("Power [GW]")
-    fig.tight_layout()
-
-    fig.savefig("{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
-        snakemake.config['results_dir'], snakemake.config['run'],
-        snakemake.wildcards["lv"],
-        carrier, start, stop, name),
-        transparent=True)
-
-
-if __name__ == "__main__":
-    if 'snakemake' not in globals():
-        from helper import mock_snakemake
-        snakemake = mock_snakemake(
-            'plot_network',
-            simpl='',
-            clusters="45",
-            lv=1.0,
-            opts='',
-            sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
-            planning_horizons="2050",
-        )
-
-    overrides = override_component_attrs(snakemake.input.overrides)
-    n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
-
-    map_opts = snakemake.config['plotting']['map']
-
-    plot_map(n,
-        components=["generators", "links", "stores", "storage_units"],
-        bus_size_factor=1.5e10,
-        transmission=False
-    )
-
-    plot_h2_map(n)
-    plot_ch4_map(n)
-    plot_map_without(n)
-
-    #plot_series(n, carrier="AC", name=suffix)
-    #plot_series(n, carrier="heat", name=suffix)

From 8143f7e0fda9ac4baf477b289939a14038884288 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Fri, 1 Jul 2022 10:16:11 +0200
Subject: [PATCH 021/243] remove accidental config file additions

---
 config.co2seq.yaml    | 595 ---------------------------------------
 config.cost.yaml      | 594 ---------------------------------------
 config.decentral.yaml | 592 ---------------------------------------
 config.gas.yaml       | 590 ---------------------------------------
 config.h2.yaml        | 596 ----------------------------------------
 config.import.yaml    | 626 ------------------------------------------
 config.lv.yaml        | 594 ---------------------------------------
 config.onw.yaml       | 594 ---------------------------------------
 config.spatial.yaml   | 593 ---------------------------------------
 config.temporal.yaml  | 590 ---------------------------------------
 10 files changed, 5964 deletions(-)
 delete mode 100644 config.co2seq.yaml
 delete mode 100644 config.cost.yaml
 delete mode 100644 config.decentral.yaml
 delete mode 100644 config.gas.yaml
 delete mode 100644 config.h2.yaml
 delete mode 100644 config.import.yaml
 delete mode 100644 config.lv.yaml
 delete mode 100644 config.onw.yaml
 delete mode 100644 config.spatial.yaml
 delete mode 100644 config.temporal.yaml

diff --git a/config.co2seq.yaml b/config.co2seq.yaml
deleted file mode 100644
index 7d3fa6c4..00000000
--- a/config.co2seq.yaml
+++ /dev/null
@@ -1,595 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-co2seq  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.5
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq50
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq100
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq200
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq400
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq600
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq800
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq1000
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.cost.yaml b/config.cost.yaml
deleted file mode 100644
index 402b5943..00000000
--- a/config.cost.yaml
+++ /dev/null
@@ -1,594 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-cost  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.5
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-solar-c0.75
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-wind-c0.75
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-Electrolysis-c0.75
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-SMR-c0.75
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-battery-c0.75
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-DAC-c0.75
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.decentral.yaml b/config.decentral.yaml
deleted file mode 100644
index 5d1489ea..00000000
--- a/config.decentral.yaml
+++ /dev/null
@@ -1,592 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-decentral  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - opt
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral-noH2network
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral-onwind+p0
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral-noH2network-onwind+p0
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.gas.yaml b/config.gas.yaml
deleted file mode 100644
index e31abd80..00000000
--- a/config.gas.yaml
+++ /dev/null
@@ -1,590 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-gas  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.0
-    - 1.5
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: true
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 170000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.h2.yaml b/config.h2.yaml
deleted file mode 100644
index b69e7955..00000000
--- a/config.h2.yaml
+++ /dev/null
@@ -1,596 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-h2  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.0
-    #- 1.25
-    - opt
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-noH2network
-    #- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-noH2network-onwind+p0.25
-    #- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.25
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-noH2network-onwind+p0
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.import.yaml b/config.import.yaml
deleted file mode 100644
index f5a6cc20..00000000
--- a/config.import.yaml
+++ /dev/null
@@ -1,626 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-import  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.5
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur 
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-import
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: [2030] # investment years for myopic and perfect; or costs year for overnight
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 3 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-  import:
-    capacity_boost: 2
-    options:
-      - pipeline-h2
-      - shipping-lh2
-      - shipping-lch4
-      - shipping-ftfuel
-      - hvdc
-    # limit: 100 # TWh
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-  import:
-  capacity_boost: 2
-  options:
-    - pipeline-h2
-    - shipping-lh2
-    - shipping-lch4
-    - shipping-ftfuel
-    - hvdc
-  # limit: 100 # TWh
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 8
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 160000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    fossil gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    gas pipeline new: '#a87c62'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    solid: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    biomass: '#baa741'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#d8f9b8'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    hydrogen: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    H2 storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 pipeline retrofitted: '#ba99b5'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    methane: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    liquid: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#f29dae'
-    CCS: '#f29dae'
-    CO2 sequestration: '#f29dae'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#2fb537'
-    power-to-gas: '#c44ce6'
-    power-to-H2: '#ff29d9'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
-    waste: '#e3d37d'
-    other: '#000000'
-    import pipeline-h2: '#fff6e0'
-    import shipping-lh2: '#ebe1ca'
-    import shipping-lch4: '#d6cbb2'
-    import shipping-ftfuel: '#bdb093'
-    import hvdc: '#91856a'
-
diff --git a/config.lv.yaml b/config.lv.yaml
deleted file mode 100644
index 04a10a3c..00000000
--- a/config.lv.yaml
+++ /dev/null
@@ -1,594 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-lv  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.0
-    - 1.125
-    - 1.25
-    - 1.5
-    - 1.75
-    - 2.0
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.onw.yaml b/config.onw.yaml
deleted file mode 100644
index 6b1e31f2..00000000
--- a/config.onw.yaml
+++ /dev/null
@@ -1,594 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-onw  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.25
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.125
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.25
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.75
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p1
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.spatial.yaml b/config.spatial.yaml
deleted file mode 100644
index 44b4e90d..00000000
--- a/config.spatial.yaml
+++ /dev/null
@@ -1,593 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-spatial  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.5
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 37
-    - 50
-    - 100
-    - 150
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'
diff --git a/config.temporal.yaml b/config.temporal.yaml
deleted file mode 100644
index 7b5b6b01..00000000
--- a/config.temporal.yaml
+++ /dev/null
@@ -1,590 +0,0 @@
-version: 0.6.0
-
-logging_level: INFO
-
-results_dir: results/
-summary_dir: results
-costs_dir: ../technology-data/outputs/
-run: 20211218-181-temporal  # use this to keep track of runs with different settings
-foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
-# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
-
-scenario:
-  simpl: # only relevant for PyPSA-Eur
-    - ''
-  lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
-    - 1.5
-  clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
-    - 181
-  opts: # only relevant for PyPSA-Eur
-    - ''
-  sector_opts: # this is where the main scenario settings are
-    - Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5
-    - Co2L0-4H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5
-  # to really understand the options here, look in scripts/prepare_sector_network.py
-  # Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
-  # Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
-  # xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
-  # single letters are sectors: T for land transport, H for building heating,
-  # B for biomass supply, I for industry, shipping and aviation,
-  # A for agriculture, forestry and fishing
-  # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
-  # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
-  # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
-  # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
-  # emissions throughout the transition path in the timeframe determined by the
-  # planning_horizons), be:beta decay; ex:exponential decay
-  # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
-  # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
-    - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
-
-# CO2 budget as a fraction of 1990 emissions
-# this is over-ridden if CO2Lx is set in sector_opts
-# this is also over-ridden if cb is set in sector_opts
-co2_budget:
-  2020: 0.7011648746
-  2025: 0.5241935484
-  2030: 0.2970430108
-  2035: 0.1500896057
-  2040: 0.0712365591
-  2045: 0.0322580645
-  2050: 0
-
-# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
-snapshots:
-  # arguments to pd.date_range
-  start: "2013-01-01"
-  end: "2014-01-01"
-  closed: left # end is not inclusive
-
-atlite:
-  cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
-
-# this information is NOT used but needed as an argument for
-# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
-electricity:
-  max_hours:
-    battery: 6
-    H2: 168
-
-# regulate what components with which carriers are kept from PyPSA-Eur;
-# some technologies are removed because they are implemented differently
-# (e.g. battery or H2 storage) or have different year-dependent costs
-# in PyPSA-Eur-Sec
-pypsa_eur:
-  Bus:
-    - AC
-  Link:
-    - DC
-  Generator:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  StorageUnit:
-    - PHS
-    - hydro
-  Store: []
-
-
-energy:
-  energy_totals_year: 2011
-  base_emissions_year: 1990
-  eurostat_report_year: 2016
-  emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
-
-biomass:
-  year: 2030
-  scenario: ENS_Med
-  classes:
-    solid biomass:
-      - Agricultural waste
-      - Fuelwood residues
-      - Secondary Forestry residues - woodchips
-      - Sawdust
-      - Residues from landscape care
-      - Municipal waste
-    not included:
-      - Sugar from sugar beet
-      - Rape seed
-      - "Sunflower, soya seed "
-      - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
-      - Miscanthus, switchgrass, RCG
-      - Willow
-      - Poplar
-      - FuelwoodRW
-      - C&P_RW
-    biogas:
-      - Manure solid, liquid
-      - Sludge
-
-
-solar_thermal:
-  clearsky_model: simple  # should be "simple" or "enhanced"?
-  orientation:
-    slope: 45.
-    azimuth: 180.
-
-# only relevant for foresight = myopic or perfect
-existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
-  threshold_capacity: 10
-  conventional_carriers:
-    - lignite
-    - coal
-    - oil
-    - uranium
-
-
-sector:
-  district_heating:
-    potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
-     # increase of today's district heating demand to potential maximum district heating share
-     # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
-    district_heating_loss: 0.15
-  bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
-  bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
-  transport_heating_deadband_upper: 20.
-  transport_heating_deadband_lower: 15.
-  ICE_lower_degree_factor: 0.375  #in per cent increase in fuel consumption per degree above deadband
-  ICE_upper_degree_factor: 1.6
-  EV_lower_degree_factor: 0.98
-  EV_upper_degree_factor: 0.63
-  bev_dsm: true #turns on EV battery
-  bev_availability: 0.5  #How many cars do smart charging
-  bev_energy: 0.05  #average battery size in MWh
-  bev_charge_efficiency: 0.9  #BEV (dis-)charging efficiency
-  bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
-  bev_charge_rate: 0.011 #3-phase charger with 11 kW
-  bev_avail_max: 0.95
-  bev_avail_mean: 0.8
-  v2g: true #allows feed-in to grid from EV battery
-  #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
-  transport_fuel_cell_efficiency: 0.5
-  transport_internal_combustion_efficiency: 0.3
-  agriculture_machinery_electric_share: 0
-  agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
-  agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
-  shipping_hydrogen_liquefaction: true # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
-  time_dep_hp_cop: true #time dependent heat pump coefficient of performance
-  heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
-   # conservatively high to cover hot water and space heating in poorly-insulated buildings
-  reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
-  # this can represent e.g. building renovation, building demolition, or if
-  # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
-  # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
-  retrofitting :  # co-optimises building renovation to reduce space heat demand
-    retro_endogen: false  # co-optimise space heat savings
-    cost_factor: 1.0   # weight costs for building renovation
-    interest_rate: 0.04  # for investment in building components
-    annualise_cost: true  # annualise the investment costs
-    tax_weighting: false   # weight costs depending on taxes in countries
-    construction_index: true   # weight costs depending on labour/material costs per country
-  tes: true
-  tes_tau: # 180 day time constant for centralised, 3 day for decentralised
-    decentral: 3
-    central: 180
-  boilers: true
-  oil_boilers: false
-  chp: true
-  micro_chp: false
-  solar_thermal: true
-  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
-  marginal_cost_storage: 0. #1e-4
-  methanation: true
-  helmeth: false
-  dac: true
-  co2_vent: false
-  SMR: true
-  co2_sequestration_potential: 200  #MtCO2/a sequestration potential for Europe
-  co2_sequestration_cost: 20   #EUR/tCO2 for sequestration of CO2
-  co2_network: false
-  cc_fraction: 0.9  # default fraction of CO2 captured with post-combustion capture
-  hydrogen_underground_storage: true
-  hydrogen_underground_storage_locations:
-    # - onshore  # more than 50 km from sea
-    - nearshore  # within 50 km of sea
-    # - offshore
-  use_fischer_tropsch_waste_heat: true
-  use_fuel_cell_waste_heat: true
-  electricity_distribution_grid: true
-  electricity_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  electricity_grid_connection: true  # only applies to onshore wind and utility PV
-  H2_network: true
-  gas_network: false
-  H2_retrofit: true  # if set to True existing gas pipes can be retrofitted to H2 pipes
-  # according to hydrogen backbone strategy (April, 2020) p.15
-  # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
-  # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
-  H2_retrofit_capacity_per_CH4: 0.6  # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
-  gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
-  gas_distribution_grid: true
-  gas_distribution_grid_cost_factor: 1.0  #multiplies cost in data/costs.csv
-  biomass_transport: false  # biomass transport between nodes
-  conventional_generation: # generator : carrier
-    OCGT: gas
-
-
-industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
-  H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
-  elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
-  MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
-  MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
-  MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
-  MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
-  NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
-  petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
-  HVC_primary_fraction: 0.45 # fraction of today's HVC produced via primary route
-  HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
-  HVC_chemical_recycling_fraction: 0.15 # fraction of today's HVC produced via chemical recycling
-  HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
-  MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
-  MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
-  chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
-  MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
-  MWh_H2_per_tCl: -0.9372  # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
-  methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
-  MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
-  MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
-  hotmaps_locate_missing: false
-  reference_year: 2015
-  # references:
-  # DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
-  # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
-
-costs:
-  lifetime: 25 #default lifetime
-  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
-  discountrate: 0.07
-  # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
-  USD2013_to_EUR2013: 0.7532
-
-  # Marginal and capital costs can be overwritten
-  # capital_cost:
-  #   onwind: 500
-  marginal_cost:
-    solar: 0.01
-    onwind: 0.015
-    offwind: 0.015
-    hydro: 0.
-    H2: 0.
-    battery: 0.
-
-  emission_prices: # only used with the option Ep (emission prices)
-    co2: 0.
-
-  lines:
-    length_factor: 1.25 #to estimate offwind connection costs
-
-
-solving:
-  #tmpdir: "path/to/tmp"
-  options:
-    formulation: kirchhoff
-    clip_p_max_pu: 1.e-2
-    load_shedding: false
-    noisy_costs: true
-    skip_iterations: true
-    track_iterations: false
-    min_iterations: 4
-    max_iterations: 6
-    keep_shadowprices:
-      - Bus
-      - Line
-      - Link
-      - Transformer
-      - GlobalConstraint
-      - Generator
-      - Store
-      - StorageUnit
-  solver:
-    name: gurobi
-    threads: 4
-    method: 2 # barrier
-    crossover: 0
-    BarConvTol: 1.e-4
-    Seed: 123
-    AggFill: 0
-    PreDual: 0
-    GURO_PAR_BARDENSETHRESH: 200
-    #FeasibilityTol: 1.e-6
-
-    #name: cplex
-    #threads: 4
-    #lpmethod: 4 # barrier
-    #solutiontype: 2 # non basic solution, ie no crossover
-    #barrier_convergetol: 1.e-5
-    #feasopt_tolerance: 1.e-6
-  mem: 126000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
-
-
-plotting:
-  map:
-    boundaries: [-11, 30, 34, 71]
-    color_geomap:
-      ocean: white
-      land: white
-  costs_max: 1000
-  costs_threshold: 1
-  energy_max: 20000
-  energy_min: -20000
-  energy_threshold: 50
-  vre_techs:
-    - onwind
-    - offwind-ac
-    - offwind-dc
-    - solar
-    - ror
-  renewable_storage_techs:
-    - PHS
-    - hydro
-  conv_techs:
-    - OCGT
-    - CCGT
-    - Nuclear
-    - Coal
-  storage_techs:
-    - hydro+PHS
-    - battery
-    - H2
-  load_carriers:
-    - AC load
-  AC_carriers:
-    - AC line
-    - AC transformer
-  link_carriers:
-    - DC line
-    - Converter AC-DC
-  heat_links:
-    - heat pump
-    - resistive heater
-    - CHP heat
-    - CHP electric
-    - gas boiler
-    - central heat pump
-    - central resistive heater
-    - central CHP heat
-    - central CHP electric
-    - central gas boiler
-  heat_generators:
-    - gas boiler
-    - central gas boiler
-    - solar thermal collector
-    - central solar thermal collector
-  tech_colors:
-    # wind
-    onwind: "#235ebc"
-    onshore wind: "#235ebc"
-    offwind: "#6895dd"
-    offshore wind: "#6895dd"
-    offwind-ac: "#6895dd"
-    offshore wind (AC): "#6895dd"
-    offwind-dc: "#74c6f2"
-    offshore wind (DC): "#74c6f2"
-    # water
-    hydro: '#298c81'
-    hydro reservoir: '#298c81'
-    ror: '#3dbfb0'
-    run of river: '#3dbfb0'
-    hydroelectricity: '#298c81'
-    PHS: '#51dbcc'
-    wave: '#a7d4cf'
-    # solar
-    solar: "#f9d002"
-    solar PV: "#f9d002"
-    solar thermal: '#ffbf2b'
-    solar rooftop: '#ffea80'
-    # gas
-    OCGT: '#e0986c'
-    OCGT marginal: '#e0986c'
-    OCGT-heat: '#e0986c'
-    gas boiler: '#db6a25'
-    gas boilers: '#db6a25'
-    gas boiler marginal: '#db6a25'
-    gas: '#e05b09'
-    natural gas: '#e05b09'
-    CCGT: '#a85522'
-    CCGT marginal: '#a85522'
-    gas for industry co2 to atmosphere: '#692e0a'
-    gas for industry co2 to stored: '#8a3400'
-    gas for industry: '#853403'
-    gas for industry CC: '#692e0a'
-    gas pipeline: '#ebbca0'
-    # oil
-    oil: '#c9c9c9'
-    oil boiler: '#adadad'
-    agriculture machinery oil: '#949494'
-    shipping oil: "#808080"
-    land transport oil: '#afafaf'
-    # nuclear
-    Nuclear: '#ff8c00'
-    Nuclear marginal: '#ff8c00'
-    nuclear: '#ff8c00'
-    uranium: '#ff8c00'
-    # coal
-    Coal: '#545454'
-    coal: '#545454'
-    Coal marginal: '#545454'
-    Lignite: '#826837'
-    lignite: '#826837'
-    Lignite marginal: '#826837'
-    # biomass
-    biogas: '#e3d37d'
-    solid biomass: '#baa741'
-    solid biomass transport: '#baa741'
-    solid biomass for industry: '#7a6d26'
-    solid biomass for industry CC: '#47411c'
-    solid biomass for industry co2 from atmosphere: '#736412'
-    solid biomass for industry co2 to stored: '#47411c'
-    # power transmission
-    lines: '#6c9459'
-    transmission lines: '#6c9459'
-    electricity distribution grid: '#97ad8c'
-    # electricity demand
-    Electric load: '#110d63'
-    electric demand: '#110d63'
-    electricity: '#110d63'
-    industry electricity: '#2d2a66'
-    industry new electricity: '#2d2a66'
-    agriculture electricity: '#494778'
-    # battery + EVs
-    battery: '#ace37f'
-    battery storage: '#ace37f'
-    home battery: '#80c944'
-    home battery storage: '#80c944'
-    BEV charger: '#baf238'
-    V2G: '#e5ffa8'
-    land transport EV: '#baf238'
-    Li ion: '#baf238'
-    # hot water storage
-    water tanks: '#e69487'
-    hot water storage: '#e69487'
-    hot water charging: '#e69487'
-    hot water discharging: '#e69487'
-    # heat demand
-    Heat load: '#cc1f1f'
-    heat: '#cc1f1f'
-    heat demand: '#cc1f1f'
-    rural heat: '#ff5c5c'
-    central heat: '#cc1f1f'
-    decentral heat: '#750606'
-    low-temperature heat for industry: '#8f2727'
-    process heat: '#ff0000'
-    agriculture heat: '#d9a5a5'
-    # heat supply
-    heat pumps: '#2fb537'
-    heat pump: '#2fb537'
-    air heat pump: '#36eb41'
-    ground heat pump: '#2fb537'
-    Ambient: '#98eb9d'
-    CHP: '#8a5751'
-    CHP CC: '#634643'
-    CHP heat: '#8a5751'
-    CHP electric: '#8a5751'
-    district heating: '#e8beac'
-    resistive heater: '#c78536'
-    retrofitting: '#8487e8'
-    building retrofitting: '#8487e8'
-    # hydrogen
-    H2 for industry: "#f073da"
-    H2 for shipping: "#ebaee0"
-    H2: '#bf13a0'
-    SMR: '#870c71'
-    SMR CC: '#4f1745'
-    H2 liquefaction: '#d647bd'
-    hydrogen storage: '#bf13a0'
-    land transport fuel cell: '#6b3161'
-    H2 pipeline: '#f081dc'
-    H2 Fuel Cell: '#c251ae'
-    H2 Electrolysis: '#ff29d9'
-    # syngas
-    Sabatier: '#9850ad'
-    methanation: '#c44ce6'
-    helmeth: '#e899ff'
-    # synfuels
-    Fischer-Tropsch: '#25c49a'
-    kerosene for aviation: '#a1ffe6'
-    naphtha for industry: '#57ebc4'
-    # co2
-    CC: '#9e132c'
-    CO2 sequestration: '#9e132c'
-    DAC: '#ff5270'
-    co2 stored: '#f2385a'
-    co2: '#f29dae'
-    co2 vent: '#ffd4dc'
-    CO2 pipeline: '#f5627f'
-    # emissions
-    process emissions CC: '#000000'
-    process emissions: '#222222'
-    process emissions to stored: '#444444'
-    process emissions to atmosphere: '#888888'
-    oil emissions: '#aaaaaa'
-    shipping oil emissions: "#555555"
-    land transport oil emissions: '#777777'
-    agriculture machinery oil emissions: '#333333'
-    # other
-    shipping: '#03a2ff'
-    power-to-heat: '#cc1f1f'
-    power-to-gas: '#c44ce6'
-    power-to-liquid: '#25c49a'
-    gas-to-power/heat: '#ee8340'

From 46413dd88c745b2a7d8c6436d758e47203799e0b Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:07:59 +0200
Subject: [PATCH 022/243] Added biomass to liquid process with an w/o CC

---
 scripts/prepare_sector_network.py | 34 +++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 7abdadff..3fec5446 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1859,6 +1859,40 @@ def add_biomass(n, costs):
             efficiency4=costs.at['solid biomass', 'CO2 intensity'] * costs.at['biomass CHP capture', 'capture_rate'],
             lifetime=costs.at[key, 'lifetime']
         )
+        
+    #Solid biomass to liquid fuel
+    if options["biomass"]["biomass_to_liquid"]
+        n.madd("Link",
+           spatial.biomass.nodes + " biomass to liquid",
+           bus0=spatial.biomass.nodes,
+           bus1="EU oil",
+           bus3="co2 atmosphere",
+           carrier="biomass to liquid",
+           lifetime=costs.at['BtL', 'lifetime'],
+           efficiency=costs.at['BtL', 'efficiency'],
+           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'],
+           p_nom_extendable=True,
+           capital_cost=costs.at['BtL', 'fixed'],
+           marginal_cost=costs.at['BtL', 'efficiency']*costs.loc["BtL", "VOM"]
+           )
+
+        #TODO: Update with energy penalty
+        n.madd("Link",
+               spatial.biomass.nodes + " biomass to liquid CC",
+               bus0=spatial.biomass.nodes,
+               bus1="EU oil",
+               bus2="co2 stored",
+               bus3="co2 atmosphere",
+               carrier="biomass to liquid",
+               lifetime=costs.at['BtL', 'lifetime'],
+               efficiency=costs.at['BtL', 'efficiency'],
+               efficiency2=costs.at['BtL', 'CO2 stored'] * costs.at['BtL', 'capture rate'],
+               efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'] * (1 - costs.at['BtL', 'capture rate']),
+               p_nom_extendable=True,
+               capital_cost=costs.at['BtL', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
+                   "BtL", "CO2 stored"],
+               marginal_cost=costs.at['BtL', 'efficiency'] * costs.loc["BtL", "VOM"]
+               )
 
 
 def add_industry(n, costs):

From 2d06d207be7b23a71cd64232542b4585506b4c75 Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:12:27 +0200
Subject: [PATCH 023/243] Added biomass_to_liquid option toggle in yaml

---
 config.default.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/config.default.yaml b/config.default.yaml
index b2fa5f6b..78a07e17 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -263,6 +263,7 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
+  biomass_to_liquid: true
 
 
 industry:

From 4bff921cf28f580c136701a7dec6c9c9e3c2cacb Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:13:18 +0200
Subject: [PATCH 024/243] Adapted biomass_to_liquid toggle reference

---
 scripts/prepare_sector_network.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 3fec5446..d3ff0e5d 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1861,7 +1861,7 @@ def add_biomass(n, costs):
         )
         
     #Solid biomass to liquid fuel
-    if options["biomass"]["biomass_to_liquid"]
+    if options["biomass_to_liquid"]
         n.madd("Link",
            spatial.biomass.nodes + " biomass to liquid",
            bus0=spatial.biomass.nodes,

From abba2a4f3ee6355fceab1d513f9b3d7c752b7077 Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:22:58 +0200
Subject: [PATCH 025/243] Added BioSNG process with and w/o CC

---
 scripts/prepare_sector_network.py | 34 +++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 7abdadff..ddc5b151 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1860,6 +1860,40 @@ def add_biomass(n, costs):
             lifetime=costs.at[key, 'lifetime']
         )
 
+    #BioSNG from solid biomass
+    if options["biosng"]:
+        n.madd("Link",
+           spatial.biomass.nodes + " solid biomass to gas",
+           bus0=spatial.biomass.nodes,
+           bus1=spatial.gas.nodes,
+           bus3="co2 atmosphere",
+           carrier="BioSNG",
+           lifetime=costs.at['BioSNG', 'lifetime'],
+           efficiency=costs.at['BioSNG', 'efficiency'],
+           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'],
+           p_nom_extendable=True,
+           capital_cost=costs.at['BioSNG', 'fixed'],
+           marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
+           )
+
+        #TODO: Update with energy penalty for CC
+        n.madd("Link",
+           spatial.biomass.nodes + " solid biomass to gas CC",
+           bus0=spatial.biomass.nodes,
+           bus1=spatial.gas.nodes,
+           bus2=spatial.co2.nodes,
+           bus3="co2 atmosphere",
+           carrier="BioSNG",
+           lifetime=costs.at['BioSNG', 'lifetime'],
+           efficiency=costs.at['BioSNG', 'efficiency'],
+           efficiency2=costs.at['BioSNG', 'CO2 stored'] * costs.at['BioSNG', 'capture rate'],
+           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'] * (1 - costs.at['BioSNG', 'capture rate']),
+           p_nom_extendable=True,
+           capital_cost=costs.at['BioSNG', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
+               "BioSNG", "CO2 stored"],
+           marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
+           )
+
 
 def add_industry(n, costs):
 

From 603d1597c4702dd3635630dfb65038049477c212 Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:24:04 +0200
Subject: [PATCH 026/243] Added BioSNG toggle to yaml

---
 config.default.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/config.default.yaml b/config.default.yaml
index b2fa5f6b..1baf392d 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -263,6 +263,7 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
+  biosng: true
 
 
 industry:

From 00197232629ea29877d58ead96a6db27b61f6753 Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:26:51 +0200
Subject: [PATCH 027/243] Updated spatial nodes for biomass to liquid

---
 scripts/prepare_sector_network.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index d3ff0e5d..8eea6a19 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1865,7 +1865,7 @@ def add_biomass(n, costs):
         n.madd("Link",
            spatial.biomass.nodes + " biomass to liquid",
            bus0=spatial.biomass.nodes,
-           bus1="EU oil",
+           bus1=spatial.oil.nodes,
            bus3="co2 atmosphere",
            carrier="biomass to liquid",
            lifetime=costs.at['BtL', 'lifetime'],
@@ -1880,8 +1880,8 @@ def add_biomass(n, costs):
         n.madd("Link",
                spatial.biomass.nodes + " biomass to liquid CC",
                bus0=spatial.biomass.nodes,
-               bus1="EU oil",
-               bus2="co2 stored",
+               bus1=spatial.oil.nodes,
+               bus2=spatial.co2.nodes,
                bus3="co2 atmosphere",
                carrier="biomass to liquid",
                lifetime=costs.at['BtL', 'lifetime'],

From 5b61d2f77704f348a9f6f8634609ea83643f490f Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:38:01 +0200
Subject: [PATCH 028/243] Added pellet boiler process

---
 scripts/prepare_sector_network.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 7abdadff..8bca2770 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1521,6 +1521,22 @@ def add_heat(n, costs):
                 capital_cost=costs.at[key, 'efficiency'] * costs.at[key, 'fixed'],
                 lifetime=costs.at[key, 'lifetime']
             )
+            
+            if options["pellet_boiler"]:
+                #TODO: Add surcharge for pellets
+                n.madd("Link",
+                    nodes[name] + " biomass boiler",
+                    p_nom_extendable=True,
+                    bus0=spatial.biomass.nodes,
+                    bus1=nodes[name] + f" {name} heat",
+                    bus2="co2 atmosphere",
+                    carrier=name + " biomass boiler",
+                    efficiency=costs.at['biomass boiler', 'efficiency'],
+                    efficiency2=costs.at['solid biomass', 'CO2 intensity']-costs.at['solid biomass', 'CO2 intensity'],
+                    capital_cost=costs.at['biomass boiler', 'efficiency'] * costs.at['biomass boiler', 'fixed'],
+                    lifetime=costs.at['biomass boiler', 'lifetime']
+                )
+
 
         if options["solar_thermal"]:
 

From 082d655a6c34a19ee7f3c8bf570519ed3939d363 Mon Sep 17 00:00:00 2001
From: Markus Millinger <50738187+millingermarkus@users.noreply.github.com>
Date: Mon, 1 Aug 2022 13:38:47 +0200
Subject: [PATCH 029/243] Added pellet_boiler toggle option in yaml

---
 config.default.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/config.default.yaml b/config.default.yaml
index b2fa5f6b..41838431 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -263,6 +263,7 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
+  pellet_boiler: true
 
 
 industry:

From 46310f0a30b887b74e0649f861d52f31107f67de Mon Sep 17 00:00:00 2001
From: millingermarkus <markus.millinger@chalmers.se>
Date: Mon, 1 Aug 2022 15:00:27 +0200
Subject: [PATCH 030/243] Minor correction to prepare_sector_network

---
 scripts/prepare_sector_network.py | 38 ++++++++++++++++---------------
 1 file changed, 20 insertions(+), 18 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 8eea6a19..15e30a65 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1861,9 +1861,10 @@ def add_biomass(n, costs):
         )
         
     #Solid biomass to liquid fuel
-    if options["biomass_to_liquid"]
+    if options["biomass_to_liquid"]:
         n.madd("Link",
-           spatial.biomass.nodes + " biomass to liquid",
+           spatial.biomass.nodes,
+           suffix=" biomass to liquid",
            bus0=spatial.biomass.nodes,
            bus1=spatial.oil.nodes,
            bus3="co2 atmosphere",
@@ -1874,25 +1875,26 @@ def add_biomass(n, costs):
            p_nom_extendable=True,
            capital_cost=costs.at['BtL', 'fixed'],
            marginal_cost=costs.at['BtL', 'efficiency']*costs.loc["BtL", "VOM"]
-           )
+        )
 
         #TODO: Update with energy penalty
         n.madd("Link",
-               spatial.biomass.nodes + " biomass to liquid CC",
-               bus0=spatial.biomass.nodes,
-               bus1=spatial.oil.nodes,
-               bus2=spatial.co2.nodes,
-               bus3="co2 atmosphere",
-               carrier="biomass to liquid",
-               lifetime=costs.at['BtL', 'lifetime'],
-               efficiency=costs.at['BtL', 'efficiency'],
-               efficiency2=costs.at['BtL', 'CO2 stored'] * costs.at['BtL', 'capture rate'],
-               efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'] * (1 - costs.at['BtL', 'capture rate']),
-               p_nom_extendable=True,
-               capital_cost=costs.at['BtL', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
-                   "BtL", "CO2 stored"],
-               marginal_cost=costs.at['BtL', 'efficiency'] * costs.loc["BtL", "VOM"]
-               )
+           spatial.biomass.nodes,
+           suffix=" biomass to liquid CC",
+           bus0=spatial.biomass.nodes,
+           bus1=spatial.oil.nodes,
+           bus2=spatial.co2.nodes,
+           bus3="co2 atmosphere",
+           carrier="biomass to liquid",
+           lifetime=costs.at['BtL', 'lifetime'],
+           efficiency=costs.at['BtL', 'efficiency'],
+           efficiency2=costs.at['BtL', 'CO2 stored'] * costs.at['BtL', 'capture rate'],
+           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'] * (1 - costs.at['BtL', 'capture rate']),
+           p_nom_extendable=True,
+           capital_cost=costs.at['BtL', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
+               "BtL", "CO2 stored"],
+           marginal_cost=costs.at['BtL', 'efficiency'] * costs.loc["BtL", "VOM"]
+        )
 
 
 def add_industry(n, costs):

From 4d1dc2db8b0bea8afe429bf4fb04661bf6aefe51 Mon Sep 17 00:00:00 2001
From: millingermarkus <markus.millinger@chalmers.se>
Date: Mon, 1 Aug 2022 15:04:45 +0200
Subject: [PATCH 031/243] Added tech color for biomass to liquid

---
 config.default.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/config.default.yaml b/config.default.yaml
index 78a07e17..4085e719 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -509,6 +509,7 @@ plotting:
     solid biomass for industry CC: '#47411c'
     solid biomass for industry co2 from atmosphere: '#736412'
     solid biomass for industry co2 to stored: '#47411c'
+    biomass to liquid: '#32CD32'
     # power transmission
     lines: '#6c9459'
     transmission lines: '#6c9459'

From 6211823514c943881aad702cb20a3bc4484e3a85 Mon Sep 17 00:00:00 2001
From: millingermarkus <markus.millinger@chalmers.se>
Date: Mon, 1 Aug 2022 15:13:59 +0200
Subject: [PATCH 032/243] Added color for BioSNG and minor adaptations

---
 config.default.yaml               |  1 +
 scripts/prepare_sector_network.py | 56 ++++++++++++++++---------------
 2 files changed, 30 insertions(+), 27 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 1baf392d..f99b1b7a 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -509,6 +509,7 @@ plotting:
     solid biomass for industry CC: '#47411c'
     solid biomass for industry co2 from atmosphere: '#736412'
     solid biomass for industry co2 to stored: '#47411c'
+    BioSNG: '#123456'
     # power transmission
     lines: '#6c9459'
     transmission lines: '#6c9459'
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index ddc5b151..9b05ed83 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1863,36 +1863,38 @@ def add_biomass(n, costs):
     #BioSNG from solid biomass
     if options["biosng"]:
         n.madd("Link",
-           spatial.biomass.nodes + " solid biomass to gas",
-           bus0=spatial.biomass.nodes,
-           bus1=spatial.gas.nodes,
-           bus3="co2 atmosphere",
-           carrier="BioSNG",
-           lifetime=costs.at['BioSNG', 'lifetime'],
-           efficiency=costs.at['BioSNG', 'efficiency'],
-           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'],
-           p_nom_extendable=True,
-           capital_cost=costs.at['BioSNG', 'fixed'],
-           marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
-           )
+            spatial.biomass.nodes,
+            suffix=" solid biomass to gas",
+            bus0=spatial.biomass.nodes,
+            bus1=spatial.gas.nodes,
+            bus3="co2 atmosphere",
+            carrier="BioSNG",
+            lifetime=costs.at['BioSNG', 'lifetime'],
+            efficiency=costs.at['BioSNG', 'efficiency'],
+            efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'],
+            p_nom_extendable=True,
+            capital_cost=costs.at['BioSNG', 'fixed'],
+            marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
+        )
 
         #TODO: Update with energy penalty for CC
         n.madd("Link",
-           spatial.biomass.nodes + " solid biomass to gas CC",
-           bus0=spatial.biomass.nodes,
-           bus1=spatial.gas.nodes,
-           bus2=spatial.co2.nodes,
-           bus3="co2 atmosphere",
-           carrier="BioSNG",
-           lifetime=costs.at['BioSNG', 'lifetime'],
-           efficiency=costs.at['BioSNG', 'efficiency'],
-           efficiency2=costs.at['BioSNG', 'CO2 stored'] * costs.at['BioSNG', 'capture rate'],
-           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'] * (1 - costs.at['BioSNG', 'capture rate']),
-           p_nom_extendable=True,
-           capital_cost=costs.at['BioSNG', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
-               "BioSNG", "CO2 stored"],
-           marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
-           )
+            spatial.biomass.nodes,
+            suffix=" solid biomass to gas CC",
+            bus0=spatial.biomass.nodes,
+            bus1=spatial.gas.nodes,
+            bus2=spatial.co2.nodes,
+            bus3="co2 atmosphere",
+            carrier="BioSNG",
+            lifetime=costs.at['BioSNG', 'lifetime'],
+            efficiency=costs.at['BioSNG', 'efficiency'],
+            efficiency2=costs.at['BioSNG', 'CO2 stored'] * costs.at['BioSNG', 'capture rate'],
+            efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'] * (1 - costs.at['BioSNG', 'capture rate']),
+            p_nom_extendable=True,
+            capital_cost=costs.at['BioSNG', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
+                "BioSNG", "CO2 stored"],
+            marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
+        )
 
 
 def add_industry(n, costs):

From f10628930ee138d90e63743f0487baf9134be546 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 1 Aug 2022 15:21:11 +0200
Subject: [PATCH 033/243] move snakemake dependencies out for co2limit cb
 option to work

---
 Snakefile                         |  9 ++++++---
 scripts/build_energy_totals.py    | 24 +++++++++++++-----------
 scripts/prepare_sector_network.py | 28 ++++++++++++++++------------
 3 files changed, 35 insertions(+), 26 deletions(-)

diff --git a/Snakefile b/Snakefile
index 7430c375..0b86042e 100644
--- a/Snakefile
+++ b/Snakefile
@@ -442,14 +442,14 @@ rule build_population_weighted_energy_totals:
 
 
 rule build_transport_demand:
-    input: 
+    input:
         clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
         pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
         transport_data='resources/transport_data.csv',
         traffic_data_KFZ="data/emobility/KFZ__count",
         traffic_data_Pkw="data/emobility/Pkw__count",
         temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
-    output: 
+    output:
         transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
         transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
         avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
@@ -464,12 +464,14 @@ rule prepare_sector_network:
         overrides="data/override_component_attrs",
         network=pypsaeur('networks/elec_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
         energy_totals_name='resources/energy_totals.csv',
+        eurostat=input_eurostat,
         pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
         transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
         transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
         avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
         dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv",
         co2_totals_name='resources/co2_totals.csv',
+        co2="data/eea/UNFCCC_v23.csv",
         biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
         heat_profile="data/heat_load_profile_BDEW.csv",
         costs=CDIR + "costs_{planning_horizons}.csv",
@@ -568,7 +570,8 @@ rule plot_summary:
     input:
         costs=SDIR + '/csvs/costs.csv',
         energy=SDIR + '/csvs/energy.csv',
-        balances=SDIR + '/csvs/supply_energy.csv'
+        balances=SDIR + '/csvs/supply_energy.csv',
+        clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
     output:
         costs=SDIR + '/graphs/costs.pdf',
         energy=SDIR + '/graphs/energy.pdf',
diff --git a/scripts/build_energy_totals.py b/scripts/build_energy_totals.py
index 3f376b0c..575070ad 100644
--- a/scripts/build_energy_totals.py
+++ b/scripts/build_energy_totals.py
@@ -127,17 +127,16 @@ to_ipcc = {
 }
 
 
-def build_eurostat(countries, year):
+def build_eurostat(input_eurostat, countries, report_year,  year):
     """Return multi-index for all countries' energy data in TWh/a."""
 
-    report_year = snakemake.config["energy"]["eurostat_report_year"]
     filenames = {
         2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
         2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx"
     }
 
     dfs = pd.read_excel(
-        snakemake.input.eurostat + filenames[report_year],
+        input_eurostat + filenames[report_year],
         sheet_name=None,
         skiprows=1,
         index_col=list(range(4)),
@@ -563,18 +562,18 @@ def build_energy_totals(countries, eurostat, swiss, idees):
     return df
 
 
-def build_eea_co2(year=1990):
+def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
 
     # https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
     # downloaded 201228 (modified by EEA last on 201221)
-    df = pd.read_csv(snakemake.input.co2, encoding="latin-1")
+    df = pd.read_csv(input_co2, encoding="latin-1")
 
     df.replace(dict(Year="1985-1987"), 1986, inplace=True)
     df.Year = df.Year.astype(int)
     index_col = ["Country_code", "Pollutant_name", "Year", "Sector_name"]
     df = df.set_index(index_col).sort_index()
 
-    emissions_scope = snakemake.config["energy"]["emissions"]
+    emissions_scope = emissions_scope
 
     cts = ["CH", "EUA", "NO"] + eu28_eea
 
@@ -611,9 +610,9 @@ def build_eea_co2(year=1990):
     return emissions / 1e3
 
 
-def build_eurostat_co2(countries, year=1990):
+def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
 
-    eurostat = build_eurostat(countries, year)
+    eurostat = build_eurostat(input_eurostat, countries, report_year, year)
 
     specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
 
@@ -702,7 +701,9 @@ if __name__ == "__main__":
     idees_countries = countries.intersection(eu28)
 
     data_year = config["energy_totals_year"]
-    eurostat = build_eurostat(countries, data_year)
+    report_year = snakemake.config["energy"]["eurostat_report_year"]
+    input_eurostat = snakemake.input.eurostat
+    eurostat = build_eurostat(input_eurostat, countries, report_year, data_year)
     swiss = build_swiss(data_year)
     idees = build_idees(idees_countries, data_year)
 
@@ -710,8 +711,9 @@ if __name__ == "__main__":
     energy.to_csv(snakemake.output.energy_name)
 
     base_year_emissions = config["base_emissions_year"]
-    eea_co2 = build_eea_co2(base_year_emissions)
-    eurostat_co2 = build_eurostat_co2(countries, base_year_emissions)
+    emissions_scope = snakemake.config["energy"]["emissions"]
+    eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
+    eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, base_year_emissions)
 
     co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
     co2.to_csv(snakemake.output.co2_name)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 7abdadff..d77e2201 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -158,21 +158,24 @@ def get(item, investment_year=None):
         return item
 
 
-def co2_emissions_year(countries, opts, year):
+def co2_emissions_year(opts, year):
     """
     Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
     """
-
-    eea_co2 = build_eea_co2(year)
+    emissions_scope = snakemake.config["energy"]["emissions"]
+    eea_co2 = build_eea_co2(snakemake.input.co2, year, emissions_scope)
+    input_eurostat = snakemake.input.eurostat
 
     # TODO: read Eurostat data from year > 2014
     # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
+    report_year = snakemake.config["energy"]["eurostat_report_year"]
+    countries =  pd.Index(pop_layout.ct.unique())
     if year > 2014:
-        eurostat_co2 = build_eurostat_co2(year=2014)
+        eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year=2014)
     else:
-        eurostat_co2 = build_eurostat_co2(year)
+        eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year)
 
-    co2_totals = build_co2_totals(eea_co2, eurostat_co2)
+    co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2)
 
     sectors = emission_sectors_from_opts(opts)
 
@@ -202,10 +205,10 @@ def build_carbon_budget(o, fn):
 
     countries = n.buses.country.dropna().unique()
 
-    e_1990 = co2_emissions_year(countries, opts, year=1990)
+    e_1990 = co2_emissions_year( opts, year=1990)
 
     #emissions at the beginning of the path (last year available 2018)
-    e_0 = co2_emissions_year(countries, opts, year=2018)
+    e_0 = co2_emissions_year(opts, year=2018)
 
     planning_horizons = snakemake.config['scenario']['planning_horizons']
     t_0 = planning_horizons[0]
@@ -233,8 +236,9 @@ def build_carbon_budget(o, fn):
         co2_cap = pd.Series({t: exponential_decay(t) for t in planning_horizons}, name=o)
 
     # TODO log in Snakefile
-    if not os.path.exists(fn):
-        os.makedirs(fn)
+    csvs_folder = fn.rsplit("/", 1)[0]
+    if not os.path.exists(csvs_folder):
+        os.makedirs(csvs_folder)
     co2_cap.to_csv(fn, float_format='%.3f')
 
 
@@ -2333,7 +2337,7 @@ if __name__ == "__main__":
             opts="",
             clusters="37",
             lv=1.5,
-            sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
+            sector_opts='cb40ex0-365H-T-H-B-I-A-solar+p3-dist1',
             planning_horizons="2020",
         )
 
@@ -2439,7 +2443,7 @@ if __name__ == "__main__":
         if not os.path.exists(fn):
             build_carbon_budget(o, fn)
         co2_cap = pd.read_csv(fn, index_col=0).squeeze()
-        limit = co2_cap[investment_year]
+        limit = co2_cap.loc[investment_year]
         break
     for o in opts:
         if not "Co2L" in o: continue

From 18f4eac35185bfa594edbe9066a9668188081909 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 1 Aug 2022 15:24:07 +0200
Subject: [PATCH 034/243] adjust script to new grouping years

---
 scripts/add_existing_baseyear.py | 98 +++++++++++++++++++-------------
 1 file changed, 59 insertions(+), 39 deletions(-)

diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 11b8d49b..34e80849 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -193,24 +193,33 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
 
     for grouping_year, generator in df.index:
 
+
         # capacity is the capacity in MW at each node for this
         capacity = df.loc[grouping_year, generator]
         capacity = capacity[~capacity.isna()]
         capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
-
+        suffix = '-ac' if generator == 'offwind' else ''
+        name_suffix = f' {generator}{suffix}-{baseyear}'
+        asset_i = capacity.index + name_suffix
         if generator in ['solar', 'onwind', 'offwind']:
 
-            suffix = '-ac' if generator == 'offwind' else ''
-            name_suffix = f' {generator}{suffix}-{baseyear}'
-
             # to consider electricity grid connection costs or a split between
             # solar utility and rooftop as well, rather take cost assumptions
             # from existing network than from the cost database
             capital_cost = n.generators.loc[n.generators.carrier==generator+suffix, "capital_cost"].mean()
 
+            # check if assets are already in network (e.g. for 2020)
+            already_build = n.generators.index.intersection(asset_i)
+            new_build = asset_i.difference(n.generators.index)
+
+            # this is for the year 2020
+            if not already_build.empty:
+                n.generators.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
+            new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
+
             if 'm' in snakemake.wildcards.clusters:
 
-                for ind in capacity.index:
+                for ind in new_capacity.index:
 
                     # existing capacities are split evenly among regions in every country
                     inv_ind = [i for i in inv_busmap[ind]]
@@ -225,7 +234,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
                         [i + name_suffix for i in inv_ind],
                         bus=ind,
                         carrier=generator,
-                        p_nom=capacity[ind] / len(inv_ind), # split among regions in a country
+                        p_nom=new_capacity[ind] / len(inv_ind), # split among regions in a country
                         marginal_cost=costs.at[generator,'VOM'],
                         capital_cost=capital_cost,
                         efficiency=costs.at[generator, 'efficiency'],
@@ -238,43 +247,54 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
 
                 p_max_pu = n.generators_t.p_max_pu[capacity.index + name_suffix]
 
-                n.madd("Generator",
-                    capacity.index,
-                    suffix=' ' + generator +"-"+ str(grouping_year),
-                    bus=capacity.index,
-                    carrier=generator,
-                    p_nom=capacity,
-                    marginal_cost=costs.at[generator, 'VOM'],
-                    capital_cost=capital_cost,
-                    efficiency=costs.at[generator, 'efficiency'],
-                    p_max_pu=p_max_pu.rename(columns=n.generators.bus),
-                    build_year=grouping_year,
-                    lifetime=costs.at[generator, 'lifetime']
-                )
+                if not new_build.empty:
+                    n.madd("Generator",
+                        new_capacity.index,
+                        suffix=' ' + generator + name_suffix,
+                        bus=new_capacity.index,
+                        carrier=generator,
+                        p_nom=new_capacity,
+                        marginal_cost=costs.at[generator, 'VOM'],
+                        capital_cost=capital_cost,
+                        efficiency=costs.at[generator, 'efficiency'],
+                        p_max_pu=p_max_pu.rename(columns=n.generators.bus),
+                        build_year=grouping_year,
+                        lifetime=costs.at[generator, 'lifetime']
+                    )
 
         else:
             bus0 = vars(spatial)[carrier[generator]].nodes
             if "EU" not in vars(spatial)[carrier[generator]].locations:
                 bus0 = bus0.intersection(capacity.index + " gas")
 
-            n.madd("Link",
-                capacity.index,
-                suffix= " " + generator +"-" + str(grouping_year),
-                bus0=bus0,
-                bus1=capacity.index,
-                bus2="co2 atmosphere",
-                carrier=generator,
-                marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
-                capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
-                p_nom=capacity / costs.at[generator, 'efficiency'],
-                efficiency=costs.at[generator, 'efficiency'],
-                efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
-                build_year=grouping_year,
-                lifetime=costs.at[generator, 'lifetime']
-            )
+            already_build = n.links.index.intersection(asset_i)
+            new_build = asset_i.difference(n.links.index)
+
+            # this is for the year 2020
+            if not already_build.empty:
+                n.links.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
+
+            if not new_build.empty:
+                new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
+
+                n.madd("Link",
+                    new_capacity.index,
+                    suffix= name_suffix,
+                    bus0=bus0,
+                    bus1=new_capacity.index,
+                    bus2="co2 atmosphere",
+                    carrier=generator,
+                    marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
+                    capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
+                    p_nom=capacity / costs.at[generator, 'efficiency'],
+                    efficiency=costs.at[generator, 'efficiency'],
+                    efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
+                    build_year=grouping_year,
+                    lifetime=costs.at[generator, 'lifetime']
+                )
 
 
-def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
+def add_heating_capacities_installed_before_baseyear(n, baseyear, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
     """
     Parameters
     ----------
@@ -294,7 +314,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
     # https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
     # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
     # TODO start from original file
-
+    grouping_years = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
     # retrieve existing heating capacities
     techs = [
         'gas boiler',
@@ -454,10 +474,10 @@ if __name__ == "__main__":
         snakemake = mock_snakemake(
             'add_existing_baseyear',
             simpl='',
-            clusters="37",
+            clusters="45",
             lv=1.0,
             opts='',
-            sector_opts='168H-T-H-B-I-solar+p3-dist1',
+            sector_opts='cb40ex0-365H-T-H-B-I-A-solar+p3-dist1',
             planning_horizons=2020,
         )
 
@@ -493,6 +513,6 @@ if __name__ == "__main__":
         ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
         gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
         default_lifetime = snakemake.config['costs']['lifetime']
-        add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
+        add_heating_capacities_installed_before_baseyear(n, baseyear, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
 
     n.export_to_netcdf(snakemake.output[0])

From 65aaa3d188a5d8cb6055d35b3593fcd1824d6111 Mon Sep 17 00:00:00 2001
From: millingermarkus <markus.millinger@chalmers.se>
Date: Mon, 1 Aug 2022 15:28:53 +0200
Subject: [PATCH 035/243] Added tech color for biomass boiler and minor
 adaptations to biomass boiler process

---
 config.default.yaml               | 3 ++-
 scripts/prepare_sector_network.py | 6 +++---
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 41838431..4cadbef3 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -263,7 +263,7 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
-  pellet_boiler: true
+  biomass_boiler: true
 
 
 industry:
@@ -509,6 +509,7 @@ plotting:
     solid biomass for industry CC: '#47411c'
     solid biomass for industry co2 from atmosphere: '#736412'
     solid biomass for industry co2 to stored: '#47411c'
+    biomass boiler: '#8A9A5B'
     # power transmission
     lines: '#6c9459'
     transmission lines: '#6c9459'
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 8bca2770..386b6aaa 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1522,12 +1522,12 @@ def add_heat(n, costs):
                 lifetime=costs.at[key, 'lifetime']
             )
             
-            if options["pellet_boiler"]:
+            if options["biomass_boiler"] and name not in ["urban central"]:
                 #TODO: Add surcharge for pellets
                 n.madd("Link",
-                    nodes[name] + " biomass boiler",
+                    nodes[name] + f" {name} biomass boiler",
                     p_nom_extendable=True,
-                    bus0=spatial.biomass.nodes,
+                    bus0=spatial.biomass.df.loc[nodes[name], "nodes"].values,
                     bus1=nodes[name] + f" {name} heat",
                     bus2="co2 atmosphere",
                     carrier=name + " biomass boiler",

From f683a93f0811641b98269c71e5bd6a46ebd3ead0 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 1 Aug 2022 16:04:14 +0200
Subject: [PATCH 036/243] adjust plot_summary to work with myopic, remove
 update config

---
 Snakefile                         |  3 ++-
 scripts/plot_summary.py           | 14 +++++++-------
 scripts/prepare_sector_network.py |  8 +++-----
 3 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/Snakefile b/Snakefile
index 0b86042e..6db0bca0 100644
--- a/Snakefile
+++ b/Snakefile
@@ -571,7 +571,8 @@ rule plot_summary:
         costs=SDIR + '/csvs/costs.csv',
         energy=SDIR + '/csvs/energy.csv',
         balances=SDIR + '/csvs/supply_energy.csv',
-        clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
+        eurostat=input_eurostat,
+        country_codes='data/Country_codes.csv',
     output:
         costs=SDIR + '/graphs/costs.pdf',
         energy=SDIR + '/graphs/energy.pdf',
diff --git a/scripts/plot_summary.py b/scripts/plot_summary.py
index 7ebfde24..091957d5 100644
--- a/scripts/plot_summary.py
+++ b/scripts/plot_summary.py
@@ -202,7 +202,7 @@ def plot_energy():
     new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
 
     new_columns = df.columns.sort_values()
-    
+
     fig, ax = plt.subplots(figsize=(12,8))
 
     print(df.loc[new_index, new_columns])
@@ -363,7 +363,7 @@ def historical_emissions(cts):
 
 
 
-def plot_carbon_budget_distribution():
+def plot_carbon_budget_distribution(input_eurostat):
     """
     Plot historical carbon emissions in the EU and decarbonization path
     """
@@ -385,9 +385,10 @@ def plot_carbon_budget_distribution():
     ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
 
     path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
-    countries=pd.read_csv(path_cb + 'countries.csv',  index_col=1)
+    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
+    countries=pd.read_csv(snakemake.input.country_codes, index_col=1)
     cts=countries.index.to_list()
-    e_1990 = co2_emissions_year(cts, opts, year=1990)
+    e_1990 = co2_emissions_year(cts, input_eurostat, opts, year=1990)
     CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',
                         index_col=0)
 
@@ -438,8 +439,7 @@ if __name__ == "__main__":
     if 'snakemake' not in globals():
         from helper import mock_snakemake
         snakemake = mock_snakemake('plot_summary')
-    
-    update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
+
 
     n_header = 4
 
@@ -453,4 +453,4 @@ if __name__ == "__main__":
         opts=sector_opts.split('-')
         for o in opts:
             if "cb" in o:
-                plot_carbon_budget_distribution()
+                plot_carbon_budget_distribution(snakemake.input.eurostat)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index d77e2201..5d1594fc 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -158,18 +158,16 @@ def get(item, investment_year=None):
         return item
 
 
-def co2_emissions_year(opts, year):
+def co2_emissions_year(countries, input_eurostat, opts, year):
     """
     Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
     """
     emissions_scope = snakemake.config["energy"]["emissions"]
     eea_co2 = build_eea_co2(snakemake.input.co2, year, emissions_scope)
-    input_eurostat = snakemake.input.eurostat
 
     # TODO: read Eurostat data from year > 2014
     # this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
     report_year = snakemake.config["energy"]["eurostat_report_year"]
-    countries =  pd.Index(pop_layout.ct.unique())
     if year > 2014:
         eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year=2014)
     else:
@@ -205,10 +203,10 @@ def build_carbon_budget(o, fn):
 
     countries = n.buses.country.dropna().unique()
 
-    e_1990 = co2_emissions_year( opts, year=1990)
+    e_1990 = co2_emissions_year(countries, snakemake.input.eurostat, opts, year=1990)
 
     #emissions at the beginning of the path (last year available 2018)
-    e_0 = co2_emissions_year(opts, year=2018)
+    e_0 = co2_emissions_year(countries, snakemake.input.eurostat, opts, year=2018)
 
     planning_horizons = snakemake.config['scenario']['planning_horizons']
     t_0 = planning_horizons[0]

From a11f7bc24500c4e3a16ef7adeae6ca46b3f0ea58 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Tue, 2 Aug 2022 08:47:46 +0200
Subject: [PATCH 037/243] remove not needed pop_layout from plot_summary

---
 scripts/plot_summary.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/scripts/plot_summary.py b/scripts/plot_summary.py
index 091957d5..940bbb2e 100644
--- a/scripts/plot_summary.py
+++ b/scripts/plot_summary.py
@@ -385,9 +385,8 @@ def plot_carbon_budget_distribution(input_eurostat):
     ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
 
     path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
-    pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
-    countries=pd.read_csv(snakemake.input.country_codes, index_col=1)
-    cts=countries.index.to_list()
+    countries = pd.read_csv(snakemake.input.country_codes, index_col=1)
+    cts = countries.index.to_list()
     e_1990 = co2_emissions_year(cts, input_eurostat, opts, year=1990)
     CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',
                         index_col=0)

From 339ccc602697f6196681bb1b3fa63a4a26720f40 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Tue, 2 Aug 2022 08:52:48 +0200
Subject: [PATCH 038/243] remove snakemake dependencies

---
 scripts/prepare_sector_network.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 5d1594fc..1afcbde9 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -158,7 +158,7 @@ def get(item, investment_year=None):
         return item
 
 
-def co2_emissions_year(countries, input_eurostat, opts, year):
+def co2_emissions_year(countries, input_eurostat, opts, emissions_scope, report_year, year):
     """
     Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
     """
@@ -186,7 +186,7 @@ def co2_emissions_year(countries, input_eurostat, opts, year):
 
 
 # TODO: move to own rule with sector-opts wildcard?
-def build_carbon_budget(o, fn):
+def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year):
     """
     Distribute carbon budget following beta or exponential transition path.
     """
@@ -203,10 +203,12 @@ def build_carbon_budget(o, fn):
 
     countries = n.buses.country.dropna().unique()
 
-    e_1990 = co2_emissions_year(countries, snakemake.input.eurostat, opts, year=1990)
+    e_1990 = co2_emissions_year(countries, input_eurostat, opts, emissions_scope,
+                                report_year, year=1990)
 
     #emissions at the beginning of the path (last year available 2018)
-    e_0 = co2_emissions_year(countries, snakemake.input.eurostat, opts, year=2018)
+    e_0 = co2_emissions_year(countries, input_eurostat, opts, emissions_scope,
+                             report_year,year=2018)
 
     planning_horizons = snakemake.config['scenario']['planning_horizons']
     t_0 = planning_horizons[0]
@@ -2439,7 +2441,9 @@ if __name__ == "__main__":
         limit_type = "carbon budget"
         fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
         if not os.path.exists(fn):
-            build_carbon_budget(o, fn)
+            emissions_scope = snakemake.config["energy"]["emissions"]
+            report_year = snakemake.config["energy"]["eurostat_report_year"]
+            build_carbon_budget(o, snakemake.input.eurostat, fn, emissions_scope, report_year)
         co2_cap = pd.read_csv(fn, index_col=0).squeeze()
         limit = co2_cap.loc[investment_year]
         break

From 7c3b4b6664a2b6fb5137725e882ec49ab9aaa6d0 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Tue, 2 Aug 2022 09:18:06 +0200
Subject: [PATCH 039/243] split grouping years for power and heat techs

---
 config.default.yaml              |  3 ++-
 scripts/add_existing_baseyear.py | 12 +++++++-----
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index b2fa5f6b..d95e642d 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -134,7 +134,8 @@ solar_thermal:
 
 # only relevant for foresight = myopic or perfect
 existing_capacities:
-  grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
+  grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
+  grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
   threshold_capacity: 10
   conventional_carriers:
     - lignite
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 34e80849..5a2c226e 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -294,7 +294,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
                 )
 
 
-def add_heating_capacities_installed_before_baseyear(n, baseyear, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
+def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
     """
     Parameters
     ----------
@@ -314,7 +314,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, ashp_cop, gshp
     # https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
     # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
     # TODO start from original file
-    grouping_years = [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
+
     # retrieve existing heating capacities
     techs = [
         'gas boiler',
@@ -505,14 +505,16 @@ if __name__ == "__main__":
         snakemake.config['costs']['lifetime']
     )
 
-    grouping_years = snakemake.config['existing_capacities']['grouping_years']
-    add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
+    grouping_years_power = snakemake.config['existing_capacities']['grouping_years_power']
+    grouping_years_heat = snakemake.config['existing_capacities']['grouping_years_heat']
+    add_power_capacities_installed_before_baseyear(n, grouping_years_power, costs, baseyear)
 
     if "H" in opts:
         time_dep_hp_cop = options["time_dep_hp_cop"]
         ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
         gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
         default_lifetime = snakemake.config['costs']['lifetime']
-        add_heating_capacities_installed_before_baseyear(n, baseyear, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
+        add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years_heat,
+                                                         ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
 
     n.export_to_netcdf(snakemake.output[0])

From 286520f396d2f364fa4de94649fbdd1401ee65dd Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 3 Aug 2022 17:06:41 +0200
Subject: [PATCH 040/243] compatibility: fix plotting for new pypsa version

---
 scripts/plot_network.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 4a1bc6d0..cbc7cdbd 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -307,7 +307,6 @@ def plot_h2_map(network):
     )
 
     n.plot(
-        geomap=False,
         bus_sizes=0,
         link_colors='#72d3d6',
         link_widths=link_widths_retro,
@@ -441,7 +440,6 @@ def plot_ch4_map(network):
     )
 
     n.plot(
-        geomap=False,
         ax=ax,
         bus_sizes=0.,
         link_colors='#e8d1d1',
@@ -451,7 +449,6 @@ def plot_ch4_map(network):
     )
 
     n.plot(
-        geomap=False,
         ax=ax,
         bus_sizes=0.,
         link_colors=link_color_used,

From 9cf5be84fd9e3f62e1d50e08ef2805a82964217c Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 3 Aug 2022 17:07:19 +0200
Subject: [PATCH 041/243] bugfix: solve_network should look for wildcard
 sector_opts

---
 scripts/solve_network.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index d8ed176a..563d8c29 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -296,7 +296,7 @@ if __name__ == "__main__":
     if tmpdir is not None:
         from pathlib import Path
         Path(tmpdir).mkdir(parents=True, exist_ok=True)
-    opts = snakemake.wildcards.opts.split('-')
+    opts = snakemake.wildcards.sector_opts.split('-')
     solve_opts = snakemake.config['solving']['options']
 
     fn = getattr(snakemake.log, 'memory', None)

From 23539ee219c39b07969b66c3fa8a5f0cae405650 Mon Sep 17 00:00:00 2001
From: lisazeyen <35347358+lisazeyen@users.noreply.github.com>
Date: Thu, 4 Aug 2022 15:34:20 +0200
Subject: [PATCH 042/243] Update config.default.yaml

set default of biosng to false
---
 config.default.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/config.default.yaml b/config.default.yaml
index f99b1b7a..2d9f2c63 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -263,7 +263,7 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
-  biosng: true
+  biosng: false
 
 
 industry:

From ee458eeafb0f83e380e724aa8ae411d25319eba2 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Thu, 4 Aug 2022 16:03:16 +0200
Subject: [PATCH 043/243] change names of buses

---
 scripts/prepare_sector_network.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 15e30a65..19637b5f 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1859,7 +1859,7 @@ def add_biomass(n, costs):
             efficiency4=costs.at['solid biomass', 'CO2 intensity'] * costs.at['biomass CHP capture', 'capture_rate'],
             lifetime=costs.at[key, 'lifetime']
         )
-        
+
     #Solid biomass to liquid fuel
     if options["biomass_to_liquid"]:
         n.madd("Link",
@@ -1867,11 +1867,11 @@ def add_biomass(n, costs):
            suffix=" biomass to liquid",
            bus0=spatial.biomass.nodes,
            bus1=spatial.oil.nodes,
-           bus3="co2 atmosphere",
+           bus2="co2 atmosphere",
            carrier="biomass to liquid",
            lifetime=costs.at['BtL', 'lifetime'],
            efficiency=costs.at['BtL', 'efficiency'],
-           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'],
+           efficiency2=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'],
            p_nom_extendable=True,
            capital_cost=costs.at['BtL', 'fixed'],
            marginal_cost=costs.at['BtL', 'efficiency']*costs.loc["BtL", "VOM"]
@@ -1883,13 +1883,13 @@ def add_biomass(n, costs):
            suffix=" biomass to liquid CC",
            bus0=spatial.biomass.nodes,
            bus1=spatial.oil.nodes,
-           bus2=spatial.co2.nodes,
-           bus3="co2 atmosphere",
+           bus2="co2 atmosphere",
+           bus3=spatial.co2.nodes,
            carrier="biomass to liquid",
            lifetime=costs.at['BtL', 'lifetime'],
            efficiency=costs.at['BtL', 'efficiency'],
-           efficiency2=costs.at['BtL', 'CO2 stored'] * costs.at['BtL', 'capture rate'],
-           efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'] * (1 - costs.at['BtL', 'capture rate']),
+           efficiency2=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'] * (1 - costs.at['BtL', 'capture rate']),
+           efficiency3=costs.at['BtL', 'CO2 stored'] * costs.at['BtL', 'capture rate'],
            p_nom_extendable=True,
            capital_cost=costs.at['BtL', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
                "BtL", "CO2 stored"],

From 274e7df5af22847e106a058c7c2ff6fc70d4035e Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Thu, 4 Aug 2022 16:03:32 +0200
Subject: [PATCH 044/243] update release notes

---
 doc/release_notes.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index f1fb12fe..1d4735ca 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -56,6 +56,10 @@ incorporates retrofitting options to hydrogen.
 
 **New features and functionality**
 
+* option for BioSNG (methane from biomass) with and without CC is added
+
+* option for BtL (Biomass to liquid fuel/oil) with and without CC is added
+
 * Units are assigned to the buses. These only provide a better understanding. The specifications of the units are not taken into account in the optimisation, which means that no automatic conversion of units takes place.
 
 * Option ``retrieve_sector_databundle`` to automatically retrieve and extract data bundle.

From 289c25268837f686459ed2633c507c243a0fcd69 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Thu, 4 Aug 2022 17:42:57 +0200
Subject: [PATCH 045/243] remove link to co2 atmosphere

---
 scripts/prepare_sector_network.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 386b6aaa..a6e1e8be 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1521,7 +1521,7 @@ def add_heat(n, costs):
                 capital_cost=costs.at[key, 'efficiency'] * costs.at[key, 'fixed'],
                 lifetime=costs.at[key, 'lifetime']
             )
-            
+
             if options["biomass_boiler"] and name not in ["urban central"]:
                 #TODO: Add surcharge for pellets
                 n.madd("Link",
@@ -1529,10 +1529,8 @@ def add_heat(n, costs):
                     p_nom_extendable=True,
                     bus0=spatial.biomass.df.loc[nodes[name], "nodes"].values,
                     bus1=nodes[name] + f" {name} heat",
-                    bus2="co2 atmosphere",
                     carrier=name + " biomass boiler",
                     efficiency=costs.at['biomass boiler', 'efficiency'],
-                    efficiency2=costs.at['solid biomass', 'CO2 intensity']-costs.at['solid biomass', 'CO2 intensity'],
                     capital_cost=costs.at['biomass boiler', 'efficiency'] * costs.at['biomass boiler', 'fixed'],
                     lifetime=costs.at['biomass boiler', 'lifetime']
                 )

From cfd875968432a30852c2ae2c92b9b4e6d4e97d6e Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Fri, 5 Aug 2022 11:27:14 +0200
Subject: [PATCH 046/243] small clean up

---
 config.default.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/config.default.yaml b/config.default.yaml
index 4cadbef3..7095df4e 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -225,6 +225,7 @@ sector:
     central: 180
   boilers: true
   oil_boilers: false
+  biomass_boiler: true
   chp: true
   micro_chp: false
   solar_thermal: true
@@ -263,7 +264,6 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
-  biomass_boiler: true
 
 
 industry:

From ff3d171bd7ac5961afc7b29371fdcd23a357603d Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Fri, 5 Aug 2022 12:19:47 +0200
Subject: [PATCH 047/243] move biomass boilers to add_biomass

---
 scripts/prepare_sector_network.py | 29 ++++++++++++++++-------------
 1 file changed, 16 insertions(+), 13 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index c0032845..bb3f48a5 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1522,19 +1522,6 @@ def add_heat(n, costs):
                 lifetime=costs.at[key, 'lifetime']
             )
 
-            if options["biomass_boiler"] and name not in ["urban central"]:
-                #TODO: Add surcharge for pellets
-                n.madd("Link",
-                    nodes[name] + f" {name} biomass boiler",
-                    p_nom_extendable=True,
-                    bus0=spatial.biomass.df.loc[nodes[name], "nodes"].values,
-                    bus1=nodes[name] + f" {name} heat",
-                    carrier=name + " biomass boiler",
-                    efficiency=costs.at['biomass boiler', 'efficiency'],
-                    capital_cost=costs.at['biomass boiler', 'efficiency'] * costs.at['biomass boiler', 'fixed'],
-                    lifetime=costs.at['biomass boiler', 'lifetime']
-                )
-
 
         if options["solar_thermal"]:
 
@@ -1874,6 +1861,22 @@ def add_biomass(n, costs):
             lifetime=costs.at[key, 'lifetime']
         )
 
+    if options["pellet_boiler"]:
+        #TODO: Add surcharge for pellets
+        nodes_heat = create_nodes_for_heat_sector()[0]
+        for name in ["residential rural", "services rural",
+                     "residential urban decentral", "services urban decentral"]:
+
+            n.madd("Link",
+                nodes_heat[name] + f" {name} biomass boiler",
+                p_nom_extendable=True,
+                bus0=spatial.biomass.df.loc[nodes_heat[name], "nodes"].values,
+                bus1=nodes_heat[name] + f" {name} heat",
+                carrier=name + " biomass boiler",
+                efficiency=costs.at['biomass boiler', 'efficiency'],
+                capital_cost=costs.at['biomass boiler', 'efficiency'] * costs.at['biomass boiler', 'fixed'],
+                lifetime=costs.at['biomass boiler', 'lifetime']
+            )
 
 def add_industry(n, costs):
 

From 8f92f0857920630d6d56439a59976c8e8487b604 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Fri, 5 Aug 2022 12:21:32 +0200
Subject: [PATCH 048/243] update relase notes

---
 doc/release_notes.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index f1fb12fe..a463f350 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -56,6 +56,8 @@ incorporates retrofitting options to hydrogen.
 
 **New features and functionality**
 
+* Add option for biomass boilers (wood pellets) for decentral heating
+
 * Units are assigned to the buses. These only provide a better understanding. The specifications of the units are not taken into account in the optimisation, which means that no automatic conversion of units takes place.
 
 * Option ``retrieve_sector_databundle`` to automatically retrieve and extract data bundle.

From 8dd143f369e8a5f25708500139fa993b0bb173ae Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Fri, 5 Aug 2022 15:55:41 +0200
Subject: [PATCH 049/243] bug fixes for missing bracket

---
 scripts/prepare_sector_network.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 6000f532..0c175ff4 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1861,7 +1861,7 @@ def add_biomass(n, costs):
             lifetime=costs.at[key, 'lifetime']
         )
 
-    if options["pellet_boiler"]:
+    if options["biomass_boiler"]:
         #TODO: Add surcharge for pellets
         nodes_heat = create_nodes_for_heat_sector()[0]
         for name in ["residential rural", "services rural",
@@ -1911,7 +1911,7 @@ def add_biomass(n, costs):
            p_nom_extendable=True,
            capital_cost=costs.at['BtL', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
                "BtL", "CO2 stored"],
-           marginal_cost=costs.at['BtL', 'efficiency'] * costs.loc["BtL", "VOM"]
+           marginal_cost=costs.at['BtL', 'efficiency'] * costs.loc["BtL", "VOM"])
 
     #BioSNG from solid biomass
     if options["biosng"]:

From ce6b92dd6d9b3a5fe47e27e9c705c3b659181073 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 8 Aug 2022 08:53:07 +0200
Subject: [PATCH 050/243] add options to aggregate snapshots temporally

---
 scripts/prepare_sector_network.py | 85 +++++++++++++++++++++++++++++--
 1 file changed, 80 insertions(+), 5 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 0c175ff4..0bfc78e0 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2413,6 +2413,84 @@ def limit_individual_line_extension(n, maxext):
     hvdc = n.links.index[n.links.carrier == 'DC']
     n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
 
+
+def apply_time_segmentation(n, segments, solver_name="cbc",
+                            overwrite_time_dependent=False):
+    """Aggregating time series to segments with different lengths
+
+    Input:
+        n: pypsa Network
+        segments: (int) number of segments in which the typical period should be
+                  subdivided
+        solver_name: (str) name of solver
+        overwrite_time_dependent: (bool) overwrite time dependent data of pypsa network
+        with typical time series created by tsam
+    """
+    try:
+        import tsam.timeseriesaggregation as tsam
+    except:
+        raise ModuleNotFoundError("Optional dependency 'tsam' not found."
+                                  "Install via 'pip install tsam'")
+
+    # get all time-dependent data
+    columns = pd.MultiIndex.from_tuples([],names=['component', 'key', 'asset'])
+    raw = pd.DataFrame(index=n.snapshots,columns=columns)
+    for component in n.all_components:
+        pnl = n.pnl(component)
+        for key in pnl.keys():
+            if not pnl[key].empty:
+                df = pnl[key].copy()
+                df.columns = pd.MultiIndex.from_product([[component], [key], df.columns])
+                raw = pd.concat([raw, df], axis=1)
+
+    # normalise all time-dependent data
+    annual_max = raw.max().replace(0,1)
+    raw = raw.div(annual_max, level=0)
+
+    # get representative segments
+    agg = tsam.TimeSeriesAggregation(raw, hoursPerPeriod=len(raw),
+                                     noTypicalPeriods=1, noSegments=int(segments),
+                                     segmentation=True, solver=solver_name)
+    segmented = agg.createTypicalPeriods()
+
+
+    weightings = segmented.index.get_level_values("Segment Duration")
+    offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
+    timesteps = [raw.index[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
+    snapshots =  pd.DatetimeIndex(timesteps)
+    sn_weightings = pd.Series(weightings, index=snapshots, name="weightings", dtype="float64")
+
+    n.set_snapshots(sn_weightings.index)
+    n.snapshot_weightings = n.snapshot_weightings.mul(sn_weightings, axis=0)
+
+    # overwrite time-dependent data with timeseries created by tsam
+    if overwrite_time_dependent:
+        values_t = segmented.mul(annual_max).set_index(snapshots)
+        for component, key in values_t.columns.droplevel(2).unique():
+            n.pnl(component)[key] = values_t[component, key]
+
+    return n
+
+def set_temporal_aggregation(n, opts, solver_name):
+    """Aggregate network temporally."""
+    for o in opts:
+        # temporal averaging
+        m = re.match(r"^\d+h$", o, re.IGNORECASE)
+        if m is not None:
+            n = average_every_nhours(n, m.group(0))
+        # representive snapshots
+        m = re.match(r"^\d+sn$", o, re.IGNORECASE)
+        if m is not None:
+            sn = int(m.group(0).split("sn")[0])
+            logger.info("use every {} snapshot as representative".format(sn))
+            n.set_snapshots(n.snapshots[::sn])
+            n.snapshot_weightings *= sn
+        # segments with package tsam
+        if "SEG" in o:
+            segments = int(o.replace("SEG",""))
+            logger.info("use temporal segmentation with {} segments".format(segments))
+            n = apply_time_segmentation(n, segments, solver_name=solver_name)
+    return n
 #%%
 if __name__ == "__main__":
     if 'snakemake' not in globals():
@@ -2514,11 +2592,8 @@ if __name__ == "__main__":
     if options["co2_network"]:
         add_co2_network(n, costs)
 
-    for o in opts:
-        m = re.match(r'^\d+h$', o, re.IGNORECASE)
-        if m is not None:
-            n = average_every_nhours(n, m.group(0))
-            break
+    solver_name = snakemake.config["solving"]["solver"]["name"]
+    n = set_temporal_aggregation(n, opts, solver_name)
 
     limit_type = "config"
     limit = get(snakemake.config["co2_budget"], investment_year)

From 4accfff1bbf50cfdec3d39e2a2a04d9cc2d3ba96 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 8 Aug 2022 08:53:16 +0200
Subject: [PATCH 051/243] update release notes

---
 doc/release_notes.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index abcbaad5..7a454f14 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -57,11 +57,13 @@ incorporates retrofitting options to hydrogen.
 **New features and functionality**
 
 
+* Add option to aggregate network temporally using representative snapshots or segments (with tsam package)
+
 * Add option for biomass boilers (wood pellets) for decentral heating
 
-* Add option for BioSNG (methane from biomass) with and without CC 
+* Add option for BioSNG (methane from biomass) with and without CC
 
-* Add option for BtL (Biomass to liquid fuel/oil) with and without CC 
+* Add option for BtL (Biomass to liquid fuel/oil) with and without CC
 
 * Units are assigned to the buses. These only provide a better understanding. The specifications of the units are not taken into account in the optimisation, which means that no automatic conversion of units takes place.
 

From 58b018e624ed384dc4812b663de8e414c2b4bf76 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 8 Aug 2022 13:52:41 +0200
Subject: [PATCH 052/243] drop powerplants which are already decommissioned

---
 scripts/add_existing_baseyear.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 5a2c226e..a209f8dc 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -131,7 +131,8 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
         'Oil': 'oil',
         'OCGT': 'OCGT',
         'CCGT': 'CCGT',
-        'Natural Gas': 'gas'
+        'Natural Gas': 'gas',
+        'Bioenergy': 'urban central solid biomass CHP',
     }
 
     fueltype_to_drop = [
@@ -139,7 +140,6 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
         'Wind',
         'Solar',
         'Geothermal',
-        'Bioenergy',
         'Waste',
         'Other',
         'CCGT, Thermal'
@@ -150,6 +150,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
         'Storage Technologies'
     ]
 
+    # drop assets which are already phased out / decomissioned
+    phased_out = df_agg[df_agg["DateOut"]<baseyear].index
+    df_agg.drop(phased_out, inplace=True)
+    # calculate remaining lifetime before phase-out
+    df_agg["lifetime"] = df_agg.DateOut-baseyear
     df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
     df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)], inplace=True)
     df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
@@ -477,7 +482,7 @@ if __name__ == "__main__":
             clusters="45",
             lv=1.0,
             opts='',
-            sector_opts='cb40ex0-365H-T-H-B-I-A-solar+p3-dist1',
+            sector_opts='365H-T-H-B-I-A-solar+p3-dist1',
             planning_horizons=2020,
         )
 
@@ -488,7 +493,7 @@ if __name__ == "__main__":
     options = snakemake.config["sector"]
     opts = snakemake.wildcards.sector_opts.split('-')
 
-    baseyear= snakemake.config['scenario']["planning_horizons"][0]
+    baseyear = snakemake.config['scenario']["planning_horizons"][0]
 
     overrides = override_component_attrs(snakemake.input.overrides)
     n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)

From 2994c91173c303169d7a607da212c2fe1e387b13 Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 8 Aug 2022 15:28:04 +0200
Subject: [PATCH 053/243] add bioenergy to add_existing

---
 scripts/add_existing_baseyear.py | 71 ++++++++++++++++++++++----------
 1 file changed, 50 insertions(+), 21 deletions(-)

diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index a209f8dc..f865ed9f 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -153,8 +153,10 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
     # drop assets which are already phased out / decomissioned
     phased_out = df_agg[df_agg["DateOut"]<baseyear].index
     df_agg.drop(phased_out, inplace=True)
-    # calculate remaining lifetime before phase-out
-    df_agg["lifetime"] = df_agg.DateOut-baseyear
+    # calculate remaining lifetime before phase-out (+1 because assumming
+    # phase out date at the end of the year)
+    df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
+    # drop unused fueltyps and technologies
     df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
     df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)], inplace=True)
     df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
@@ -187,13 +189,21 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
         aggfunc='sum'
     )
 
+    lifetime = df_agg.pivot_table(
+        index=["grouping_year", 'Fueltype'],
+        columns='cluster_bus',
+        values='lifetime',
+        aggfunc='mean'  # currently taken mean for clustering lifetimes
+    )
+
     carrier = {
         "OCGT": "gas",
         "CCGT": "gas",
         "coal": "coal",
         "oil": "oil",
         "lignite": "lignite",
-        "nuclear": "uranium"
+        "nuclear": "uranium",
+        'urban central solid biomass CHP': "biomass",
     }
 
     for grouping_year, generator in df.index:
@@ -204,7 +214,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
         capacity = capacity[~capacity.isna()]
         capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
         suffix = '-ac' if generator == 'offwind' else ''
-        name_suffix = f' {generator}{suffix}-{baseyear}'
+        name_suffix = f' {generator}{suffix}-{grouping_year}'
         asset_i = capacity.index + name_suffix
         if generator in ['solar', 'onwind', 'offwind']:
 
@@ -250,12 +260,12 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
 
             else:
 
-                p_max_pu = n.generators_t.p_max_pu[capacity.index + name_suffix]
+                p_max_pu = n.generators_t.p_max_pu[capacity.index + f' {generator}{suffix}-{baseyear}']
 
                 if not new_build.empty:
                     n.madd("Generator",
                         new_capacity.index,
-                        suffix=' ' + generator + name_suffix,
+                        suffix=' ' + name_suffix,
                         bus=new_capacity.index,
                         carrier=generator,
                         p_nom=new_capacity,
@@ -274,6 +284,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
 
             already_build = n.links.index.intersection(asset_i)
             new_build = asset_i.difference(n.links.index)
+            lifetime_assets = lifetime.loc[grouping_year,generator].dropna()
 
             # this is for the year 2020
             if not already_build.empty:
@@ -282,21 +293,39 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
             if not new_build.empty:
                 new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
 
-                n.madd("Link",
-                    new_capacity.index,
-                    suffix= name_suffix,
-                    bus0=bus0,
-                    bus1=new_capacity.index,
-                    bus2="co2 atmosphere",
-                    carrier=generator,
-                    marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
-                    capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
-                    p_nom=capacity / costs.at[generator, 'efficiency'],
-                    efficiency=costs.at[generator, 'efficiency'],
-                    efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
-                    build_year=grouping_year,
-                    lifetime=costs.at[generator, 'lifetime']
-                )
+                if generator!="urban central solid biomass CHP":
+                    n.madd("Link",
+                        new_capacity.index,
+                        suffix= name_suffix,
+                        bus0=bus0,
+                        bus1=new_capacity.index,
+                        bus2="co2 atmosphere",
+                        carrier=generator,
+                        marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
+                        capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
+                        p_nom=new_capacity / costs.at[generator, 'efficiency'],
+                        efficiency=costs.at[generator, 'efficiency'],
+                        efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
+                        build_year=grouping_year,
+                        lifetime=lifetime_assets.loc[new_capacity.index],
+                    )
+                else:
+                    key = 'central solid biomass CHP'
+                    n.madd("Link",
+                        new_capacity.index,
+                        suffix= name_suffix,
+                        bus0=spatial.biomass.df.loc[new_capacity.index]["nodes"].values,
+                        bus1=new_capacity.index,
+                        bus2=new_capacity.index + " urban central heat",
+                        carrier=generator,
+                        p_nom=new_capacity / costs.at[key, 'efficiency'],
+                        capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'],
+                        marginal_cost=costs.at[key, 'VOM'],
+                        efficiency=costs.at[key, 'efficiency'],
+                        build_year=grouping_year,
+                        efficiency2=costs.at[key, 'efficiency-heat'],
+                        lifetime=lifetime_assets.loc[new_capacity.index]
+                    )
 
 
 def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):

From 1920c2bae1d3eea190bc7dc0784893f4a9091217 Mon Sep 17 00:00:00 2001
From: Irieo <iegor.riepin@gmail.com>
Date: Tue, 9 Aug 2022 15:30:58 +0200
Subject: [PATCH 054/243] temporary fix of DateIn and DateOut

---
 scripts/add_existing_baseyear.py | 24 ++++++++++++++++++------
 1 file changed, 18 insertions(+), 6 deletions(-)

diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index f865ed9f..47728492 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -150,17 +150,29 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
         'Storage Technologies'
     ]
 
-    # drop assets which are already phased out / decomissioned
-    phased_out = df_agg[df_agg["DateOut"]<baseyear].index
-    df_agg.drop(phased_out, inplace=True)
-    # calculate remaining lifetime before phase-out (+1 because assumming
-    # phase out date at the end of the year)
-    df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
     # drop unused fueltyps and technologies
     df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
     df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)], inplace=True)
     df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
 
+    # Intermediate fix for DateIn & DateOut
+    # Fill missing DateIn
+    Biomass = df_agg.loc[df_agg.Fueltype=='urban central solid biomass CHP'].index
+    mean = df_agg.loc[Biomass, 'DateIn'].mean()
+    df_agg.loc[Biomass, 'DateIn'] = df_agg.loc[Biomass, 'DateIn'].fillna(int(mean))
+    # Fill missing DateOut
+    dateout = df_agg.loc[Biomass, 'DateIn'] + snakemake.config['costs']['lifetime']
+    df_agg.loc[Biomass, 'DateOut'] = df_agg.loc[Biomass, 'DateOut'].fillna(dateout)
+
+
+    # drop assets which are already phased out / decomissioned
+    phased_out = df_agg[df_agg["DateOut"]<baseyear].index
+    df_agg.drop(phased_out, inplace=True)
+
+    # calculate remaining lifetime before phase-out (+1 because assumming
+    # phase out date at the end of the year)
+    df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
+
     # assign clustered bus
     busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
     busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()

From b42e76040d5bab0c9c381e3c691177d99a2549bc Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Tue, 9 Aug 2022 21:30:45 +0200
Subject: [PATCH 055/243] small clean up

---
 config.default.yaml              |  7 ++++++-
 scripts/add_existing_baseyear.py | 22 +++++++++++-----------
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index d95e642d..6e8cef81 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -42,7 +42,12 @@ scenario:
   # decay with initial growth rate 0
   planning_horizons: # investment years for myopic and perfect; or costs year for overnight
     - 2030
-  # for example, set to [2020, 2030, 2040, 2050] for myopic foresight
+  # for example, set to
+  # - 2020
+  # - 2030
+  # - 2040
+  # - 2050
+  # for myopic foresight
 
 # CO2 budget as a fraction of 1990 emissions
 # this is over-ridden if CO2Lx is set in sector_opts
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 47728492..1b13f7f1 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -157,12 +157,12 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
 
     # Intermediate fix for DateIn & DateOut
     # Fill missing DateIn
-    Biomass = df_agg.loc[df_agg.Fueltype=='urban central solid biomass CHP'].index
-    mean = df_agg.loc[Biomass, 'DateIn'].mean()
-    df_agg.loc[Biomass, 'DateIn'] = df_agg.loc[Biomass, 'DateIn'].fillna(int(mean))
+    biomass_i = df_agg.loc[df_agg.Fueltype=='urban central solid biomass CHP'].index
+    mean = df_agg.loc[biomass_i, 'DateIn'].mean()
+    df_agg.loc[biomass_i, 'DateIn'] = df_agg.loc[biomass_i, 'DateIn'].fillna(int(mean))
     # Fill missing DateOut
-    dateout = df_agg.loc[Biomass, 'DateIn'] + snakemake.config['costs']['lifetime']
-    df_agg.loc[Biomass, 'DateOut'] = df_agg.loc[Biomass, 'DateOut'].fillna(dateout)
+    dateout = df_agg.loc[biomass_i, 'DateIn'] + snakemake.config['costs']['lifetime']
+    df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
 
 
     # drop assets which are already phased out / decomissioned
@@ -442,10 +442,10 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
         for i, grouping_year in enumerate(grouping_years):
 
             if int(grouping_year) + default_lifetime <= int(baseyear):
-                ratio = 0
-            else:
-                # installation is assumed to be linear for the past 25 years (default lifetime)
-                ratio = (int(grouping_year) - int(grouping_years[i-1])) / default_lifetime
+                continue
+
+            # installation is assumed to be linear for the past 25 years (default lifetime)
+            ratio = (int(grouping_year) - int(grouping_years[i-1])) / default_lifetime
 
             n.madd("Link",
                 nodes[name],
@@ -509,7 +509,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
             # delete links with p_nom=nan corresponding to extra nodes in country
             n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and np.isnan(n.links.p_nom[index])])
 
-            # delete links if their lifetime is over and p_nom=0
+            # delete links with capacities below threshold
             threshold = snakemake.config['existing_capacities']['threshold_capacity']
             n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and n.links.p_nom[index] < threshold])
 
@@ -524,7 +524,7 @@ if __name__ == "__main__":
             lv=1.0,
             opts='',
             sector_opts='365H-T-H-B-I-A-solar+p3-dist1',
-            planning_horizons=2020,
+            planning_horizons=2030,
         )
 
     logging.basicConfig(level=snakemake.config['logging_level'])

From f129547b9d3787c8266a5996d54b5b6f1d2e6898 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Wed, 10 Aug 2022 07:24:18 +0200
Subject: [PATCH 056/243] include agriculture in covered sectors

---
 doc/index.rst | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/doc/index.rst b/doc/index.rst
index fb5f6895..0fc554dd 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -25,9 +25,10 @@ transmission network level that covers the full ENTSO-E area.
 PyPSA-Eur-Sec builds on the electricity generation and transmission
 model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ to add demand
 and supply for the following sectors: transport, space and water
-heating, biomass, industry and industrial feedstocks. This completes
-the energy system and includes all greenhouse gas emitters except
-waste management, agriculture, forestry and land use.
+heating, biomass, energy consumption in the agriculture, industry 
+and industrial feedstocks. This completes the energy system and includes 
+all greenhouse gas emitters except waste management, agriculture, 
+forestry and land use.
 
 
 **WARNING**: PyPSA-Eur-Sec is under active development and has several

From 1627e36c029b32410fd3d02b3039e009f995adf4 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:02:33 +0200
Subject: [PATCH 057/243] Add one extra line to supply_demand as example

---
 doc/supply_demand.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8d785f6a..ed2d2d4d 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -55,7 +55,9 @@ Oil and gas boilers
 Heat pumps
 -------------
 
-Either air-to-water or ground-to-water heat pumps are implemented.
+
+
+Air-to-water heatpumps are used in urban central bus. 
 
 They have coefficient of performance (COP) based on either the
 external air or the soil hourly temperature.

From 06d592281db46643a86e885b681417579c321114 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:26:49 +0200
Subject: [PATCH 058/243] Revert "Add one extra line to supply_demand as
 example"

This reverts commit 1627e36c029b32410fd3d02b3039e009f995adf4.
---
 doc/supply_demand.rst | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ed2d2d4d..8d785f6a 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -55,9 +55,7 @@ Oil and gas boilers
 Heat pumps
 -------------
 
-
-
-Air-to-water heatpumps are used in urban central bus. 
+Either air-to-water or ground-to-water heat pumps are implemented.
 
 They have coefficient of performance (COP) based on either the
 external air or the soil hourly temperature.

From 3f78c83cf2af05deb0370247ba0845d82cd2dfe5 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Thu, 11 Aug 2022 13:30:16 +0200
Subject: [PATCH 059/243] Update supply_demand.rst

Test number two. Let's hope for the best.
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ed2d2d4d..0625ecea 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -177,7 +177,7 @@ Methane supply
 Fossil, biogas, Sabatier (hydrogen to methane), HELMETH (directly power to methane with efficient heat integration).
 
 
-Solid biomass demand
+Solid biomass demand (not up to date until Gorm makes the next commit. It will be great!)
 =====================
 
 Solid biomass provides process heat up to 500 Celsius in industry, as well as feeding CHP plants in district heating networks.

From 71a94d5ef6cd5294b15a3c2f52751d840716e486 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Thu, 11 Aug 2022 13:41:13 +0200
Subject: [PATCH 060/243] Update heat demand

---
 doc/supply_demand.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8d785f6a..2e475738 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -38,6 +38,12 @@ The remaining electricity demand for households and services is distributed insi
 
 Heat demand
 =============================
+Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
+Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, line `212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_].
+
+.. literalinclude:: ../config.default.yaml
+    :language: yaml
+    :lines: 212
 
 Heat demand is split into:
 

From 1ef8416307d830589ab28545d1fd95b32eede79e Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:48:59 +0200
Subject: [PATCH 061/243] Revert "Merge pull request #2 from
 Parisra/improve-doc"

This reverts commit a978f78a966a9a4b9913a18356420969b17bb2f7, reversing
changes made to c3e1a5504966286cbe914348f4c496bd08075920.
---
 doc/supply_demand.rst | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 18c23e4d..ca4b7e5f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -38,12 +38,6 @@ The remaining electricity demand for households and services is distributed insi
 
 Heat demand
 =============================
-Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
-Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, line `212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_].
-
-.. literalinclude:: ../config.default.yaml
-    :language: yaml
-    :lines: 212
 
 Heat demand is split into:
 

From 56a154fd6e15fce56749fdc9a3ab15eb2b6e64cf Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:49:17 +0200
Subject: [PATCH 062/243] Revert "Revert "Merge pull request #2 from
 Parisra/improve-doc""

This reverts commit 1ef8416307d830589ab28545d1fd95b32eede79e.
---
 doc/supply_demand.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ca4b7e5f..18c23e4d 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -38,6 +38,12 @@ The remaining electricity demand for households and services is distributed insi
 
 Heat demand
 =============================
+Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
+Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, line `212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_].
+
+.. literalinclude:: ../config.default.yaml
+    :language: yaml
+    :lines: 212
 
 Heat demand is split into:
 

From 9e698eede01966963f8a022d77b7a0d2f13b7446 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:49:35 +0200
Subject: [PATCH 063/243] Revert "Merge pull request #2 from
 Parisra/improve-doc"

This reverts commit a978f78a966a9a4b9913a18356420969b17bb2f7, reversing
changes made to c3e1a5504966286cbe914348f4c496bd08075920.
---
 doc/supply_demand.rst | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 18c23e4d..ca4b7e5f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -38,12 +38,6 @@ The remaining electricity demand for households and services is distributed insi
 
 Heat demand
 =============================
-Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
-Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, line `212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_].
-
-.. literalinclude:: ../config.default.yaml
-    :language: yaml
-    :lines: 212
 
 Heat demand is split into:
 

From 00c05668f8b5626ebb4ee14c8f129ab8b5dd23a3 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:49:57 +0200
Subject: [PATCH 064/243] Revert "Merge pull request #1 from
 GormBruunAndresen/improve-doc"

This reverts commit c3e1a5504966286cbe914348f4c496bd08075920, reversing
changes made to 06d592281db46643a86e885b681417579c321114.
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ca4b7e5f..8d785f6a 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -175,7 +175,7 @@ Methane supply
 Fossil, biogas, Sabatier (hydrogen to methane), HELMETH (directly power to methane with efficient heat integration).
 
 
-Solid biomass demand (not up to date until Gorm makes the next commit. It will be great!)
+Solid biomass demand
 =====================
 
 Solid biomass provides process heat up to 500 Celsius in industry, as well as feeding CHP plants in district heating networks.

From 8a3fc0813a68c8dfcce81a7256e0f04e347ce33c Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:50:06 +0200
Subject: [PATCH 065/243] Revert "Revert "Add one extra line to supply_demand
 as example""

This reverts commit 06d592281db46643a86e885b681417579c321114.
---
 doc/supply_demand.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8d785f6a..ed2d2d4d 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -55,7 +55,9 @@ Oil and gas boilers
 Heat pumps
 -------------
 
-Either air-to-water or ground-to-water heat pumps are implemented.
+
+
+Air-to-water heatpumps are used in urban central bus. 
 
 They have coefficient of performance (COP) based on either the
 external air or the soil hourly temperature.

From c4f639edefca51d71ffd0702571c3c124823d2c0 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Thu, 11 Aug 2022 13:50:11 +0200
Subject: [PATCH 066/243] Revert "Add one extra line to supply_demand as
 example"

This reverts commit 1627e36c029b32410fd3d02b3039e009f995adf4.
---
 doc/supply_demand.rst | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ed2d2d4d..8d785f6a 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -55,9 +55,7 @@ Oil and gas boilers
 Heat pumps
 -------------
 
-
-
-Air-to-water heatpumps are used in urban central bus. 
+Either air-to-water or ground-to-water heat pumps are implemented.
 
 They have coefficient of performance (COP) based on either the
 external air or the soil hourly temperature.

From 8d41534109c76f0000c89dedbfe64b60183ce239 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Thu, 11 Aug 2022 13:54:42 +0200
Subject: [PATCH 067/243] Update supply_demand.rst

Updated version of the biomass description. Links to other parts of the documentation still needs to be added.
---
 doc/supply_demand.rst | 35 +++++++++++++++++++++++++++++------
 1 file changed, 29 insertions(+), 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 0625ecea..0a1990cf 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -176,16 +176,39 @@ Methane supply
 
 Fossil, biogas, Sabatier (hydrogen to methane), HELMETH (directly power to methane with efficient heat integration).
 
+Biomass
+============
 
-Solid biomass demand (not up to date until Gorm makes the next commit. It will be great!)
-=====================
+Biomass supply
+---------------
+Biomass supply potentials for each European country are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
 
-Solid biomass provides process heat up to 500 Celsius in industry, as well as feeding CHP plants in district heating networks.
+The desired scenario can be selected in the pypsa-eur-sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JREC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
 
-Solid biomass supply
-=====================
+The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the categories : biogas, solid biomass, and not included.
+Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas (that is upgraded to biomethane). More details below.
+
+Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste can be used directly or converted to gas or liquid fuels. More details below.
+
+Feedstocks labeled as not included are ignored by the model.
+A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be the medium availability scenario for 2030 where only residues from agriculture and forestry as well as biodegradable municipal waste are considered as energy feedstocks. Fuel crops are avoided because they compete with scarce land for food production, while primary wood, as well as wood chips and pellets, are avoided because of concerns about sustainability . See the supporting materials of the `paper <https://www.sciencedirect.com/science/article/pii/S1364032117302034>`_ for more details.
+
+Solid biomass conversion and use
+----------------------------------
+Solid biomass can be used directly to provide process heat up to 500 C in the industry. It can also be burnt in CHP plants and boilers associated with heating systems. These technologies are described elsewhere [link to heat and industry sections].
+
+Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture [link to technology data].
+Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture [link to technology data].
+
+Transport of solid biomass
+---------------------------
+The transport of solid biomass can either be assumed unlimited between countries or it can be associated with a country specific cost per MWh/km. In the config file these options are toggled `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. If the option is off, use of solid biomass is transport. If it is turned on, a biomass transport network will be `created <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L1803>`_ between all nodes. This network resembles road transport of biomass and the cost of transportation is a variable cost which is proportional to distance and a country specific cost per MWh/km. The latter is `estimated <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_transport_costs.py>`_ from the country specific costs per ton/km used in the publication `“The JRC-EU-TIMES model. Bioenergy potentials for EU and neighbouring countries” <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
+
+Biogas transport and use
+------------------------
+Biogas will be aggregated into a common European resources if a gas network is not modeled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
+The model can only use biogas by first upgrading it to natural gas quality [link to tech description] (bio methane) which is fed into the general gas network.
 
-Only wastes and residues from the JRC ENSPRESO biomass dataset.
 
 
 Oil product demand

From c6b7f05c73ca9e2d8ee7b85caaca8e20d8beee36 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Thu, 11 Aug 2022 14:05:24 +0200
Subject: [PATCH 068/243] Add hydrogen to supply and demand

---
 doc/supply_demand.rst | 37 +++++++++++++++++++++++++++++--------
 1 file changed, 29 insertions(+), 8 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8d785f6a..f326a63f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -145,22 +145,43 @@ Further information are given in the publication
 
 `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
 
+## Hydrogen demand
 
-Hydrogen demand
-==================
+Hydrogen is consumed in the industry sector (link to industry) to produce ammonia [link to ammonia industry section] and direct reduced iron (DRI) [link to DRI industry section]. Hydrogen is also consumed to produce synthetic methane [link to section “Methane supply”] and liquid hydrocarbons [link to fossil-oil based supply”] which have multiple uses in industry and other sectors. 
+Hydrogen is also used for transport applications (link to transport), where it is exogenously fixed. It is used in [heavy-duty land transport](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181) and as liquified hydrogen in the shipping sector [add link to shipping sector]. Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations [Add a link to the section where we describe the Electricity sector and how storage is modelled there]. The waste heat from the stationary fuel cells can be used in [district-heating systems](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256).
 
-Stationary fuel cell CHP.
+## Hydrogen supply
+Today, most of the H2 consumed globally is produced from natural gas by steam methane reforming (SMR)
 
-Transport applications (heavy-duty road vehicles, liquid H2 in shipping).
+$$
+CH_4 + H_2O → CO + 3H_2
+$$
 
-Industry (ammonia, precursor to hydrocarbons for chemicals and iron/steel).
+combined with a water-gas shift reaction
 
+$$
+CO + H_2O → CO_2 + H_2
+$$
 
-Hydrogen supply
-=================
+SMR is included [here](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245). 
+PyPSA-Eur-Sec allows this route of H2 production with and without [carbon capture (CC)] (Link to section on Carbon Capture Storage and Utilization). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
 
-Steam Methane Reforming (SMR), SMR+CCS, electrolysers.
+Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
 
+$$
+2H_2O → 2H_2 + O_2
+$$
+
+For the electrolysis, alkaline electrolysers are chosen since they have lower cost and higher cumulative installed capacity than polymer electrolyte membrane (PEM) electrolysers. The techno-economic assumptions are taken from the technology-data repository. Waste heat from electrolysis is not leveraged in the model.
+
+*Transport*
+
+Hydrogen is transported by pipelines. H2 pipelines are endogenously generated, either via a  greenfield H2 network, or by [retrofitting natural gas pipelines](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in [link to H2 backbone study]) of its nominal capacity (exogenously determined in the [config file](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266)) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
+New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
+
+*Storage*
+
+Hydrogen can be stored in overground steel tanks or [underground salt caverns](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L250). For the latter, energy storage capacities in every country are limited to the potential estimation for onshore salt caverns within [50 km](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L251) of shore to avoid environmental issues associated with brine solution disposal. Underground storage potentials for hydrogen in European salt caverns is acquired from [Caglayan et al.](https://doi.org/10.1016/j.ijhydene.2019.12.161).
 
 Methane demand
 ==================

From ed2ca5ec3cc3336cdab536ec058a672ffbd738b5 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Thu, 11 Aug 2022 14:06:26 +0200
Subject: [PATCH 069/243] Edit format of headings

---
 doc/supply_demand.rst | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index f326a63f..bacffe8d 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -145,12 +145,15 @@ Further information are given in the publication
 
 `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
 
-## Hydrogen demand
+Hydrogen demand
+=============================
 
 Hydrogen is consumed in the industry sector (link to industry) to produce ammonia [link to ammonia industry section] and direct reduced iron (DRI) [link to DRI industry section]. Hydrogen is also consumed to produce synthetic methane [link to section “Methane supply”] and liquid hydrocarbons [link to fossil-oil based supply”] which have multiple uses in industry and other sectors. 
 Hydrogen is also used for transport applications (link to transport), where it is exogenously fixed. It is used in [heavy-duty land transport](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181) and as liquified hydrogen in the shipping sector [add link to shipping sector]. Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations [Add a link to the section where we describe the Electricity sector and how storage is modelled there]. The waste heat from the stationary fuel cells can be used in [district-heating systems](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256).
 
-## Hydrogen supply
+Hydrogen supply
+=============================
+
 Today, most of the H2 consumed globally is produced from natural gas by steam methane reforming (SMR)
 
 $$

From b9a47b34ab287b153a2159b41922bde7dda17162 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Thu, 11 Aug 2022 14:17:00 +0200
Subject: [PATCH 070/243] Update supply_demand.rst

Adding some headlines for transportation.
---
 doc/supply_demand.rst | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 0a1990cf..e595a03d 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -243,6 +243,17 @@ Solid biomass is used for up to 500 Celsius, mostly in paper and pulp and food a
 
 Higher temperatures are met with methane.
 
+Transportation
+=========================
+
+Land transport
+-----------------
+
+Aviation
+-----------------
+
+Shipping
+-----------------
 
 Carbon dioxide capture, usage and sequestration (CCU/S)
 =========================================================

From 78b91b55a73bbaabfaee7c16fa3178b5a6930475 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Thu, 11 Aug 2022 14:17:47 +0200
Subject: [PATCH 071/243] Update supply_demand.rst

Adding text for shipping. Internal links needs to be updated.
---
 doc/supply_demand.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index e595a03d..1e9c8bf0 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -254,6 +254,12 @@ Aviation
 
 Shipping
 -----------------
+Shipping energy demand is covered by a combination of oil and hydrogen. The amount of oil products that are converted into hydrogen follow an `exogenously defined path <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_. To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2089>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology data. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
+
+The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.
+
+The user can toggle if the energy demand for liquefaction of the hydrogen used for shipping should be `included or not  <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
+
 
 Carbon dioxide capture, usage and sequestration (CCU/S)
 =========================================================

From 8b29e36b011c31ff5b2667b2fceefdfa07443828 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Thu, 11 Aug 2022 14:26:26 +0200
Subject: [PATCH 072/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index bacffe8d..b959bf12 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -149,7 +149,7 @@ Hydrogen demand
 =============================
 
 Hydrogen is consumed in the industry sector (link to industry) to produce ammonia [link to ammonia industry section] and direct reduced iron (DRI) [link to DRI industry section]. Hydrogen is also consumed to produce synthetic methane [link to section “Methane supply”] and liquid hydrocarbons [link to fossil-oil based supply”] which have multiple uses in industry and other sectors. 
-Hydrogen is also used for transport applications (link to transport), where it is exogenously fixed. It is used in [heavy-duty land transport](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181) and as liquified hydrogen in the shipping sector [add link to shipping sector]. Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations [Add a link to the section where we describe the Electricity sector and how storage is modelled there]. The waste heat from the stationary fuel cells can be used in [district-heating systems](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256).
+Hydrogen is also used for transport applications (link to transport), where it is exogenously fixed. It is used in `heavy-duty land transport <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_ and as liquified hydrogen in the shipping sector [add link to shipping sector]. Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations [Add a link to the section where we describe the Electricity sector and how storage is modelled there]. The waste heat from the stationary fuel cells can be used in `district-heating systems <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_.
 
 Hydrogen supply
 =============================
@@ -166,7 +166,7 @@ $$
 CO + H_2O → CO_2 + H_2
 $$
 
-SMR is included [here](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245). 
+SMR is included `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245>`_. 
 PyPSA-Eur-Sec allows this route of H2 production with and without [carbon capture (CC)] (Link to section on Carbon Capture Storage and Utilization). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
 
 Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
@@ -179,12 +179,12 @@ For the electrolysis, alkaline electrolysers are chosen since they have lower co
 
 *Transport*
 
-Hydrogen is transported by pipelines. H2 pipelines are endogenously generated, either via a  greenfield H2 network, or by [retrofitting natural gas pipelines](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in [link to H2 backbone study]) of its nominal capacity (exogenously determined in the [config file](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266)) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
+Hydrogen is transported by pipelines. H2 pipelines are endogenously generated, either via a  greenfield H2 network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in [link to H2 backbone study]) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
 New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
 
 *Storage*
 
-Hydrogen can be stored in overground steel tanks or [underground salt caverns](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L250). For the latter, energy storage capacities in every country are limited to the potential estimation for onshore salt caverns within [50 km](https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L251) of shore to avoid environmental issues associated with brine solution disposal. Underground storage potentials for hydrogen in European salt caverns is acquired from [Caglayan et al.](https://doi.org/10.1016/j.ijhydene.2019.12.161).
+Hydrogen can be stored in overground steel tanks or `underground salt caverns <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L250>`_. For the latter, energy storage capacities in every country are limited to the potential estimation for onshore salt caverns within `50 km <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L251>`_ of shore to avoid environmental issues associated with brine solution disposal. Underground storage potentials for hydrogen in European salt caverns is acquired from `Caglayan et al. <https://doi.org/10.1016/j.ijhydene.2019.12.161>`_
 
 Methane demand
 ==================

From 495b6eab2c78348c60e24f217270031cdc5ab076 Mon Sep 17 00:00:00 2001
From: Ebbe Kyhl <69363603+ebbekyhl@users.noreply.github.com>
Date: Thu, 11 Aug 2022 14:45:06 +0200
Subject: [PATCH 073/243] add section on methane

---
 doc/supply_demand.rst | 28 +++++++++++++++++++++-------
 1 file changed, 21 insertions(+), 7 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8d785f6a..1f22de28 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -162,17 +162,31 @@ Hydrogen supply
 Steam Methane Reforming (SMR), SMR+CCS, electrolysers.
 
 
-Methane demand
-==================
+Methane demand  
+====================================
+                  
+Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat[LINK TO INDUSTRY OVERVIEW]. Methane is not used in the trans- port sector because of engine slippage.
 
-Can be used in boilers, in CHPs, in industry for high temperature heat, in OCGT.
+Methane supply                          
+===================================
 
-Not used in transport because of engine slippage.
+In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modeled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
+Biogas can be upgraded to methane. 
+Synthetic methane can be produced by processing hydrogen and captures CO2 in the Sabatier reaction 
 
-Methane supply
-=================
+$$                   
+CO_2 + 4H_2 → CH_4 + 2H_2O
+$$
 
-Fossil, biogas, Sabatier (hydrogen to methane), HELMETH (directly power to methane with efficient heat integration).
+Direct power-to-methane conversion with efficient heat integration developed in the HELMETH project is also an option. The share of synthetic, biogenic and fossil methane is an optimisation result depending on the techno-economic assumptions.
+                
+*Methane transport*
+            
+The existing European gas transmission network is represented based on the SciGRID Gas IGGIELGN dataset. This dataset is based on compiled and merged data from the ENTSOG maps  and other publicly available data sources. It includes data on the capacity, diameter, pressure, length, and directionality of pipelines. Missing capacity data is conservatively inferred from the pipe diameter following conversion factors derived from an EHB report. The gas network is clustered to the selected number of model regions. Gas pipelines can be endogenously expanded or repurposed for hydrogen transport. Gas flows are represented by a lossless transport model. Methane is assumed to be transmitted without cost or capacity constraints because future demand is predicted to be low compared to available transport capacities.
+
+The following figure shows the unclustered European gas transmission network based on the SciGRID Gas IGGIELGN dataset. Pipelines are color-coded by estimated capacities. Markers indicate entry-points, sites of fossil resource extraction, and LNG terminals.            
+    
+.. image:: ../graphics/gas_pipeline_figure.png
 
 
 Solid biomass demand

From 02ee751a37227aac4f41eeec17d36202829a3e69 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Thu, 11 Aug 2022 14:46:33 +0200
Subject: [PATCH 074/243] Adding oil-based products section

---
 doc/supply_demand.rst | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index b959bf12..9db19710 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -210,15 +210,26 @@ Solid biomass supply
 Only wastes and residues from the JRC ENSPRESO biomass dataset.
 
 
-Oil product demand
-=====================
+Oil-based products demand
+========================
+Naphtha is used as a feedstock in the chemicals industry[LINK TO CHEMICAL INDUSTRY]. Furthermore, kerosene is used as transport fuel in the aviation sector[LINK TO AVIATION SECTOR]. Non-electrified agriculture machinery also consumes gasoline. 
+Land transport [LINK TO LAND TRANSPORT]  that is not electrified or converted into using H2-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
 
-Transport fuels, agriculture machinery and naphtha as a feedstock for the chemicals industry.
 
-Oil product supply
-======================
+Oil-based products supply 
+======================== 							
+Oil-based products can be either of fossil origin or synthetically produced by combining H2 [link to hydrogen] and captured CO2 [link to carbon capture] in Fischer-Tropsch plants		
 
-Fossil or Fischer-Tropsch.
+$$
+𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
+$$
+
+with costs as included from the `technology-data repository <https://github.com/PyPSA/technology-data/blob/master/latex_tables/tables_in_latex.pdf>`_. The waste heat from the Fischer-Tropsch process is supplied to `district heating networks <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_. The share of fossil and synthetic oil is an optimisation result depending on the techno-economic assumptions.
+
+
+Oil-based transport
+======================== 		
+Liquid hydrocarbons are assumed to be transported freely among the model region since future demand is predicted to be low, transport costs for liquids are low and no bottlenecks are expected. 
 
 
 Industry demand

From 86fdc61b47472c33c19d6c471dcd2f12b0394dcb Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Thu, 11 Aug 2022 14:49:55 +0200
Subject: [PATCH 075/243] Add gas figure for methane section doc

---
 graphics/gas_pipeline_figure.png | Bin 0 -> 275190 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 graphics/gas_pipeline_figure.png

diff --git a/graphics/gas_pipeline_figure.png b/graphics/gas_pipeline_figure.png
new file mode 100644
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From c17061be965c06bea46abd1900ae6a1918b4c329 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Thu, 11 Aug 2022 14:53:51 +0200
Subject: [PATCH 076/243] Update supply_demand.rst

General comment on transport.
---
 doc/supply_demand.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 1e9c8bf0..91af92ff 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -245,6 +245,7 @@ Higher temperatures are met with methane.
 
 Transportation
 =========================
+Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
 
 Land transport
 -----------------

From aefef9b0911e2e413b2165fd4b4e2d3157f71405 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Thu, 11 Aug 2022 14:55:25 +0200
Subject: [PATCH 077/243] Update supply_demand.rst

Description of aviation. It is pretty simple.
---
 doc/supply_demand.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 91af92ff..db7d23dc 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -252,6 +252,7 @@ Land transport
 
 Aviation
 -----------------
+The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
 Shipping
 -----------------

From d6265ed81b0898b837a17c08ccf0385f23b6c77b Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Fri, 19 Aug 2022 09:20:55 +0200
Subject: [PATCH 078/243] Update index.rst

---
 doc/index.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/index.rst b/doc/index.rst
index 0fc554dd..fd9cac6c 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -26,8 +26,8 @@ PyPSA-Eur-Sec builds on the electricity generation and transmission
 model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ to add demand
 and supply for the following sectors: transport, space and water
 heating, biomass, energy consumption in the agriculture, industry 
-and industrial feedstocks. This completes the energy system and includes 
-all greenhouse gas emitters except waste management, agriculture, 
+and industrial feedstocks, carbon management, carbon capture and usage/sequestration. 
+This completes the energy system and includes all greenhouse gas emitters except waste management, agriculture, 
 forestry and land use.
 
 
@@ -41,9 +41,9 @@ patchy.
 We cannot support this model if you choose to use it.
 
 .. note::
-    You can find showcases of the model's capabilities in the
+    You can find showcases of the model's capabilities in the Supplementary Materials of the 
     preprint `Benefits of a Hydrogen Network in Europe
-    <https://arxiv.org/abs/2207.05816>`_, a `paper in Joule with a
+    <https://arxiv.org/abs/2207.05816>`_, the Supplementary Materials of the `paper in Joule with a
     description of the industry sector
     <https://arxiv.org/abs/2109.09563>`_, or in `a 2021 presentation
     at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_.

From f80cd2f4186edea4dc6575e49b817c6866a439b0 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 10:53:21 +0200
Subject: [PATCH 079/243] Add files via upload

---
 graphics/Heat_and_el_demand_timeseries.png | Bin 0 -> 99944 bytes
 graphics/demand-map-heat.png               | Bin 0 -> 75356 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 graphics/Heat_and_el_demand_timeseries.png
 create mode 100644 graphics/demand-map-heat.png

diff --git a/graphics/Heat_and_el_demand_timeseries.png b/graphics/Heat_and_el_demand_timeseries.png
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From 1554dae40bfc245f46d55fb1f3c704edb6e67c56 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 10:54:14 +0200
Subject: [PATCH 080/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 134 ++++++++++++++++++++++++------------------
 1 file changed, 77 insertions(+), 57 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 2334edf0..b2b1e4eb 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -38,93 +38,118 @@ The remaining electricity demand for households and services is distributed insi
 
 Heat demand
 =============================
+Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
+Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
 
-Heat demand is split into:
+•	Space heating
+The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
 
-* ``urban central``: large-scale district heating networks in urban areas with dense heat demand
-* ``residential/services urban decentral``: heating for individual buildings in urban areas
-* ``residential/services rural``: heating for individual buildings in rural areas, agriculture heat uses
+.. literalinclude:: ../config.default.yaml
+    :language: yaml
+    :lines: 212
+
+Co-optimsing of building renovation is also possible, if it is activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_.
+Renovation of the thermal envelope reduces the space heating demand and is optimised at each node for every heat bus. Renovation measures through additional insulation material and replacement of energy inefficient windows are considered.
+In a first step, costs per energy savings are estimated in `build_retro_cost.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_retro_cost.py>`_. They depend on the insulation condition of the building stock and costs for renovation of the building elements. In a second step, for those cost per energy savings two possible renovation strengths are determined: a moderate renovation with lower costs, a lower maximum possible space heat savings, and an ambitious renovation with associated higher costs and higher efficiency gains. They are added by step-wise linearisation in form of two additional generations in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_.
+Further information are given in the publication :
+ `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021)  <https://arxiv.org/abs/2012.01831>`_.
+•	Water heating
+Hot water demand is assumed to be constant throughout the year.
+•	Urban and rural heating
+For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
+
+•	Cooling demand
+Cooling is electrified and is included in the electricity demand. Cooling demand is assumed to remain at current levels.  An example of regional distribution of the total heat demand for network 181 regions is depicted below.
+
+.. image:: ../graphics/demand-map-heat.png
+
+As below figure shows, the current total heat demand in Europe is similar to the total electricity demand but features much more pronounced seasonal variations. The current total building heating demand in Europe adds up to 3084 TWh/a of which 78% occurs in urban areas.
+
+.. image:: ../graphics/Heat_and_el_demand_timeseries.png
+
+In practice, in PyPSA-Eur-Sec, there are heat demand buses to which the corresponding heat demands are added. 
+
+
+1)	Urban central heat: large-scale district heating networks in urban areas with dense heat population. Residential and services demand in these areas are added as demands to this bus
+2)	Residential urban decentral heat: heating for residential buildings in urban areas not using district heating
+3)	Services urban decentral heat: heating for services buildings in urban areas not using district heating
+4)	Residential rural heat: heating for residential buildings in rural areas with low population density.
+5)	Services rural heat: heating for residential services buildings in rural areas with low population density. Heat demand from agriculture  sector is also included here. 
 
 
 Heat supply
 =======================
 
-Oil and gas boilers
---------------------
+Different supply options are available depending on whether demand is met centrally through district heating systems, or decentrally through appliances in individual buildings. 
 
-Heat pumps
--------------
+**Urban central heat:** 
 
-Either air-to-water or ground-to-water heat pumps are implemented.
+For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. 
 
-They have coefficient of performance (COP) based on either the
-external air or the soil hourly temperature.
+**Residential and Urban decentral heat:**
 
-Ground-source heat pumps are only allowed in rural areas because of
-space constraints.
+Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
+Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `Technology-data repository <https://github.com/PyPSA/technology-data>`_.
 
-Only air-source heat pumps are allowed in urban areas. This is a
-conservative assumption, since there are many possible sources of
-low-temperature heat that could be tapped in cities (waste water,
-rivers, lakes, seas, etc.).
+Below are more detailed explanations for each heating supply component, all of which are modeled as `Links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_. in PyPSA-Eue-Sec.
 
-Resistive heaters
---------------------
+•	Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
+ 
+CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The efficiencies of each are given on the back pressure line, where the back pressure coefficient cb is the electricity output divided by the heat output. (For a more complete explanation of the operation of CHPs refer to the study by Dahl et al. : `Cost sensitivity of optimal sector-coupled district heating production systems <https://arxiv.org/pdf/1804.07557.pdf>`_.
 
+PyPSA-Eur-Sec includes CHP plants fueled by methane and solid biomass from waste and residues. Hydrogen fuel cells also produce both electricity and heat.
 
-Large Combined Heat and Power (CHP) plants
---------------------------------------------
+The methane CHP is modeled on the Danish Energy Agency (DEA) “Gas turbine simple cycle (large)” while the solid biomass CHP is based on the DEA’s “09b Wood Pellets Medium”. For biomass CHP, cb = `0.46 <https://ens.dk/sites/ens.dk/files/Statistik/technology_data_catalogue_for_el_and_dh_-_0009.pdf#page=156>`_ , whereas for gas CHP, cb = `1 <https://ens.dk/sites/ens.dk/files/Statistik/technology_data_catalogue_for_el_and_dh_-_0009.pdf#page=64>`_.
 
-A good summary of CHP options that can be implemented in PyPSA can be found in the paper `Cost sensitivity of optimal sector-coupled district heating production systems <https://doi.org/10.1016/j.energy.2018.10.044>`_.
+NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the study by `Brown et al. <https://arxiv.org/abs/1801.05290>`_ We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
 
-PyPSA-Eur-Sec includes CHP plants fuelled by methane, hydrogen and solid biomass from waste and residues.
+-  Micro-CHP 
+Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
 
-Hydrogen CHPs are fuel cells.
+•	Heat pumps
+The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter[refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
-Methane and biomass CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The methane CHP is modelled on the Danish Energy Agency (DEA) "Gas turbine simple cycle (large)" while the solid biomass CHP is based on the DEA's "09b Wood Pellets Medium".
+$$ &Delta; T = T_(sink) − T_(source) $$ 
 
-The efficiencies of each are given on the back pressure line, where the back pressure coefficient ``c_b`` is the electricity output divided by the heat output. The plants are not allowed to deviate from the back pressure line and are implement as ``Link`` objects with a fixed ratio of heat to electricity output.
+For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file] For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
 
+$$ COP (&Delta; T) = 6.81 + 0.121&Delta; T + 0.000630.&Delta; T^2; $$   
 
-NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the `Synergies paper <https://arxiv.org/abs/1801.05290>`_. We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
+for ground-sourced heat pumps (GSHP), we use the function:
 
+$$  COP(&Delta; T) = 8.77 + 0.150&Delta; T + 0.000734&Delta; T^2 $$     
 
-Micro-CHP for individual buildings
------------------------------------
+•	Resistive heaters (can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option)
+Resistive heaters produce heat with a fixed conversion efficiency (refer to `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ). 
 
-Optional.
+•	Gas, oil, and biomass boilers (can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option)
+Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas ,oil or biomass.
 
-Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants
--------------------------------------------------------------------
+•	Solar thermal collectors (can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option)
+Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the Config file, which are then used by the atlite tool to calculate the solar resource time series.
 
+•	Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants
+Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ , methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
 
-Solar thermal collectors
--------------------------
+**Existing heating capacities and decommissioning**
 
-Thermal energy storage using hot water tanks
----------------------------------------------
+For the myopic transition paths, capacities already existing for technologies supplying heat are retrieved from `“Mapping and analyses of the current and future (2020 - 2030)” <https://ec.europa.eu/energy/en/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment>`_ . For the sake of simplicity, coal, oil and gas boiler capacities are assimilated to gas boilers. Besides that, existing capacities for heat resistors, air-sourced and ground-sourced heat pumps are included in the model. For heating capacities, 25% of existing capacities in 2015 are assumed to be decommissioned in every 5-year time step after 2020.
 
-Small for decentral applications.
+**Thermal Energy Storage** (Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option)
 
-Big water pit storage for district heating.
+Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modeled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
+A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
-.. _retro:
+**Retrofitting of the thermal envelope of buildings**
 
-Retrofitting of the thermal envelope of buildings
-===================================================
-Co-optimising building renovation is only enabled if in the ``config.yaml`` the
-option :mod:`retro_endogen: True`. To reduce the computational burden
-default setting is
-
-.. literalinclude:: ../config.default.yaml
-    :language: yaml
-    :lines: 134-135
+Co-optimising building renovation is only enabled if in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_ file. To reduce the computational burden,
+default setting is set as false
 
 Renovation of the thermal envelope reduces the space heating demand and is
 optimised at each node for every heat bus. Renovation measures through additional
 insulation material and replacement of energy inefficient windows are considered.
 
-In a first step, costs per energy savings are estimated in :mod:`build_retro_cost.py`.
+In a first step, costs per energy savings are estimated in the `build_retro_cost.py <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_retro_cost.py>`_ script.
 They depend on the insulation condition of the building stock and costs for
 renovation of the building elements.
 In a second step, for those cost per energy savings two possible renovation
@@ -132,18 +157,13 @@ strengths are determined: a moderate renovation with lower costs and lower
 maximum possible space heat savings, and an ambitious renovation with associated
 higher costs and higher efficiency gains. They are added by step-wise
 linearisation in form of two additional generations in
-:mod:`prepare_sector_network.py`.
+the  `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L1600>`_  script.
 
 Settings in the config.yaml concerning the endogenously optimisation of building
-renovation
+renovation include `cost factor <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L223>`_, `interest rate <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L224>`_, `annualised cost <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L225>`_, `tax weighting <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L226>`_, and `construction index <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L227>`_.
 
-.. literalinclude:: ../config.default.yaml
-    :language: yaml
-    :lines: 136-140
+Further information are given in the study by Zeyen et al. : `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
 
-Further information are given in the publication
-
-`Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
 
 Hydrogen demand
 =============================

From b565730617b932a63d0e361b28ad6ff4b3aaed05 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:11:31 +0200
Subject: [PATCH 081/243] Industry section overview--including figures

---
 doc/supply_demand.rst | 60 +++++++++++++++++++++++++++++++------------
 1 file changed, 43 insertions(+), 17 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index b2b1e4eb..c682bb9f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -233,14 +233,11 @@ The following figure shows the unclustered European gas transmission network bas
 .. image:: ../graphics/gas_pipeline_figure.png
 
 
-Biomass
-============
-
-Biomass supply
----------------
+Biomass Supply
+=====================
 Biomass supply potentials for each European country are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
 
-Solid biomass demand
+Biomass demand
 =====================
 
 
@@ -254,19 +251,19 @@ Feedstocks categorized as solid biomass, e.g. secondary forest residues or munic
 Feedstocks labeled as not included are ignored by the model.
 A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be the medium availability scenario for 2030 where only residues from agriculture and forestry as well as biodegradable municipal waste are considered as energy feedstocks. Fuel crops are avoided because they compete with scarce land for food production, while primary wood, as well as wood chips and pellets, are avoided because of concerns about sustainability . See the supporting materials of the `paper <https://www.sciencedirect.com/science/article/pii/S1364032117302034>`_ for more details.
 
-Solid biomass conversion and use
-----------------------------------
+*Solid biomass conversion and use*
+
 Solid biomass can be used directly to provide process heat up to 500 C in the industry. It can also be burnt in CHP plants and boilers associated with heating systems. These technologies are described elsewhere [link to heat and industry sections].
 
 Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture [link to technology data].
 Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture [link to technology data].
 
-Transport of solid biomass
----------------------------
+*Transport of solid biomass*
+
 The transport of solid biomass can either be assumed unlimited between countries or it can be associated with a country specific cost per MWh/km. In the config file these options are toggled `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. If the option is off, use of solid biomass is transport. If it is turned on, a biomass transport network will be `created <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L1803>`_ between all nodes. This network resembles road transport of biomass and the cost of transportation is a variable cost which is proportional to distance and a country specific cost per MWh/km. The latter is `estimated <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_transport_costs.py>`_ from the country specific costs per ton/km used in the publication `“The JRC-EU-TIMES model. Bioenergy potentials for EU and neighbouring countries” <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
 
-Biogas transport and use
-------------------------
+*Biogas transport and use*
+
 Biogas will be aggregated into a common European resources if a gas network is not modeled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
 The model can only use biogas by first upgrading it to natural gas quality [link to tech description] (bio methane) which is fed into the general gas network.
 
@@ -289,19 +286,48 @@ $$
 with costs as included from the `technology-data repository <https://github.com/PyPSA/technology-data/blob/master/latex_tables/tables_in_latex.pdf>`_. The waste heat from the Fischer-Tropsch process is supplied to `district heating networks <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_. The share of fossil and synthetic oil is an optimisation result depending on the techno-economic assumptions.
 
 
-Oil-based transport
-======================== 		
+*Oil-based transport*
+	
 Liquid hydrocarbons are assumed to be transported freely among the model region since future demand is predicted to be low, transport costs for liquids are low and no bottlenecks are expected. 
 
 
 Industry demand
 ================
 
-Based on materials demand from JRC-IDEES and other sources such as the USGS for ammonia.
+Industry demand is split into a dozen different sectors with specific energy demands, process
+emissions of carbon dioxide, as well as existing and prospective mitigation strategies.
 
-Industry is split into many sectors, including iron and steel, ammonia, other basic chemicals, cement, non-metalic minerals, alumuninium, other non-ferrous metals, pulp, paper and printing, food, beverages and tobacco, and other more minor sectors.
+Subsection overview (link to section overview) provides a general description of the modelling approach for the industry sector. The following subsections describe the current energy demands, available mitigation strategies, and whether mitigation is exogenously fixed or co-optimised with the other components of the model for each industry subsector in more detail. See details for Iron and Steel (link to subsection Iron and Steel), Chemicals Industry (link to subsection Chemicals Industry), Ammonia (link to subsection Ammonia), Non-metallic Mineral products (link to subsection Non-metallic products), Non-ferrous Metals (link to subsection Non-ferrous Metals), Other Industry Subsectors (link to subsection Other Industry Subsectors).
 
-Inside each country the industrial demand is distributed using the `Hotmaps Industrial Database <https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database>`_.
+*Overview*
+
+Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia (NH3), ethylene (C2H4) and methanol (CH3OH). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
+
+The overarching modelling procedure can be described as follows. First, the energy demands and process emissions for every unit of material output are estimated based on data from the `JRC-IDEES database <https://data.europa.eu/doi/10.2760/182725>`_ and the fuel and process switching described in the subsequent sections. Second, the 2050 energy demands and process emissions are calculated using the per-unit-of-material ratios based on the industry transformations and the `country-level material production in 2015 <https://data.europa.eu/doi/10.2760/182725>`_, assuming constant material demand.
+
+Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_,  `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_. 
+
+
+Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane. 
+
+Hydrogen for high-temperature process heat is not part of the model currently. 
+
+Where process heat is required, our approach depends on the necessary temperature. For example, due to the high share of high-temperature process heat demand  (see `Naegler et al. <https://doi.org/10.1002/er.3436>`_ and `Rehfeldt el al. <https://link.springer.com/article/10.1007/s12053-017-9571-y>`_), we disregard geothermal and solar thermal energy as sources for process heat since they cannot attain high-temperature heat. 
+
+The following figure shows the final consumption of energy and non-energy feedstocks in industry today in comparison to the scenario in 2050 assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_. 
+
+.. image:: ../graphics/fec_industry_today_tomorrow.png
+
+
+The following figure shows the process emissions in industry today (top bar) and in 2050 without
+carbon capture (bottom bar) assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_. 
+
+.. image:: ../graphics/process-emissions.png
+
+
+Inside each country the industrial demand is then distributed using the `Hotmaps Industrial Database <https://zenodo.org/record/4687147#.YvOaxhxBy5c>`_, which is illustrated in the figure below. This open database includes georeferenced industrial sites of energy-intensive industry sectors in EU28, including cement, basic chemicals, glass, iron and steel, non-ferrous metals, non-metallic minerals, paper, and refineries subsectors. The use of this spatial dataset enables the calculation of regional and process-specific energy demands. This approach assumes that there will be no significant migration of energy-intensive industries.
+
+.. image:: ../graphics/hotmaps.png
 
 
 Industry supply

From 1975430a73185620df310e2ba1bca450183aa2ed Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:12:27 +0200
Subject: [PATCH 082/243] Adding png files to graphics

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diff --git a/graphics/hotmaps.png b/graphics/hotmaps.png
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From fbda00e399ff64f8106df0e5b066b7c99fd617cd Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Fri, 19 Aug 2022 11:17:08 +0200
Subject: [PATCH 083/243] Update limitations.rst

---
 doc/limitations.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/doc/limitations.rst b/doc/limitations.rst
index 9f02d763..f54813e6 100644
--- a/doc/limitations.rst
+++ b/doc/limitations.rst
@@ -35,6 +35,10 @@ See also the `GitHub repository issues <https://github.com/PyPSA/pypsa-eur-sec/i
   To avoid penny-switching the transformation of transport and
   industry away from fossil fuels is determined exogenously.
 
+- **Industry materials production constant and inelastic:**
+  For industry, the production of different materials per country is 
+  assumed to remain constant and no industry demand elasticity is included in the modelled.
+
 - **Energy demand distribution within countries:**
   Assumptions
   have been made about the distribution of demand in each country proportional to

From 7bb6197bdfbc8636d81ae8b3cb4eee96173b1c4f Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:23:18 +0200
Subject: [PATCH 084/243] Updating subsection headings in heating

---
 doc/supply_demand.rst | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index c682bb9f..ff0ee4aa 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -41,7 +41,7 @@ Heat demand
 Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
 
-•	Space heating
+*Space heating*
 The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
 
 .. literalinclude:: ../config.default.yaml
@@ -53,12 +53,13 @@ Renovation of the thermal envelope reduces the space heating demand and is optim
 In a first step, costs per energy savings are estimated in `build_retro_cost.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_retro_cost.py>`_. They depend on the insulation condition of the building stock and costs for renovation of the building elements. In a second step, for those cost per energy savings two possible renovation strengths are determined: a moderate renovation with lower costs, a lower maximum possible space heat savings, and an ambitious renovation with associated higher costs and higher efficiency gains. They are added by step-wise linearisation in form of two additional generations in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_.
 Further information are given in the publication :
  `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021)  <https://arxiv.org/abs/2012.01831>`_.
-•	Water heating
+
+*Water heating*
 Hot water demand is assumed to be constant throughout the year.
-•	Urban and rural heating
+*Urban and rural heating*
 For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
 
-•	Cooling demand
+*Cooling demand*
 Cooling is electrified and is included in the electricity demand. Cooling demand is assumed to remain at current levels.  An example of regional distribution of the total heat demand for network 181 regions is depicted below.
 
 .. image:: ../graphics/demand-map-heat.png
@@ -82,18 +83,18 @@ Heat supply
 
 Different supply options are available depending on whether demand is met centrally through district heating systems, or decentrally through appliances in individual buildings. 
 
-**Urban central heat:** 
+*Urban central heat*
 
 For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. 
 
-**Residential and Urban decentral heat:**
+*Residential and Urban decentral heat*
 
 Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
 Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `Technology-data repository <https://github.com/PyPSA/technology-data>`_.
 
 Below are more detailed explanations for each heating supply component, all of which are modeled as `Links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_. in PyPSA-Eue-Sec.
 
-•	Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
+Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
  
 CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The efficiencies of each are given on the back pressure line, where the back pressure coefficient cb is the electricity output divided by the heat output. (For a more complete explanation of the operation of CHPs refer to the study by Dahl et al. : `Cost sensitivity of optimal sector-coupled district heating production systems <https://arxiv.org/pdf/1804.07557.pdf>`_.
 
@@ -103,10 +104,10 @@ The methane CHP is modeled on the Danish Energy Agency (DEA) “Gas turbine simp
 
 NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the study by `Brown et al. <https://arxiv.org/abs/1801.05290>`_ We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
 
--  Micro-CHP 
+*Micro-CHP*
 Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
 
-•	Heat pumps
+*Heat pumps*
 The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter[refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
 $$ &Delta; T = T_(sink) − T_(source) $$ 
@@ -131,16 +132,16 @@ Solar thermal profiles are built based on weather data and also have the `option
 •	Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants
 Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ , methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
 
-**Existing heating capacities and decommissioning**
+*Existing heating capacities and decommissioning*
 
 For the myopic transition paths, capacities already existing for technologies supplying heat are retrieved from `“Mapping and analyses of the current and future (2020 - 2030)” <https://ec.europa.eu/energy/en/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment>`_ . For the sake of simplicity, coal, oil and gas boiler capacities are assimilated to gas boilers. Besides that, existing capacities for heat resistors, air-sourced and ground-sourced heat pumps are included in the model. For heating capacities, 25% of existing capacities in 2015 are assumed to be decommissioned in every 5-year time step after 2020.
 
-**Thermal Energy Storage** (Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option)
+*Thermal Energy Storage* (Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option)
 
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modeled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
 A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
-**Retrofitting of the thermal envelope of buildings**
+*Retrofitting of the thermal envelope of buildings*
 
 Co-optimising building renovation is only enabled if in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_ file. To reduce the computational burden,
 default setting is set as false

From 69c140a8b3cdcf151c4568de89949157fcbfb11f Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:26:37 +0200
Subject: [PATCH 085/243] Editing subsection headings

---
 doc/supply_demand.rst | 34 +++++++++++++++++++++++-----------
 1 file changed, 23 insertions(+), 11 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ff0ee4aa..3fda15be 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -55,6 +55,7 @@ Further information are given in the publication :
  `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021)  <https://arxiv.org/abs/2012.01831>`_.
 
 *Water heating*
+
 Hot water demand is assumed to be constant throughout the year.
 *Urban and rural heating*
 For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
@@ -105,9 +106,11 @@ The methane CHP is modeled on the Danish Energy Agency (DEA) “Gas turbine simp
 NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the study by `Brown et al. <https://arxiv.org/abs/1801.05290>`_ We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
 
 *Micro-CHP*
+
 Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
 
 *Heat pumps*
+
 The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter[refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
 $$ &Delta; T = T_(sink) − T_(source) $$ 
@@ -120,23 +123,32 @@ for ground-sourced heat pumps (GSHP), we use the function:
 
 $$  COP(&Delta; T) = 8.77 + 0.150&Delta; T + 0.000734&Delta; T^2 $$     
 
-•	Resistive heaters (can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option)
+*Resistive heaters*
+
+Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option
 Resistive heaters produce heat with a fixed conversion efficiency (refer to `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ). 
 
-•	Gas, oil, and biomass boilers (can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option)
+*Gas, oil, and biomass boilers* 
+
+Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option.
 Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas ,oil or biomass.
 
-•	Solar thermal collectors (can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option)
+*Solar thermal collectors* 
+
+Can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option.
 Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the Config file, which are then used by the atlite tool to calculate the solar resource time series.
 
-•	Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants
+*Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants*
+
 Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ , methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
 
 *Existing heating capacities and decommissioning*
 
 For the myopic transition paths, capacities already existing for technologies supplying heat are retrieved from `“Mapping and analyses of the current and future (2020 - 2030)” <https://ec.europa.eu/energy/en/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment>`_ . For the sake of simplicity, coal, oil and gas boiler capacities are assimilated to gas boilers. Besides that, existing capacities for heat resistors, air-sourced and ground-sourced heat pumps are included in the model. For heating capacities, 25% of existing capacities in 2015 are assumed to be decommissioned in every 5-year time step after 2020.
 
-*Thermal Energy Storage* (Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option)
+*Thermal Energy Storage* 
+
+Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modeled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
 A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
@@ -346,15 +358,15 @@ Transportation
 =========================
 Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
 
-Land transport
------------------
+*Land transport*
+
+
+*Aviation*
 
-Aviation
------------------
 The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
-Shipping
------------------
+*Shipping*
+
 Shipping energy demand is covered by a combination of oil and hydrogen. The amount of oil products that are converted into hydrogen follow an `exogenously defined path <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_. To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2089>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology data. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
 
 The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.

From c6413c84f597f9389fe81d92e623a38738bbe0a1 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:27:50 +0200
Subject: [PATCH 086/243] Edit of space heating subsection title

---
 doc/supply_demand.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 3fda15be..9b3e82b9 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -42,6 +42,7 @@ Building heating in residential and services sectors is resolved regionally, bot
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
 
 *Space heating*
+
 The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
 
 .. literalinclude:: ../config.default.yaml

From 3416a1a5b3e314291d2aadd7fe5dbf80cd9e5cd9 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:43:35 +0200
Subject: [PATCH 087/243] Industry sector--chemicals

---
 doc/supply_demand.rst | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 9b3e82b9..a861dbbe 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -344,16 +344,26 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 .. image:: ../graphics/hotmaps.png
 
 
-Industry supply
-================
+*Iron and Steel*
 
-Process switching (e.g. from blast furnaces to direct reduction and electric arc furnaces for steel) is defined exogenously.
+*Chemicals Industry*
 
-Fuel switching for process heat is mostly also done exogenously.
+The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
 
-Solid biomass is used for up to 500 Celsius, mostly in paper and pulp and food and beverages.
+The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
+				
+The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (LINK TO HYDROGEN SUPPLY), for methane (LINK TO METHANE SUPPLY), and for oil-based products (LINK TO OIL-BASED PRODUCTS SUPPLY). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the opti- misation.
+					
+The basic chemicals consumption data from the `JRC IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ database comprises high- value chemicals (ethylene, propylene and BTX), chlorine, methanol and ammonia. However, it is necessary to separate out these chemicals because their current and future production routes are different.
+					
+Statistics for the production of ammonia, which is commonly used as a fertilizer, are taken from the `USGS <https://www.usgs.gov/media/files/nitrogen-2017-xlsx>`_ for every country. Ammonia can be made from hydrogen and nitrogen using the Haber-Bosch process.
+					
+$$
+N_2 + 3H_2 → 2NH_3
+$$
 
-Higher temperatures are met with methane.
+
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ($6.5 MWh_{H_2}$/$t_{NH_3}$) and electricity (1.17 MWhel/tNH3) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
 Transportation
 =========================

From 8fef2f094041757ef5ab52dfbce440809b576aed Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:44:22 +0200
Subject: [PATCH 088/243] Editing eq in chemicals

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index a861dbbe..bee7f757 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -363,7 +363,7 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ($6.5 MWh_{H_2}$/$t_{NH_3}$) and electricity (1.17 MWhel/tNH3) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ($$6.5 MWh_{H_2}$$/$$t_{NH_3}$$) and electricity (1.17 MWhel/tNH3) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
 Transportation
 =========================

From 0c4d948d5bc318b3f4dc785063d091d56f7ba2a2 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:45:40 +0200
Subject: [PATCH 089/243] Editing eq

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index bee7f757..f0633b15 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -363,7 +363,7 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ($$6.5 MWh_{H_2}$$/$$t_{NH_3}$$) and electricity (1.17 MWhel/tNH3) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $$6.5 MWh_{H_2}$$ / $t_{NH_3}$ ) and electricity (1.17 MWhel/tNH3) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
 Transportation
 =========================

From f217c3197438880224d93d9cc0762bb4fc8227c9 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Fri, 19 Aug 2022 11:46:28 +0200
Subject: [PATCH 090/243] add perfect

---
 doc/index.rst   | 1 +
 doc/perfect.rst | 9 +++++++++
 2 files changed, 10 insertions(+)
 create mode 100644 doc/perfect.rst

diff --git a/doc/index.rst b/doc/index.rst
index fd9cac6c..3c25815b 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -134,6 +134,7 @@ Documentation
 
 * :doc:`overnight`
 * :doc:`myopic`
+* :doc:`perfect`
 
 .. toctree::
    :hidden:
diff --git a/doc/perfect.rst b/doc/perfect.rst
new file mode 100644
index 00000000..d7ca9cc0
--- /dev/null
+++ b/doc/perfect.rst
@@ -0,0 +1,9 @@
+.. _perfect:
+
+##########################################
+Perfect foresight scenarios
+##########################################
+
+Perfect foresight is currently under development but it is not yet implemented.
+
+For this, use ``foresight : 'perfect'`` in ``config.yaml``.

From 9457704c18059d7ef4c11ffc970970d085082f80 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:47:17 +0200
Subject: [PATCH 091/243] editing eq

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index f0633b15..5fe378a5 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -363,7 +363,7 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $$6.5 MWh_{H_2}$$ / $t_{NH_3}$ ) and electricity (1.17 MWhel/tNH3) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
 Transportation
 =========================

From 3e884ed58bcc2b9c39904786a861b38bbe30c7fc Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 11:48:25 +0200
Subject: [PATCH 092/243] parentheses

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 5fe378a5..82fed45b 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -363,7 +363,7 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_. Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
 Transportation
 =========================

From c0c8d92a0bcfacaa0045c39fdaa6c997863714b0 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:31:55 +0200
Subject: [PATCH 093/243] Update spatial_resolution.rst

---
 doc/spatial_resolution.rst | 59 ++++++++++++++------------------------
 1 file changed, 21 insertions(+), 38 deletions(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index d410065a..1de39ba8 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -4,55 +4,38 @@
 Spatial resolution
 ##########################################
 
-The default nodal resolution of the model follows the electricity
-generation and transmission model `PyPSA-Eur
-<https://github.com/PyPSA/pypsa-eur>`_, which clusters down the
-electricity transmission substations in each European country based on
-the k-means algorithm. This gives nodes which correspond to major load
-and generation centres (typically cities).
+The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ , which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities).
 
-The total number of nodes for Europe is set in the ``config.yaml`` file
-under ``clusters``. The number of nodes can vary between 37, the number
-of independent countries / synchronous areas, and several
-hundred. With 200-300 nodes the model needs 100-150 GB RAM to solve
-with a commerical solver like Gurobi.
+Exemplary unsolved network clustered to left:512 nodes and right: 37 nodes
 
+The total number of nodes for Europe is set in the config.yaml file under `clusters <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L20>`_.  The number of nodes can vary between 37, the number of independent countries/ synchronous areas, and several hundred. With 200-300 nodes, the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
+Not all of the sectors are at the full nodal resolution, and some demand for some sectors is distributed to nodes using heuristics (Example?) that need to be corrected. Some networks (Example?) are copper-plated to reduce computational times.
 
-Not all of the sectors are at the full nodal resolution, and some
-demand for some sectors is distributed to nodes using heuristics that
-need to be corrected. Some networks are copper-plated to reduce
-computational times.
+Here are some examples of how spatial resolution is set for different sectors in PyPSA-Eur-Sec:
 
-For example:
+•	Electricity network: Modeled as nodal
 
-Electricity network: nodal.
+•	Electricity residential and commercial demand: Modeled as nodal, distributed in each country based on population and GDP.
 
-Electricity residential and commercial demand: nodal, distributed in
-each country based on population and GDP.
+•	Electricity distribution network: Not included in the model, but a link per node can be used to represent energy transferred between distribution and transmission levels. (explained more in detail below)
 
-Electricity demand in industry: based on the location of industrial
-facilities from `HotMaps database <https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database>`_.
+•	Residential and commercial building heating demand: Modeled as nodal, distributed in each country based on population.
 
-Building heating demand: nodal, distributed in each country based on
-population.
+•	Electricity demand in industry: Modeled as nodal, based on the location of industrial facilities from HotMaps database.
 
-Industry demand: nodal, distributed in each country based on
-locations of industry from `HotMaps database <https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database>`_.
+•	Industry demand (heat, chemicals, etc.) : Modeled as nodal, distributed in each country based on locations of industry from HotMaps database.
+•	Hydrogen network: Modeled as nodal (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L260>`_ file).
 
-Hydrogen network: nodal.
+•	Methane network: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_ . One node can be considered reasonable since future demand is expected to be low and no bottlenecks are expected. Also, the nodally resolved methane grid is based on SciGRID_gas data.
 
-Methane network: single node for Europe, since future demand is so low and no
-bottlenecks are expected. Optionally, if for example retrofitting from fossil
-gas to hydrogen is to be considered, the methane grid can be nodally resolved
-based on SciGRID_gas data.
+•	Solid biomass: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. Nodal modeling includes modeling biomass potential per country (given per country, then distributed by population density within) and the transport of solid biomass between countries.
 
-Solid biomass: choice between single node for Europe and nodal where biomass
-potential is regionally disaggregated (currently given per country,
-then distributed by population density within)
-and transport of solid biomass is possible.
+•	CO2: It can be modeled as a single node for Europe or it can be nodally resolved with CO2 transport pipelines if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_ . It should mentioned that in single node mode a transport and storage cost is added for sequestered CO2, the cost of which can be adjusted in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_ file. 
 
-CO2:  single node for Europe, but a transport and storage cost is added for
-sequestered CO2. Optionally: nodal, with CO2 transport via pipelines.
+•	Liquid hydrocarbons: Modeled as a single node for Europe, since transport costs for liquids are low and no bottlenecks are expected.
 
-Liquid hydrocarbons: single node for Europe, since transport costs for
-liquids are low.
+**Electricity distribution network**
+
+Contrary to the transmission grid, the grid topology at the distribution level (at and below 110 kV) is not included due to very high computational burden. However, a link per node can be used (activated in `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L257>`_ file) to represent energy transferred between distribution and transmission levels. So basically, only the total capacity from the transmission grid down to the low-voltage level is optimized. The Cost of this link between transmission level and distribution level can be adjusted from zero to any number in Config file `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L258>`_ , and is currently assumed to be 500 eur/kW. 
+
+Rooftop PV, heat pumps, resistive heater, home batteries and chargers for passenger cars are connected to low-voltage level. All the remaining generation and storage technologies are connected to the transmission grid. In practice this means that the distribution grid capacity is only extended if it is necessary to balance the mismatch between local generation and demand. 

From 87f1078d1db29fd92775d4416a9b82e8eb8deb9c Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:32:57 +0200
Subject: [PATCH 094/243] Add files via upload

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From 59d213b4c0d897597e8b72e503555f572cd7d4f0 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:33:49 +0200
Subject: [PATCH 095/243] Biomass with Marta edits

---
 doc/supply_demand.rst | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 82fed45b..3d80d51d 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -255,23 +255,32 @@ Biomass demand
 =====================
 
 
-The desired scenario can be selected in the pypsa-eur-sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JREC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
+Biomass supply potentials for every NUTS2 region are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and different availability scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_. The data for NUTS2 regions is mapped to PyPSA-Eur-Sec model regions in proportion to the area overlap.
 
-The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the categories : biogas, solid biomass, and not included.
-Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas (that is upgraded to biomethane). More details below.
 
-Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste can be used directly or converted to gas or liquid fuels. More details below.
+The desired scenario can be selected in the pypsa-eur-sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JRC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
+
+
+The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the following categories: biogas, solid biomass, and not included.Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas, which can be upgraded to biomethane. Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste . are available for combustion in combined-heat-and power (CHP) plants and for medium temperature heat (below 500 °C) applications in industry. It can also converted to gas or liquid fuels.
+
 
 Feedstocks labeled as not included are ignored by the model.
-A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be the medium availability scenario for 2030 where only residues from agriculture and forestry as well as biodegradable municipal waste are considered as energy feedstocks. Fuel crops are avoided because they compete with scarce land for food production, while primary wood, as well as wood chips and pellets, are avoided because of concerns about sustainability . See the supporting materials of the `paper <https://www.sciencedirect.com/science/article/pii/S1364032117302034>`_ for more details.
+
+
+A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be the medium availability scenario for 2030 where only residues from agriculture and forestry as well as biodegradable municipal waste are considered as energy feedstocks. Fuel crops are avoided because they compete with scarce land for food production, while primary wood, as well as wood chips and pellets, are avoided because of concerns about sustainability. See the supporting materials of the `paper <https://www.sciencedirect.com/science/article/pii/S1364032117302034>`_ for more details.
+
 
 *Solid biomass conversion and use*
 
-Solid biomass can be used directly to provide process heat up to 500 C in the industry. It can also be burnt in CHP plants and boilers associated with heating systems. These technologies are described elsewhere [link to heat and industry sections].
+Solid biomass can be used directly to provide process heat up to 500˚C in the industry. It can also be burned in CHP plants and boilers associated with heating systems. These technologies are described elsewhere [link to heat and industry sections].
+
 
 Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture [link to technology data].
+
+
 Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture [link to technology data].
 
+
 *Transport of solid biomass*
 
 The transport of solid biomass can either be assumed unlimited between countries or it can be associated with a country specific cost per MWh/km. In the config file these options are toggled `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. If the option is off, use of solid biomass is transport. If it is turned on, a biomass transport network will be `created <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L1803>`_ between all nodes. This network resembles road transport of biomass and the cost of transportation is a variable cost which is proportional to distance and a country specific cost per MWh/km. The latter is `estimated <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_transport_costs.py>`_ from the country specific costs per ton/km used in the publication `“The JRC-EU-TIMES model. Bioenergy potentials for EU and neighbouring countries” <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.

From 0bfab39a4e732355c2c0012c72af7fa091dd9b68 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:35:49 +0200
Subject: [PATCH 096/243] Update spatial_resolution.rst

---
 doc/spatial_resolution.rst | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 1de39ba8..dd977cff 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -6,7 +6,13 @@ Spatial resolution
 
 The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ , which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities).
 
-Exemplary unsolved network clustered to left:512 nodes and right: 37 nodes
+Exemplary unsolved network clustered to 512 nodes:
+
+.. image:: ../graphics/elec_s_512.png 
+
+Exemplary unsolved network clustered to 37 nodes:
+
+.. image:: ../graphics/elec_s_37.png
 
 The total number of nodes for Europe is set in the config.yaml file under `clusters <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L20>`_.  The number of nodes can vary between 37, the number of independent countries/ synchronous areas, and several hundred. With 200-300 nodes, the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
 Not all of the sectors are at the full nodal resolution, and some demand for some sectors is distributed to nodes using heuristics (Example?) that need to be corrected. Some networks (Example?) are copper-plated to reduce computational times.

From d8a6e2134e72a54aaa7bcdf6d67e2219dd61de38 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Fri, 19 Aug 2022 13:35:50 +0200
Subject: [PATCH 097/243] Updated the Shipping section

Updated the Shipping section to the most recent version. The (small) changes include the review comments.
---
 .DS_Store             | Bin 0 -> 6148 bytes
 doc/supply_demand.rst |  11 +++++++----
 2 files changed, 7 insertions(+), 4 deletions(-)
 create mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
new file mode 100644
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GIT binary patch
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diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index db7d23dc..26528eef 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -255,12 +255,15 @@ Aviation
 The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
 Shipping
------------------
-Shipping energy demand is covered by a combination of oil and hydrogen. The amount of oil products that are converted into hydrogen follow an `exogenously defined path <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_. To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2089>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology data. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
+----------------
+Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec.The share of shipping that is assumed to be supplied by hydrogen can be selected in the config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
 
-The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.
+To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
+
+The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e., is connected to the general oil bus.
+
+The energy demand for liquefaction of the hydrogen used for shipping can be `included  <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
 
-The user can toggle if the energy demand for liquefaction of the hydrogen used for shipping should be `included or not  <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
 
 
 Carbon dioxide capture, usage and sequestration (CCU/S)

From 6e2a6c8a5e03a157ab0269abad3684a8d801aacd Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:37:51 +0200
Subject: [PATCH 098/243] Small edit to heating

---
 doc/supply_demand.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 3d80d51d..d7ce274a 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -58,7 +58,9 @@ Further information are given in the publication :
 *Water heating*
 
 Hot water demand is assumed to be constant throughout the year.
+
 *Urban and rural heating*
+
 For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
 
 *Cooling demand*

From 817a0894bb1e4f59c47c68041f38601c34a24444 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:43:06 +0200
Subject: [PATCH 099/243] Update spatial_resolution.rst

---
 doc/spatial_resolution.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index dd977cff..de638598 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -15,7 +15,7 @@ Exemplary unsolved network clustered to 37 nodes:
 .. image:: ../graphics/elec_s_37.png
 
 The total number of nodes for Europe is set in the config.yaml file under `clusters <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L20>`_.  The number of nodes can vary between 37, the number of independent countries/ synchronous areas, and several hundred. With 200-300 nodes, the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
-Not all of the sectors are at the full nodal resolution, and some demand for some sectors is distributed to nodes using heuristics (Example?) that need to be corrected. Some networks (Example?) are copper-plated to reduce computational times.
+Not all of the sectors are at the full nodal resolution, and some demand for some sectors is distributed to nodes using heuristics that need to be corrected. Some networks are copper-plated to reduce computational times.
 
 Here are some examples of how spatial resolution is set for different sectors in PyPSA-Eur-Sec:
 
@@ -23,7 +23,7 @@ Here are some examples of how spatial resolution is set for different sectors in
 
 •	Electricity residential and commercial demand: Modeled as nodal, distributed in each country based on population and GDP.
 
-•	Electricity distribution network: Not included in the model, but a link per node can be used to represent energy transferred between distribution and transmission levels. (explained more in detail below)
+•	Electricity distribution network: Not included in the model, but a link per node can be used to represent energy transferred between distribution and transmission levels (explained more in detail below).
 
 •	Residential and commercial building heating demand: Modeled as nodal, distributed in each country based on population.
 

From 9518d35381ca875d8f0fd40ce1110acc8896ff76 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Fri, 19 Aug 2022 13:49:25 +0200
Subject: [PATCH 100/243] add file to describe technology assumptions

---
 doc/index.rst                  | 4 +++-
 doc/technology_assumptions.rst | 7 +++++++
 2 files changed, 10 insertions(+), 1 deletion(-)
 create mode 100644 doc/technology_assumptions.rst

diff --git a/doc/index.rst b/doc/index.rst
index 3c25815b..d366f5b7 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -120,6 +120,7 @@ Documentation
 
 * :doc:`spatial_resolution`
 * :doc:`supply_demand`
+* :doc:`technology_assumptions`
 
 .. toctree::
    :hidden:
@@ -128,7 +129,7 @@ Documentation
 
    spatial_resolution
    supply_demand
-
+   technology_assumptions
 
 **Foresight options**
 
@@ -143,6 +144,7 @@ Documentation
 
    overnight
    myopic
+   perfect
 
 **References**
 
diff --git a/doc/technology_assumptions.rst b/doc/technology_assumptions.rst
new file mode 100644
index 00000000..e179e78f
--- /dev/null
+++ b/doc/technology_assumptions.rst
@@ -0,0 +1,7 @@
+.. _technology_assumptions:
+
+##########################################
+Technology and cost assumptions
+##########################################
+
+Add text here.

From f229a5c37424c3f4b80f4997b599b7cb755b4d4d Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Fri, 19 Aug 2022 13:52:34 +0200
Subject: [PATCH 101/243] Updated the aviation section

Very minor update of the aviation section.
---
 doc/supply_demand.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 3ea3d5e9..cd3c44ae 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -374,11 +374,11 @@ Annual energy demands for land transport, aviation and shipping for every countr
 
 *Aviation*
 
-The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
+The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
 
 *Shipping*
-----------------
+
 Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec.The share of shipping that is assumed to be supplied by hydrogen can be selected in the config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
 
 To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.

From 8c738437df6a193b99b99caebe881b4f642f0755 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:53:52 +0200
Subject: [PATCH 102/243] Test of link to other section

---
 doc/supply_demand.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index d7ce274a..fccbc3b7 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -405,3 +405,5 @@ plants, and directly from the air (DAC).
 
 Carbon dioxide can be used as an input for methanation and
 Fischer-Tropsch fuels, or it can be sequestered underground.
+
+[Heat demand](#heat-demand)

From 0a7c3bbb86524fcfe0b1f23ecb5658cf25941a03 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:54:38 +0200
Subject: [PATCH 103/243] Testing

---
 doc/supply_demand.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index fccbc3b7..7eb9a23c 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -36,8 +36,8 @@ Also unlike PyPSA-Eur, PyPSA-Eur-Sec subtracts existing electrified heating from
 The remaining electricity demand for households and services is distributed inside each country proportional to GDP and population.
 
 
-Heat demand
-=============================
+#Heat demand
+
 Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
 

From 44248986c22e48e2970f8f421e6b04a165fd4ce9 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:55:00 +0200
Subject: [PATCH 104/243] Testing

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 7eb9a23c..d90533fd 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -36,7 +36,7 @@ Also unlike PyPSA-Eur, PyPSA-Eur-Sec subtracts existing electrified heating from
 The remaining electricity demand for households and services is distributed inside each country proportional to GDP and population.
 
 
-#Heat demand
+# Heat demand
 
 Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  

From 0477a84e33569e0cbfca444c7cd5b096f271cdb8 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:56:33 +0200
Subject: [PATCH 105/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 37 ++++++++++++++++++++++++++++++++-----
 1 file changed, 32 insertions(+), 5 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 82fed45b..9d5b102b 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -388,9 +388,36 @@ The user can toggle if the energy demand for liquefaction of the hydrogen used f
 Carbon dioxide capture, usage and sequestration (CCU/S)
 =========================================================
 
-Carbon dioxide can be captured from industry process emissions,
-emissions related to industry process heat, combined heat and power
-plants, and directly from the air (DAC).
+PyPSA-Eur-Sec includes carbon capture from air (i.e., direct air capture (DAC)), electricity generators and industrial facilities. It furthermore includes carbon dioxide storage and transport, the usage of carbon dioxide in synthetic methane and oil products, as well as the sequestration of carbon dioxide underground.
 
-Carbon dioxide can be used as an input for methanation and
-Fischer-Tropsch fuels, or it can be sequestered underground.
+**Carbon dioxide capture**
+
+For the following point source emissions, carbon capture is applicable: 
+
+•	Industry process emissions, e.g., from limestone in cement production
+
+•	Methane or biomass used for process heat in the industry
+
+•	Hydrogen production by SMR  
+
+•	CHP plants using biomass or methane
+
+•	`Coal power plants <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L242>`_.
+
+Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
+is currently ignored.
+
+DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO2 prior to storage which consumes electricity and rejects heat. 
+
+*Carbon dioxide usage*
+
+Captured CO2 can be used to produce synthetic methane and synthetic oil products (e.g.
+naphtha). If captured carbon is used, the CO2 emissions of the synthetic fuels are net-neutral. 
+
+*Carbon dioxide sequestration*
+
+Captured CO2 can also be sequestered underground up to an annual sequestration limit of 200 MtCO2/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO2 transport constraints are neglected. Since CO2 sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+
+*Carbon dioxide transport*
+
+Carbon dioxide can be modelled as a single node for Europe (in this case, CO2 transport constraints are neglected). A network for modelling the transport of CO2 among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 

From b2026c64480e2a98665aa168664766484056cc5a Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:57:22 +0200
Subject: [PATCH 106/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 9d5b102b..d0f59f61 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -388,7 +388,7 @@ The user can toggle if the energy demand for liquefaction of the hydrogen used f
 Carbon dioxide capture, usage and sequestration (CCU/S)
 =========================================================
 
-PyPSA-Eur-Sec includes carbon capture from air (i.e., direct air capture (DAC)), electricity generators and industrial facilities. It furthermore includes carbon dioxide storage and transport, the usage of carbon dioxide in synthetic methane and oil products, as well as the sequestration of carbon dioxide underground.
+PyPSA-Eur-Sec includes carbon capture from air (i.e., direct air capture (DAC)), electricity generators, and industrial facilities. It furthermore includes carbon dioxide storage and transport, the usage of carbon dioxide in synthetic methane and oil products, as well as the sequestration of carbon dioxide underground.
 
 **Carbon dioxide capture**
 

From fbb6acdc7cf5d520c5a56ac985f36c0f7ce55988 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 13:58:59 +0200
Subject: [PATCH 107/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index d90533fd..2cb30795 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -36,7 +36,8 @@ Also unlike PyPSA-Eur, PyPSA-Eur-Sec subtracts existing electrified heating from
 The remaining electricity demand for households and services is distributed inside each country proportional to GDP and population.
 
 
-# Heat demand
+Heat demand
+===========
 
 Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
@@ -406,4 +407,4 @@ plants, and directly from the air (DAC).
 Carbon dioxide can be used as an input for methanation and
 Fischer-Tropsch fuels, or it can be sequestered underground.
 
-[Heat demand](#heat-demand)
+test <a href="doc/supply_demand.rst#L397">

From e68105584c61898c6bce07c8754ccd50ea2d0c9b Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 14:00:10 +0200
Subject: [PATCH 108/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 2cb30795..4f74f58c 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -407,4 +407,4 @@ plants, and directly from the air (DAC).
 Carbon dioxide can be used as an input for methanation and
 Fischer-Tropsch fuels, or it can be sequestered underground.
 
-test <a href="doc/supply_demand.rst#L397">
+[test] <a href="doc/supply_demand.rst#L397">

From 8789f2c26b1022f48e1df15cf297bdd4a7d33501 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 19 Aug 2022 14:02:04 +0200
Subject: [PATCH 109/243] Update technology_assumptions.rst

---
 doc/technology_assumptions.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/doc/technology_assumptions.rst b/doc/technology_assumptions.rst
index e179e78f..2a42420e 100644
--- a/doc/technology_assumptions.rst
+++ b/doc/technology_assumptions.rst
@@ -4,4 +4,6 @@
 Technology and cost assumptions
 ##########################################
 
-Add text here.
+*Techno-Economic Assumptions*
+
+For the technological assumptions (cost,efficiency, lifetime, etc.), we take estimates for the investment year specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L43>`_. Many of those come from a database published by the Danish Energy Agency (`DEA <https://ens.dk/en/our-services/projections-and-models/technology-data>`_). Assumptions are maintained at the `technology data repository <https://github.com/PyPSA/technology-data>`_.

From 44db7bc23343ea593c448c71f8358ede74d18ee4 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 14:02:27 +0200
Subject: [PATCH 110/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 4f74f58c..47c6416e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -407,4 +407,4 @@ plants, and directly from the air (DAC).
 Carbon dioxide can be used as an input for methanation and
 Fischer-Tropsch fuels, or it can be sequestered underground.
 
-[test] <a href="doc/supply_demand.rst#L397">
+[test] <a href="pypsa-eur-sec/doc/supply_demand.rst">doc/supply_demand.rst</a>

From 5d5ecd73b6f6773a8b4e96a6fe9a5bfa7f4659f4 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 19 Aug 2022 14:06:18 +0200
Subject: [PATCH 111/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 47c6416e..7273d926 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -407,4 +407,4 @@ plants, and directly from the air (DAC).
 Carbon dioxide can be used as an input for methanation and
 Fischer-Tropsch fuels, or it can be sequestered underground.
 
-[test] <a href="pypsa-eur-sec/doc/supply_demand.rst">doc/supply_demand.rst</a>
+[test] (supply_demand.rst?plain=1#L405)

From aa3b1f957ca0770682064812f59ac561291ef8d5 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Fri, 19 Aug 2022 16:10:02 +0200
Subject: [PATCH 112/243] Description of land transport

The description of land transport is added. It needs a review since that has not been done yet.
---
 doc/supply_demand.rst | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index cd3c44ae..0d4612f5 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -371,6 +371,31 @@ Annual energy demands for land transport, aviation and shipping for every countr
 
 *Land transport*
 
+Both road and rail transport is combined as `land transport demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_transport_demand.py#L74>`_ although electrified rail transport is excluded because the energy is included in the electricity demand.
+
+The most important settings for land transport are the exogenously fixed fuel mix. In the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_, the share of battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCEV) can be set. The remaining percentage will be treated as internal combustion engines (ICE) that consume oil products.
+
+*Electric vehicles (BEV)*
+
+For the electrified land transport, country-specific factors are computed by comparing the `current car final energy consumption per km in <https://www.sciencedirect.com/science/article/pii/S0360544216310295>`_ (average for Europe 0.7 kWh/km) to the 0.18 kWh/km value assumed for battery-to-wheels efficiency in EVs. The characteristic `weekly profile <https://doi.org/10.1057/s41302-016-0009-6>`_ provided by the German Federal Highway Research Institute (BASt) is used to obtain hourly time series for European countries taking into account the corresponding local times. Furthermore, a temperature dependence is included in the time series to account for heating/cooling demand in transport. For temperatures `below <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L166>`_/`above <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L165>`_ certain threshold values, e.g. 15C/20C, `temperature coefficients <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L169>`_ of typically 0.98%/C and 0.63%/C are assumed, based on the `paper <https://www.sciencedirect.com/science/article/pii/S036054421831288X>`_.
+
+For BEVs the user can define the `storage energy capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L173>`_, `charging power capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L176>`_, and `charging efficiency <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L174>`_.
+
+For BEV, smart charging is an option. A `certain share <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L172>`_ of the BEV fleet can shift their charging time. The BEV state of charge is forced to be higher than a `set percentage <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L163>`_, e.g. 75%, every day at a `specified hour <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L164>`_, e.g., 7 am, to ensure that the batteries are sufficiently charged for peak usage in the morning. They also have the option to participate in vehicle-to-grid (V2G) services to facilitate system operation if that `is enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L179>`_
+
+The battery cost of BEV is not included in the model since it is assumed that BEV owners buy them to primarily satisfy their mobility needs.
+
+*Hydrogen fuel cell vehicles (FCEV)*
+
+The share of all land transport that is specified to be be FCEV will be converted to a demand for hydrogen [link to hydrogen] using the `FCEV effeciency
+<https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L191>`_.
+
+FCEVs are typically used to simulate demand for transport that is hard to electrify directly, e.g. heavy construction machinery. But it may also be used to investigate a more widespread adoption of the technology.
+
+*Internal combustion engine vehicles (ICE)* Ready for review
+
+All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products [link to oil products] using the `ICE effeciency
+<https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L192>`_.
 
 *Aviation*
 

From 79ed33aa0a4cb17861bd604b039a5fc2c37f54f4 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Fri, 19 Aug 2022 16:10:36 +0200
Subject: [PATCH 113/243] Small change to land transport

Very minor change
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 0d4612f5..16ca40cb 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -392,7 +392,7 @@ The share of all land transport that is specified to be be FCEV will be converte
 
 FCEVs are typically used to simulate demand for transport that is hard to electrify directly, e.g. heavy construction machinery. But it may also be used to investigate a more widespread adoption of the technology.
 
-*Internal combustion engine vehicles (ICE)* Ready for review
+*Internal combustion engine vehicles (ICE)* 
 
 All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products [link to oil products] using the `ICE effeciency
 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L192>`_.

From 18c31495a8ebc9f747bc98f82c7f8701cab68dae Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 24 Aug 2022 09:43:22 +0200
Subject: [PATCH 114/243] Remove test

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 7273d926..ff1ce5a0 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -407,4 +407,4 @@ plants, and directly from the air (DAC).
 Carbon dioxide can be used as an input for methanation and
 Fischer-Tropsch fuels, or it can be sequestered underground.
 
-[test] (supply_demand.rst?plain=1#L405)
+

From d859738b7d92ab557e607f151f3a7214ad8cb3d6 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 24 Aug 2022 09:45:34 +0200
Subject: [PATCH 115/243] Incorporating Marta changes

---
 doc/supply_demand.rst | 35 ++++++++++++++++++++++++++++++-----
 1 file changed, 30 insertions(+), 5 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ff1ce5a0..72a4c3a2 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -400,11 +400,36 @@ The user can toggle if the energy demand for liquefaction of the hydrogen used f
 Carbon dioxide capture, usage and sequestration (CCU/S)
 =========================================================
 
-Carbon dioxide can be captured from industry process emissions,
-emissions related to industry process heat, combined heat and power
-plants, and directly from the air (DAC).
+PyPSA-Eur-Sec includes carbon capture from air (i.e., direct air capture (DAC)), electricity generators, and industrial facilities. It furthermore includes carbon dioxide storage and transport, the usage of carbon dioxide in synthetic methane and oil products, as well as the sequestration of carbon dioxide underground.
 
-Carbon dioxide can be used as an input for methanation and
-Fischer-Tropsch fuels, or it can be sequestered underground.
+**Carbon dioxide capture**
 
+For the following point source emissions, carbon capture is applicable: 
 
+•	Industry process emissions, e.g., from limestone in cement production
+
+•	Methane or biomass used for process heat in the industry
+
+•	Hydrogen production by SMR  
+
+•	CHP plants using biomass or methane
+
+•	`Coal power plants <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L242>`_.
+
+Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
+is currently ignored.
+
+DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO2 prior to storage which consumes electricity and rejects heat. 
+
+*Carbon dioxide usage*
+
+Captured CO2 can be used to produce synthetic methane and synthetic oil products (e.g.
+naphtha). If captured carbon is used, the CO2 emissions of the synthetic fuels are net-neutral. 
+
+*Carbon dioxide sequestration*
+
+Captured CO2 can also be sequestered underground up to an annual sequestration limit of 200 MtCO2/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO2 transport constraints are neglected. Since CO2 sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+
+*Carbon dioxide transport*
+
+Carbon dioxide can be modelled as a single node for Europe (in this case, CO2 transport constraints are neglected). A network for modelling the transport of CO2 among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 

From 66a465aad7fc618103f03edfb13d2c23f02ccfad Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 24 Aug 2022 10:08:59 +0200
Subject: [PATCH 116/243] Eq insert in iron and steel

---
 doc/supply_demand.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index c3fe4cbf..c0cf2f4e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -357,6 +357,10 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 
 *Iron and Steel*
+$$CO_2 + C→ 2 CO$$ 
+$$ 3 Fe_2O_3 + CO → 2 Fe_3O_4 + CO$$ 
+$$Fe_3O_4 + CO −−−→ 3 FeO + CO_2$$
+$$FeO + CO −−−→ Fe + CO_2$$
 
 *Chemicals Industry*
 

From 1727fc79991955f3706dd32d7c1ed883bb91c6b7 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 24 Aug 2022 10:12:23 +0200
Subject: [PATCH 117/243] Equation edits

---
 doc/supply_demand.rst | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index c0cf2f4e..ec5c8201 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -357,10 +357,19 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 
 *Iron and Steel*
-$$CO_2 + C→ 2 CO$$ 
-$$ 3 Fe_2O_3 + CO → 2 Fe_3O_4 + CO$$ 
-$$Fe_3O_4 + CO −−−→ 3 FeO + CO_2$$
-$$FeO + CO −−−→ Fe + CO_2$$
+
+$$ 
+CO_2 + C→ 2 CO
+$$ 
+$$ 
+3 Fe_2O_3 + CO → 2 Fe_3O_4 + CO
+$$ 
+$$
+Fe_3O_4 + CO −−−→ 3 FeO + CO_2
+$$
+$$
+FeO + CO −−−→ Fe + CO_2
+$$
 
 *Chemicals Industry*
 

From 35c2e5354e7dd1dcde97c15c7440f21a05998499 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 24 Aug 2022 10:13:01 +0200
Subject: [PATCH 118/243] Equation edits

---
 doc/supply_demand.rst | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ec5c8201..f7041ad4 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -361,12 +361,15 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 $$ 
 CO_2 + C→ 2 CO
 $$ 
+
 $$ 
 3 Fe_2O_3 + CO → 2 Fe_3O_4 + CO
 $$ 
+
 $$
 Fe_3O_4 + CO −−−→ 3 FeO + CO_2
 $$
+
 $$
 FeO + CO −−−→ Fe + CO_2
 $$

From 8afa575a0c83e9f40fc793f45d2623cf705c6146 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 24 Aug 2022 10:46:05 +0200
Subject: [PATCH 119/243] Equation edits

---
 doc/supply_demand.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index f7041ad4..26db7fd0 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -358,6 +358,7 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 *Iron and Steel*
 
+
 $$ 
 CO_2 + C→ 2 CO
 $$ 

From 0f6c2cd479c16819c25572494815d22740e7a87e Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Tue, 30 Aug 2022 14:18:31 +0200
Subject: [PATCH 120/243] Inserting iron section

---
 doc/supply_demand.rst | 36 ++++++++++++++++++++++++++++++++----
 1 file changed, 32 insertions(+), 4 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 26db7fd0..81b09850 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -358,8 +358,11 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 *Iron and Steel*
 
-
-$$ 
+Two alternative routes are used today to manufacture steel in Europe. The primary route (integrated steelworks) represents 60% of steel production, while the secondary route (electric arc furnaces, EAF), represents the other 40% `(Lechtenböhmer et. al) <https://doi.org/10.1016/j.energy.2016.07.110>`_.
+					
+The primary route uses blast furnaces in which coke is used to reduce iron ore into molten iron, which is then converted into steel:
+					
+$$
 CO_2 + C→ 2 CO
 $$ 
 
@@ -368,12 +371,37 @@ $$
 $$ 
 
 $$
-Fe_3O_4 + CO −−−→ 3 FeO + CO_2
+Fe_3O_4 + CO → 3 FeO + CO_2
 $$
 
 $$
-FeO + CO −−−→ Fe + CO_2
+FeO + CO→ Fe + CO_2
 $$
+					
+The primary route of steelmaking implies large process emissions of 0.22 t $_{CO_2}$ /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_. 
+					
+In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the CO$_2$ emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t $_{CO_2}$ /t of steel.
+					
+We assume that the primary route can be replaced by a third route in 2050, using direct reduced iron (DRI) and subsequent processing in an EAF.
+					
+$$
+3 Fe_2O_3 + H_2→ 2 Fe_3O_4 + H_2O
+$$
+
+$$
+Fe_3O_4 +H_2 →3FeO+H_2O
+$$
+
+$$
+FeO + H_2 → Fe + H_2O
+$$	
+
+This circumvents the process emissions associated with the use of coke. For hydrogen- based DRI, we assume energy requirements of 1.7 MWh $_{H_2}$ /t steel `(Vogl et. al) <https://doi.org/10.1016/j.jclepro.2018.08.279>`_ and 0.322 MWh $_{el}$ /t steel `(HYBRIT 2016) <https://dh5k8ug1gwbyz.cloudfront.net/uploads/2021/02/Hybrit-broschure-engelska.pdf>`_. 
+								
+					
+The share of steel produced via the primary route is exogenously set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L279>`_. The share of steel obtained via hydrogen-based DRI plus EAF is also set exogenously in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L287>`_. The remaining share is manufactured through the secondary route using scrap metal in EAF. Bioenergy as alternative to coke in blast furnaces is not considered in the model (`Mandova et.al <https://doi.org/10.1016/j.biombioe.2018.04.021>`_, `Suopajärvi et.al <https://doi.org/10.1016/j.apenergy.2018.01.060>`_).
+					
+For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (add link to the overview section). 
 
 *Chemicals Industry*
 

From e34bd9b7fb6a9bad241b95e2f678f344652ff1d3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ebbe=20Kyhl=20G=C3=B8tske=20ekg=40mpe=2Eau=2Edk?=
 <au485969@fe1.prime.net>
Date: Wed, 31 Aug 2022 13:07:43 +0200
Subject: [PATCH 121/243] Change costs year in snakefile and config file

---
 Snakefile           | 6 +++---
 config.default.yaml | 3 ++-
 2 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/Snakefile b/Snakefile
index 6db0bca0..6b195d12 100644
--- a/Snakefile
+++ b/Snakefile
@@ -474,7 +474,7 @@ rule prepare_sector_network:
         co2="data/eea/UNFCCC_v23.csv",
         biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
         heat_profile="data/heat_load_profile_BDEW.csv",
-        costs=CDIR + "costs_{planning_horizons}.csv",
+        costs=CDIR + "costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else CDIR + "costs_{planning_horizons}.csv",
         profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
         profile_offwind_dc=pypsaeur("resources/profile_offwind-dc.nc"),
         h2_cavern="resources/salt_cavern_potentials_s{simpl}_{clusters}.csv",
@@ -539,7 +539,7 @@ rule make_summary:
             RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
             **config['scenario']
         ),
-        costs=CDIR + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
+        costs=CDIR + "costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else CDIR + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
         plots=expand(
             RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
             **config['scenario']
@@ -589,7 +589,7 @@ if config["foresight"] == "overnight":
         input:
             overrides="data/override_component_attrs",
             network=RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
-            costs=CDIR + "costs_{planning_horizons}.csv",
+            costs=CDIR + "costs_{}.csv".format(config['costs']['year']),
             config=SDIR + '/configs/config.yaml'
         output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
         shadow: "shallow"
diff --git a/config.default.yaml b/config.default.yaml
index 5f7cbb33..c964abce 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -40,7 +40,7 @@ scenario:
   # planning_horizons), be:beta decay; ex:exponential decay
   # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
   # decay with initial growth rate 0
-  planning_horizons: # investment years for myopic and perfect; or costs year for overnight
+  planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
     - 2030
   # for example, set to
   # - 2020
@@ -327,6 +327,7 @@ industry:
   # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
 
 costs:
+  year: 2030
   lifetime: 25 #default lifetime
   # From a Lion Hirth paper, also reflects average of Noothout et al 2016
   discountrate: 0.07

From dcbe5e55937086f148f071c546d3e2327c0c3bcc Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Fri, 2 Sep 2022 13:48:27 +0200
Subject: [PATCH 122/243] Update spatial_resolution.rst

---
 doc/spatial_resolution.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index de638598..24efcb12 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -42,6 +42,6 @@ Here are some examples of how spatial resolution is set for different sectors in
 
 **Electricity distribution network**
 
-Contrary to the transmission grid, the grid topology at the distribution level (at and below 110 kV) is not included due to very high computational burden. However, a link per node can be used (activated in `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L257>`_ file) to represent energy transferred between distribution and transmission levels. So basically, only the total capacity from the transmission grid down to the low-voltage level is optimized. The Cost of this link between transmission level and distribution level can be adjusted from zero to any number in Config file `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L258>`_ , and is currently assumed to be 500 eur/kW. 
+Contrary to the transmission grid, the grid topology at the distribution level (at and below 110 kV) is not included due to the very high computational burden. However, a link per node can be used (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L257>`_ file) to represent energy transferred between distribution and transmission levels at every node. In essence, the total energy capacity connecting the transmission grid and the low-voltage level is optimized. The cost assumptions for this link can be adjusted in Config file `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L258>`_ , and is currently assumed to be 500 eur/kW. 
 
-Rooftop PV, heat pumps, resistive heater, home batteries and chargers for passenger cars are connected to low-voltage level. All the remaining generation and storage technologies are connected to the transmission grid. In practice this means that the distribution grid capacity is only extended if it is necessary to balance the mismatch between local generation and demand. 
+Rooftop PV, heat pumps, resistive heater, home batteries chargers for passenger EVs, as well as individual heating technologies (heat pumps and resistive heaters)  are connected to low-voltage level. All the remaining generation and storage technologies are connected to the transmission grid. In practice, this means that the distribution grid capacity is only extended if it is necessary to balance the mismatch between local generation and demand.

From 108effd30045a86d3f9ee0d69dc0290c6fe45c94 Mon Sep 17 00:00:00 2001
From: Gorm Bruun Andresen <gba@eng.au.dk>
Date: Fri, 2 Sep 2022 14:16:10 +0200
Subject: [PATCH 123/243] Minor updates to the transport documentation

Some minor errors in the transport documentation was corrected.
---
 doc/supply_demand.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 16ca40cb..1c3652ab 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -371,30 +371,30 @@ Annual energy demands for land transport, aviation and shipping for every countr
 
 *Land transport*
 
-Both road and rail transport is combined as `land transport demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_transport_demand.py#L74>`_ although electrified rail transport is excluded because the energy is included in the electricity demand.
+Both road and rail transport is combined as `land transport demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_transport_demand.py#L74>`_ although electrified rail transport is excluded because that demand is included in the current electricity demand.
 
-The most important settings for land transport are the exogenously fixed fuel mix. In the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_, the share of battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCEV) can be set. The remaining percentage will be treated as internal combustion engines (ICE) that consume oil products.
+The most important settings for land transport are the exogenously fixed fuel mix (an option enabling the endogeous optimization of transport electrification is planned but not yet implemented) . In the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_, the share of battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCEV) can be set. The remaining percentage will be treated as internal combustion engines (ICE) that consume oil products.
 
-*Electric vehicles (BEV)*
+*Battery Electric vehicles (BEV)*
 
-For the electrified land transport, country-specific factors are computed by comparing the `current car final energy consumption per km in <https://www.sciencedirect.com/science/article/pii/S0360544216310295>`_ (average for Europe 0.7 kWh/km) to the 0.18 kWh/km value assumed for battery-to-wheels efficiency in EVs. The characteristic `weekly profile <https://doi.org/10.1057/s41302-016-0009-6>`_ provided by the German Federal Highway Research Institute (BASt) is used to obtain hourly time series for European countries taking into account the corresponding local times. Furthermore, a temperature dependence is included in the time series to account for heating/cooling demand in transport. For temperatures `below <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L166>`_/`above <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L165>`_ certain threshold values, e.g. 15C/20C, `temperature coefficients <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L169>`_ of typically 0.98%/C and 0.63%/C are assumed, based on the `paper <https://www.sciencedirect.com/science/article/pii/S036054421831288X>`_.
+For the electrified land transport, country-specific factors are computed by comparing the `current car final energy consumption per km in <https://www.sciencedirect.com/science/article/pii/S0360544216310295>`_ (average for Europe 0.7 kWh/km) to the 0.18 kWh/km value assumed for battery-to-wheels efficiency in EVs. The characteristic `weekly profile <https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/zaehl_node.html>`_ provided by the German Federal Highway Research Institute (BASt) is used to obtain hourly time series for European countries taking into account the corresponding local times. Furthermore, a temperature dependence is included in the time series to account for heating/cooling demand in transport. For temperatures `below <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L166>`_/`above <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L165>`_ certain threshold values, e.g. 15C/20C, `temperature coefficients <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L169>`_ of typically 0.98%/C and 0.63%/C are assumed, based on the `paper <https://www.sciencedirect.com/science/article/pii/S036054421831288X>`_.
 
 For BEVs the user can define the `storage energy capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L173>`_, `charging power capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L176>`_, and `charging efficiency <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L174>`_.
 
-For BEV, smart charging is an option. A `certain share <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L172>`_ of the BEV fleet can shift their charging time. The BEV state of charge is forced to be higher than a `set percentage <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L163>`_, e.g. 75%, every day at a `specified hour <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L164>`_, e.g., 7 am, to ensure that the batteries are sufficiently charged for peak usage in the morning. They also have the option to participate in vehicle-to-grid (V2G) services to facilitate system operation if that `is enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L179>`_
+For BEV, smart charging is an option. A `certain share <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L172>`_ of the BEV fleet can shift their charging time. The BEV state of charge is forced to be higher than a `set percentage <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L163>`_, e.g. 75%, every day at a `specified hour <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L164>`_, e.g., 7 am, to ensure that the batteries are sufficiently charged for peak usage in the morning and they not behave as seasonal storage. They also have the option to participate in vehicle-to-grid (V2G) services to facilitate system operation if that `is enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L179>`_
 
 The battery cost of BEV is not included in the model since it is assumed that BEV owners buy them to primarily satisfy their mobility needs.
 
 *Hydrogen fuel cell vehicles (FCEV)*
 
-The share of all land transport that is specified to be be FCEV will be converted to a demand for hydrogen [link to hydrogen] using the `FCEV effeciency
+The share of all land transport that is specified to be be FCEV will be converted to a demand for hydrogen [link to hydrogen] using the `FCEV efficiency
 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L191>`_.
 
 FCEVs are typically used to simulate demand for transport that is hard to electrify directly, e.g. heavy construction machinery. But it may also be used to investigate a more widespread adoption of the technology.
 
 *Internal combustion engine vehicles (ICE)* 
 
-All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products [link to oil products] using the `ICE effeciency
+All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products [link to oil products] using the `ICE efficiency
 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L192>`_.
 
 *Aviation*
@@ -408,7 +408,7 @@ Shipping energy demand is covered by a combination of oil and hydrogen. Other fu
 
 To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
 
-The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e., is connected to the general oil bus.
+The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.
 
 The energy demand for liquefaction of the hydrogen used for shipping can be `included  <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
 

From b3b40f75abe83df393c0cddd58435edd7aa46b17 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Fri, 2 Sep 2022 14:28:31 +0200
Subject: [PATCH 124/243] Update release_notes.rst

---
 doc/release_notes.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index abcbaad5..931ca060 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -73,6 +73,8 @@ incorporates retrofitting options to hydrogen.
 
 * Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials.
 
+* Updated and extended documentation in <https://pypsa-eur-sec.readthedocs.io/en/latest/>
+
 **Bugfixes**
 
 * The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)

From ed8f7830964e576f70815f17c1fefb448ad6cf3e Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Wed, 14 Sep 2022 16:16:50 +0200
Subject: [PATCH 125/243] include code review

---
 scripts/prepare_sector_network.py | 30 +++++++++++++++++-------------
 1 file changed, 17 insertions(+), 13 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 0bfc78e0..2a7d6ce9 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2415,7 +2415,7 @@ def limit_individual_line_extension(n, maxext):
 
 
 def apply_time_segmentation(n, segments, solver_name="cbc",
-                            overwrite_time_dependent=False):
+                            overwrite_time_dependent=True):
     """Aggregating time series to segments with different lengths
 
     Input:
@@ -2435,12 +2435,12 @@ def apply_time_segmentation(n, segments, solver_name="cbc",
     # get all time-dependent data
     columns = pd.MultiIndex.from_tuples([],names=['component', 'key', 'asset'])
     raw = pd.DataFrame(index=n.snapshots,columns=columns)
-    for component in n.all_components:
-        pnl = n.pnl(component)
-        for key in pnl.keys():
-            if not pnl[key].empty:
-                df = pnl[key].copy()
-                df.columns = pd.MultiIndex.from_product([[component], [key], df.columns])
+    for c in n.iterate_components():
+        for attr, pnl in c.pnl.items():
+            # exclude e_min_pu which is used for SOC of EVs in the morning
+            if not pnl.empty and attr != 'e_min_pu':
+                df = pnl.copy()
+                df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
                 raw = pd.concat([raw, df], axis=1)
 
     # normalise all time-dependent data
@@ -2478,18 +2478,22 @@ def set_temporal_aggregation(n, opts, solver_name):
         m = re.match(r"^\d+h$", o, re.IGNORECASE)
         if m is not None:
             n = average_every_nhours(n, m.group(0))
+            break
         # representive snapshots
-        m = re.match(r"^\d+sn$", o, re.IGNORECASE)
+        m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
         if m is not None:
-            sn = int(m.group(0).split("sn")[0])
-            logger.info("use every {} snapshot as representative".format(sn))
+            sn = int(m[1])
+            logger.info(f"use every {sn} snapshot as representative")
             n.set_snapshots(n.snapshots[::sn])
             n.snapshot_weightings *= sn
+            break
         # segments with package tsam
-        if "SEG" in o:
-            segments = int(o.replace("SEG",""))
-            logger.info("use temporal segmentation with {} segments".format(segments))
+        m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
+        if m is not None:
+            segments = int(m[1])
+            logger.info(f"use temporal segmentation with {segments} segments")
             n = apply_time_segmentation(n, segments, solver_name=solver_name)
+            break
     return n
 #%%
 if __name__ == "__main__":

From 77dfca76e06cb5b5ab941259bfd43b4d028f1dbb Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Wed, 14 Sep 2022 16:44:53 +0200
Subject: [PATCH 126/243] uncommented annual dictionaries, set reference year
 to 2050

---
 config.default.yaml | 112 ++++++++++++++++++++++----------------------
 1 file changed, 56 insertions(+), 56 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index c964abce..9824b1f5 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -41,7 +41,7 @@ scenario:
   # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
   # decay with initial growth rate 0
   planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
-    - 2030
+    - 2050
   # for example, set to
   # - 2020
   # - 2030
@@ -154,11 +154,11 @@ sector:
     potential: 0.6  # maximum fraction of urban demand which can be supplied by district heating
      # increase of today's district heating demand to potential maximum district heating share
      # progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
-    progress: 1
-      # 2020: 0.0
-      # 2030: 0.3
-      # 2040: 0.6
-      # 2050: 1.0
+    progress:
+      2020: 0.0
+      2030: 0.3
+      2040: 0.6
+      2050: 1.0
     district_heating_loss: 0.15
   bev_dsm_restriction_value: 0.75  #Set to 0 for no restriction on BEV DSM
   bev_dsm_restriction_time: 7  #Time at which SOC of BEV has to be dsm_restriction_value
@@ -178,16 +178,16 @@ sector:
   bev_avail_mean: 0.8
   v2g: true #allows feed-in to grid from EV battery
   #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
-    # 2020: 0
-    # 2030: 0.05
-    # 2040: 0.1
-    # 2050: 0.15
-  land_transport_electric_share: 0.85 # 1 means all EVs
-    # 2020: 0
-    # 2030: 0.25
-    # 2040: 0.6
-    # 2050: 0.85
+  land_transport_fuel_cell_share:  # 1 means all FCEVs
+    2020: 0
+    2030: 0.05
+    2040: 0.1
+    2050: 0.15
+  land_transport_electric_share:  # 1 means all EVs
+    2020: 0
+    2030: 0.25
+    2040: 0.6
+    2050: 0.85
   transport_fuel_cell_efficiency: 0.5
   transport_internal_combustion_efficiency: 0.3
   agriculture_machinery_electric_share: 0
@@ -195,29 +195,29 @@ sector:
   agriculture_machinery_electric_efficiency: 0.3 # electricity per use
   shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share: 1 # 1 means all hydrogen FC
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.15
-    # 2040: 0.3
-    # 2045: 0.6
-    # 2050: 1
+  shipping_hydrogen_share:  # 1 means all hydrogen FC
+    2020: 0
+    2025: 0
+    2030: 0.05
+    2035: 0.15
+    2040: 0.3
+    2045: 0.6
+    2050: 1
   time_dep_hp_cop: true #time dependent heat pump coefficient of performance
   heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
    # conservatively high to cover hot water and space heating in poorly-insulated buildings
   reduce_space_heat_exogenously: true  # reduces space heat demand by a given factor (applied before losses in DH)
   # this can represent e.g. building renovation, building demolition, or if
   # the factor is negative: increasing floor area, increased thermal comfort, population growth
-  reduce_space_heat_exogenously_factor: 0.29 # per unit reduction in space heat demand
+  reduce_space_heat_exogenously_factor:  # per unit reduction in space heat demand
   # the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
-    # 2020: 0.10  # this results in a space heat demand reduction of 10%
-    # 2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
-    # 2030: 0.09
-    # 2035: 0.11
-    # 2040: 0.16
-    # 2045: 0.21
-    # 2050: 0.29
+    2020: 0.10  # this results in a space heat demand reduction of 10%
+    2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
+    2030: 0.09
+    2035: 0.11
+    2040: 0.16
+    2045: 0.21
+    2050: 0.29
   retrofitting :  # co-optimises building renovation to reduce space heat demand
     retro_endogen: false  # co-optimise space heat savings
     cost_factor: 1.0   # weight costs for building renovation
@@ -276,32 +276,32 @@ sector:
 
 
 industry:
-  St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
-    # 2020: 0.6
-    # 2025: 0.55
-    # 2030: 0.5
-    # 2035: 0.45
-    # 2040: 0.4
-    # 2045: 0.35
-    # 2050: 0.3
-  DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
-    # 2020: 0
-    # 2025: 0
-    # 2030: 0.05
-    # 2035: 0.2
-    # 2040: 0.4
-    # 2045: 0.7
-    # 2050: 1
+  St_primary_fraction:  # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
+    2020: 0.6
+    2025: 0.55
+    2030: 0.5
+    2035: 0.45
+    2040: 0.4
+    2045: 0.35
+    2050: 0.3
+  DRI_fraction:  # fraction of the primary route converted to DRI + EAF
+    2020: 0
+    2025: 0
+    2030: 0.05
+    2035: 0.2
+    2040: 0.4
+    2045: 0.7
+    2050: 1
   H2_DRI: 1.7   #H2 consumption in Direct Reduced Iron (DRI),  MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
   elec_DRI: 0.322   #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
-  Al_primary_fraction: 0.2 # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
-    # 2020: 0.4
-    # 2025: 0.375
-    # 2030: 0.35
-    # 2035: 0.325
-    # 2040: 0.3
-    # 2045: 0.25
-    # 2050: 0.2
+  Al_primary_fraction:  # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
+    2020: 0.4
+    2025: 0.375
+    2030: 0.35
+    2035: 0.325
+    2040: 0.3
+    2045: 0.25
+    2050: 0.2
   MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
   MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
   MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)

From ffb296c06d82a6957af771d795df702b8b017879 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Sun, 18 Sep 2022 11:02:52 +0200
Subject: [PATCH 127/243] Update conf.py

---
 doc/conf.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/conf.py b/doc/conf.py
index d647e953..de9908be 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -32,6 +32,7 @@ sys.path.insert(0, os.path.abspath('../scripts'))
 extensions = [
     #'sphinx.ext.autodoc',
     #'sphinx.ext.autosummary',
+    'sphinx.ext.autosectionlabel',
     'sphinx.ext.intersphinx',
     'sphinx.ext.todo',
     'sphinx.ext.mathjax',

From cc325b73afd1274adfb2cb1f2257ba21b9740b3f Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 09:27:50 +0200
Subject: [PATCH 128/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 78773682..27328a3b 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -39,7 +39,7 @@ The remaining electricity demand for households and services is distributed insi
 Heat demand
 ===========
 
-Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options [To do:link to next section]
+Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options (see :ref:`heat-supply`.)
 Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.  
 
 *Space heating*
@@ -82,7 +82,7 @@ In practice, in PyPSA-Eur-Sec, there are heat demand buses to which the correspo
 4)	Residential rural heat: heating for residential buildings in rural areas with low population density.
 5)	Services rural heat: heating for residential services buildings in rural areas with low population density. Heat demand from agriculture  sector is also included here. 
 
-
+.. _heat-supply:
 Heat supply
 =======================
 
@@ -90,7 +90,7 @@ Different supply options are available depending on whether demand is met centra
 
 *Urban central heat*
 
-For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. 
+For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more setailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`
 
 *Residential and Urban decentral heat*
 
@@ -228,6 +228,7 @@ Methane demand
                   
 Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat[LINK TO INDUSTRY OVERVIEW]. Methane is not used in the trans- port sector because of engine slippage.
 
+.. _Methane supply:
 Methane supply                          
 ===================================
 
@@ -300,6 +301,7 @@ Oil-based products demand
 Naphtha is used as a feedstock in the chemicals industry[LINK TO CHEMICAL INDUSTRY]. Furthermore, kerosene is used as transport fuel in the aviation sector[LINK TO AVIATION SECTOR]. Non-electrified agriculture machinery also consumes gasoline. 
 Land transport [LINK TO LAND TRANSPORT]  that is not electrified or converted into using H2-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
 
+.. _Oil-based products supply:
 
 Oil-based products supply 
 ======================== 							

From c668ce3c0ce4760dc53221445046b639b839a4cd Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 09:46:07 +0200
Subject: [PATCH 129/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 27328a3b..2a2f0d1c 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -357,6 +357,7 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 .. image:: ../graphics/hotmaps.png
 
+.. _Iron and Steel:
 
 *Iron and Steel*
 
@@ -405,6 +406,8 @@ The share of steel produced via the primary route is exogenously set in the `con
 					
 For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (add link to the overview section). 
 
+.. _Chemicals Industry:
+
 *Chemicals Industry*
 
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
@@ -424,6 +427,8 @@ $$
 
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
+.. _Transportation:
+
 Transportation
 =========================
 Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
@@ -460,6 +465,7 @@ All land transport that is not specified to be either BEV or FCEV will be treate
 
 The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
+.. _Shipping:
 
 *Shipping*
 

From 5efb0abe02aeed6a757d2dd0d16be5f11a61f69e Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 12:50:09 +0200
Subject: [PATCH 130/243] Create supply_demand.rst

---
 doc/supply_demand.rst | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 2a2f0d1c..93cb71ed 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -17,7 +17,7 @@ The basic supply (left column) and demand (right column) options in the model ar
 
 .. image:: ../graphics/multisector_figure.png
 
-
+.. _Electricity supply and demand:
 
 Electricity supply and demand
 =============================
@@ -185,8 +185,8 @@ Further information are given in the study by Zeyen et al. : `Mitigating heat de
 Hydrogen demand
 =============================
 
-Hydrogen is consumed in the industry sector (link to industry) to produce ammonia [link to ammonia industry section] and direct reduced iron (DRI) [link to DRI industry section]. Hydrogen is also consumed to produce synthetic methane [link to section “Methane supply”] and liquid hydrocarbons [link to fossil-oil based supply”] which have multiple uses in industry and other sectors. 
-Hydrogen is also used for transport applications (link to transport), where it is exogenously fixed. It is used in `heavy-duty land transport <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_ and as liquified hydrogen in the shipping sector [add link to shipping sector]. Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations [Add a link to the section where we describe the Electricity sector and how storage is modelled there]. The waste heat from the stationary fuel cells can be used in `district-heating systems <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_.
+Hydrogen is consumed in the industry sector (see :ref:`Industry demand`) to produce ammonia (see :ref:`Chemicals Industry`) and direct reduced iron (DRI) (see :ref:`Iron and Steel`). Hydrogen is also consumed to produce synthetic methane (see :ref:`Methane supply`) and liquid hydrocarbons (see :ref:`Oil-based products supply`) which have multiple uses in industry and other sectors. 
+Hydrogen is also used for transport applications (see :ref:`Transportation`), where it is exogenously fixed. It is used in `heavy-duty land transport <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_ and as liquified hydrogen in the shipping sector (see :ref:`Shipping`). Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations (see :ref:`Electricity supply and demand`). The waste heat from the stationary fuel cells can be used in `district-heating systems <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_.
 
 Hydrogen supply
 =============================
@@ -318,6 +318,7 @@ with costs as included from the `technology-data repository <https://github.com/
 	
 Liquid hydrocarbons are assumed to be transported freely among the model region since future demand is predicted to be low, transport costs for liquids are low and no bottlenecks are expected. 
 
+.. _Industry demand:
 
 Industry demand
 ================
@@ -357,7 +358,6 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 .. image:: ../graphics/hotmaps.png
 
-.. _Iron and Steel:
 
 *Iron and Steel*
 
@@ -406,8 +406,6 @@ The share of steel produced via the primary route is exogenously set in the `con
 					
 For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (add link to the overview section). 
 
-.. _Chemicals Industry:
-
 *Chemicals Industry*
 
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
@@ -427,8 +425,6 @@ $$
 
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
 
-.. _Transportation:
-
 Transportation
 =========================
 Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
@@ -465,7 +461,6 @@ All land transport that is not specified to be either BEV or FCEV will be treate
 
 The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
 
-.. _Shipping:
 
 *Shipping*
 

From 5eb20ebdd5b5d2f102189ab0a002f798fdf0fd91 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 14:40:56 +0200
Subject: [PATCH 131/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 30 +++++++++++++++++++-----------
 1 file changed, 19 insertions(+), 11 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 93cb71ed..299aea1a 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -83,6 +83,7 @@ In practice, in PyPSA-Eur-Sec, there are heat demand buses to which the correspo
 5)	Services rural heat: heating for residential services buildings in rural areas with low population density. Heat demand from agriculture  sector is also included here. 
 
 .. _heat-supply:
+
 Heat supply
 =======================
 
@@ -99,6 +100,10 @@ Ground-source heat pumps are only allowed in rural areas because of space constr
 
 Below are more detailed explanations for each heating supply component, all of which are modeled as `Links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_. in PyPSA-Eue-Sec.
 
+.. _Large-scale CHP:
+
+*Large-scale CHP*
+
 Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
  
 CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The efficiencies of each are given on the back pressure line, where the back pressure coefficient cb is the electricity output divided by the heat output. (For a more complete explanation of the operation of CHPs refer to the study by Dahl et al. : `Cost sensitivity of optimal sector-coupled district heating production systems <https://arxiv.org/pdf/1804.07557.pdf>`_.
@@ -204,7 +209,7 @@ CO + H_2O → CO_2 + H_2
 $$
 
 SMR is included `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245>`_. 
-PyPSA-Eur-Sec allows this route of H2 production with and without [carbon capture (CC)] (Link to section on Carbon Capture Storage and Utilization). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
+PyPSA-Eur-Sec allows this route of H2 production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
 
 Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
 
@@ -226,9 +231,10 @@ Hydrogen can be stored in overground steel tanks or `underground salt caverns <h
 Methane demand  
 ====================================
                   
-Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat[LINK TO INDUSTRY OVERVIEW]. Methane is not used in the trans- port sector because of engine slippage.
+Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat (see :ref:`Industry demand`). Methane is not used in the trans- port sector because of engine slippage.
 
 .. _Methane supply:
+
 Methane supply                          
 ===================================
 
@@ -276,13 +282,13 @@ A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be th
 
 *Solid biomass conversion and use*
 
-Solid biomass can be used directly to provide process heat up to 500˚C in the industry. It can also be burned in CHP plants and boilers associated with heating systems. These technologies are described elsewhere [link to heat and industry sections].
+Solid biomass can be used directly to provide process heat up to 500˚C in the industry. It can also be burned in CHP plants and boilers associated with heating systems. These technologies are described elsewhere (see :ref:`Large-scale CHP` and :ref:`Industry demand`).
 
 
-Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture [link to technology data].
+Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ).
 
 
-Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture [link to technology data].
+Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ).
 
 
 *Transport of solid biomass*
@@ -292,20 +298,20 @@ The transport of solid biomass can either be assumed unlimited between countries
 *Biogas transport and use*
 
 Biogas will be aggregated into a common European resources if a gas network is not modeled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
-The model can only use biogas by first upgrading it to natural gas quality [link to tech description] (bio methane) which is fed into the general gas network.
+The model can only use biogas by first upgrading it to natural gas quality [see :ref:`Methane supply`] (bio methane) which is fed into the general gas network.
 
 
 
 Oil-based products demand
 ========================
-Naphtha is used as a feedstock in the chemicals industry[LINK TO CHEMICAL INDUSTRY]. Furthermore, kerosene is used as transport fuel in the aviation sector[LINK TO AVIATION SECTOR]. Non-electrified agriculture machinery also consumes gasoline. 
-Land transport [LINK TO LAND TRANSPORT]  that is not electrified or converted into using H2-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
+Naphtha is used as a feedstock in the chemicals industry (see :ref:`Chemicals Industry`). Furthermore, kerosene is used as transport fuel in the aviation sector (see :ref:`Aviation`). Non-electrified agriculture machinery also consumes gasoline. 
+Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using H2-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
 
 .. _Oil-based products supply:
 
 Oil-based products supply 
 ======================== 							
-Oil-based products can be either of fossil origin or synthetically produced by combining H2 [link to hydrogen] and captured CO2 [link to carbon capture] in Fischer-Tropsch plants		
+Oil-based products can be either of fossil origin or synthetically produced by combining H2 (see :ref:`Hydrogen supply`) and captured CO2 (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
 
 $$
 𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
@@ -326,7 +332,9 @@ Industry demand
 Industry demand is split into a dozen different sectors with specific energy demands, process
 emissions of carbon dioxide, as well as existing and prospective mitigation strategies.
 
-Subsection overview (link to section overview) provides a general description of the modelling approach for the industry sector. The following subsections describe the current energy demands, available mitigation strategies, and whether mitigation is exogenously fixed or co-optimised with the other components of the model for each industry subsector in more detail. See details for Iron and Steel (link to subsection Iron and Steel), Chemicals Industry (link to subsection Chemicals Industry), Ammonia (link to subsection Ammonia), Non-metallic Mineral products (link to subsection Non-metallic products), Non-ferrous Metals (link to subsection Non-ferrous Metals), Other Industry Subsectors (link to subsection Other Industry Subsectors).
+The Subsection overview below provides a general description of the modelling approach for the industry sector. The following subsections describe the current energy demands, available mitigation strategies, and whether mitigation is exogenously fixed or co-optimised with the other components of the model for each industry subsector in more detail. See details for Iron and Steel (see :ref:`Iron and Steel`), Chemicals Industry and Ammonia (see :ref:`Chemicals Industry`), Non-metallic Mineral products , Non-ferrous Metals , and other Industry Subsectors.
+
+.. _Overview:
 
 *Overview*
 
@@ -404,7 +412,7 @@ This circumvents the process emissions associated with the use of coke. For hydr
 					
 The share of steel produced via the primary route is exogenously set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L279>`_. The share of steel obtained via hydrogen-based DRI plus EAF is also set exogenously in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L287>`_. The remaining share is manufactured through the secondary route using scrap metal in EAF. Bioenergy as alternative to coke in blast furnaces is not considered in the model (`Mandova et.al <https://doi.org/10.1016/j.biombioe.2018.04.021>`_, `Suopajärvi et.al <https://doi.org/10.1016/j.apenergy.2018.01.060>`_).
 					
-For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (add link to the overview section). 
+For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (see :ref:`Overview`). 
 
 *Chemicals Industry*
 

From 77e2e53aa565ed885914fe92773f4f3f99cce4e9 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 14:46:38 +0200
Subject: [PATCH 132/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 299aea1a..7e7e9122 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -420,7 +420,7 @@ The chemicals industry includes a wide range of diverse industries, including th
 
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
 				
-The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (LINK TO HYDROGEN SUPPLY), for methane (LINK TO METHANE SUPPLY), and for oil-based products (LINK TO OIL-BASED PRODUCTS SUPPLY). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the opti- misation.
+The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (see :ref:`Hydrogen supply`), for methane (see :ref:`Methane supply`), and for oil-based products (see :ref:`Oil-based products supply`). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the opti- misation.
 					
 The basic chemicals consumption data from the `JRC IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ database comprises high- value chemicals (ethylene, propylene and BTX), chlorine, methanol and ammonia. However, it is necessary to separate out these chemicals because their current and future production routes are different.
 					
@@ -431,7 +431,7 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (LINK TO HYDROGEN SUPPLY)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
 Transportation
 =========================
@@ -455,19 +455,19 @@ The battery cost of BEV is not included in the model since it is assumed that BE
 
 *Hydrogen fuel cell vehicles (FCEV)*
 
-The share of all land transport that is specified to be be FCEV will be converted to a demand for hydrogen [link to hydrogen] using the `FCEV efficiency
+The share of all land transport that is specified to be be FCEV will be converted to a demand for hydrogen (see :ref:`Hydrogen supply`) using the `FCEV efficiency
 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L191>`_.
 
 FCEVs are typically used to simulate demand for transport that is hard to electrify directly, e.g. heavy construction machinery. But it may also be used to investigate a more widespread adoption of the technology.
 
 *Internal combustion engine vehicles (ICE)* 
 
-All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products [link to oil products] using the `ICE efficiency
+All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products (see :ref:`Oil-based products supply`) using the `ICE efficiency
 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L192>`_.
 
 *Aviation*
 
-The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin [link to oil product].
+The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
 
 
 *Shipping*
@@ -476,7 +476,7 @@ Shipping energy demand is covered by a combination of oil and hydrogen. Other fu
 
 To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
 
-The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers [link to hydrogen]. The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.
+The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers (see :ref:`Hydrogen supply`). The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.
 
 The energy demand for liquefaction of the hydrogen used for shipping can be `included  <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
 

From b3f66561c882d7ca557bb916cbdb950ce985d3e6 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 14:51:46 +0200
Subject: [PATCH 133/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 7e7e9122..fda9e383 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -104,7 +104,7 @@ Below are more detailed explanations for each heating supply component, all of w
 
 *Large-scale CHP*
 
-Large Combined Heat and Power plants are included in the model if it is specified in the `config file. <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
+Large Combined Heat and Power plants are included in the model if it is specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
  
 CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The efficiencies of each are given on the back pressure line, where the back pressure coefficient cb is the electricity output divided by the heat output. (For a more complete explanation of the operation of CHPs refer to the study by Dahl et al. : `Cost sensitivity of optimal sector-coupled district heating production systems <https://arxiv.org/pdf/1804.07557.pdf>`_.
 

From 1d8e964e124beef9d7e4c68608b26fac3bda05d0 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 15:02:42 +0200
Subject: [PATCH 134/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index fda9e383..6204ca6e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -433,6 +433,12 @@ $$
 
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
+Agriculture demand
+=========================
+
+Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC-IDEES database <http://data.europa.eu/89h/jrc-10110-10001>`_. Missing countries are filled with `Eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat) machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is assigned at “services rural heat” buses. Time series for demands are assumed to be constant and distributed inside countries by population.
+
+
 Transportation
 =========================
 Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.

From ac2747e25c8f191d24e22bd8e6b807660a8efbc0 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 15:06:12 +0200
Subject: [PATCH 135/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 6204ca6e..daf8aa06 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -271,7 +271,7 @@ Biomass supply potentials for every NUTS2 region are taken from the `JRC ENSPRES
 The desired scenario can be selected in the pypsa-eur-sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JRC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
 
 
-The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the following categories: biogas, solid biomass, and not included.Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas, which can be upgraded to biomethane. Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste . are available for combustion in combined-heat-and power (CHP) plants and for medium temperature heat (below 500 °C) applications in industry. It can also converted to gas or liquid fuels.
+The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the following categories: biogas, solid biomass, and not included. Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas, which can be upgraded to biomethane. Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste, are available for combustion in combined-heat-and power (CHP) plants and for medium temperature heat (below 500 °C) applications in industry. It can also converted to gas or liquid fuels.
 
 
 Feedstocks labeled as not included are ignored by the model.

From 98d94a21fe49968ed3b4e551bf104dbff34fbcb5 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 15:10:02 +0200
Subject: [PATCH 136/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index daf8aa06..56947f96 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -120,7 +120,7 @@ Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https:
 
 *Heat pumps*
 
-The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter[refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
+The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter (refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
 $$ &Delta; T = T_(sink) − T_(source) $$ 
 

From af99cd63cc6baeca37427b9677ee2c86ae5f3908 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 15:11:44 +0200
Subject: [PATCH 137/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 56947f96..8c9ebff2 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -436,7 +436,7 @@ The Haber-Bosch process is not explicitly represented in the model, such that de
 Agriculture demand
 =========================
 
-Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC-IDEES database <http://data.europa.eu/89h/jrc-10110-10001>`_. Missing countries are filled with `Eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat) machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is assigned at “services rural heat” buses. Time series for demands are assumed to be constant and distributed inside countries by population.
+Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC-IDEES database <http://data.europa.eu/89h/jrc-10110-10001>`_. Missing countries are filled with `Eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat), and machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is assigned at “services rural heat” buses. Time series for demands are assumed to be constant and distributed inside countries by population.
 
 
 Transportation

From b535a3f40556743484fff98f9d76ce864fcf7735 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 15:23:48 +0200
Subject: [PATCH 138/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8c9ebff2..d261b4f6 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -35,6 +35,7 @@ Also unlike PyPSA-Eur, PyPSA-Eur-Sec subtracts existing electrified heating from
 
 The remaining electricity demand for households and services is distributed inside each country proportional to GDP and population.
 
+.. _Heat demand:
 
 Heat demand
 ===========
@@ -186,6 +187,7 @@ renovation include `cost factor <https://github.com/PyPSA/pypsa-eur-sec/blob/3da
 
 Further information are given in the study by Zeyen et al. : `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
 
+.. _Hydrogen demand:
 
 Hydrogen demand
 =============================
@@ -193,6 +195,8 @@ Hydrogen demand
 Hydrogen is consumed in the industry sector (see :ref:`Industry demand`) to produce ammonia (see :ref:`Chemicals Industry`) and direct reduced iron (DRI) (see :ref:`Iron and Steel`). Hydrogen is also consumed to produce synthetic methane (see :ref:`Methane supply`) and liquid hydrocarbons (see :ref:`Oil-based products supply`) which have multiple uses in industry and other sectors. 
 Hydrogen is also used for transport applications (see :ref:`Transportation`), where it is exogenously fixed. It is used in `heavy-duty land transport <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_ and as liquified hydrogen in the shipping sector (see :ref:`Shipping`). Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations (see :ref:`Electricity supply and demand`). The waste heat from the stationary fuel cells can be used in `district-heating systems <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_.
 
+.. _Hydrogen supply:
+
 Hydrogen supply
 =============================
 
@@ -228,6 +232,8 @@ New pipelines can be built additionally on all routes where there currently is a
 
 Hydrogen can be stored in overground steel tanks or `underground salt caverns <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L250>`_. For the latter, energy storage capacities in every country are limited to the potential estimation for onshore salt caverns within `50 km <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L251>`_ of shore to avoid environmental issues associated with brine solution disposal. Underground storage potentials for hydrogen in European salt caverns is acquired from `Caglayan et al. <https://doi.org/10.1016/j.ijhydene.2019.12.161>`_
 
+.. _Methane demand:
+
 Methane demand  
 ====================================
                   
@@ -256,11 +262,14 @@ The following figure shows the unclustered European gas transmission network bas
     
 .. image:: ../graphics/gas_pipeline_figure.png
 
+.. _Biomass supply:
 
 Biomass Supply
 =====================
 Biomass supply potentials for each European country are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
 
+.. _Biomass demand:
+
 Biomass demand
 =====================
 
@@ -300,6 +309,7 @@ The transport of solid biomass can either be assumed unlimited between countries
 Biogas will be aggregated into a common European resources if a gas network is not modeled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
 The model can only use biogas by first upgrading it to natural gas quality [see :ref:`Methane supply`] (bio methane) which is fed into the general gas network.
 
+.. _Oil-based products demand:
 
 
 Oil-based products demand
@@ -367,6 +377,8 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 .. image:: ../graphics/hotmaps.png
 
 
+.. _Iron and Steel:
+
 *Iron and Steel*
 
 Two alternative routes are used today to manufacture steel in Europe. The primary route (integrated steelworks) represents 60% of steel production, while the secondary route (electric arc furnaces, EAF), represents the other 40% `(Lechtenböhmer et. al) <https://doi.org/10.1016/j.energy.2016.07.110>`_.
@@ -414,6 +426,8 @@ The share of steel produced via the primary route is exogenously set in the `con
 					
 For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (see :ref:`Overview`). 
 
+.. _Chemicals Industry:
+
 *Chemicals Industry*
 
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
@@ -438,12 +452,15 @@ Agriculture demand
 
 Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC-IDEES database <http://data.europa.eu/89h/jrc-10110-10001>`_. Missing countries are filled with `Eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat), and machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is assigned at “services rural heat” buses. Time series for demands are assumed to be constant and distributed inside countries by population.
 
+.. _Transportation:
 
 Transportation
 =========================
 Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
 
-*Land transport*
+.. _Land transport:
+
+**Land transport**
 
 Both road and rail transport is combined as `land transport demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_transport_demand.py#L74>`_ although electrified rail transport is excluded because that demand is included in the current electricity demand.
 
@@ -471,12 +488,15 @@ FCEVs are typically used to simulate demand for transport that is hard to electr
 All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products (see :ref:`Oil-based products supply`) using the `ICE efficiency
 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L192>`_.
 
-*Aviation*
+.. _Aviation:
+
+**Aviation**
 
 The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
 
+.. _Shipping:
 
-*Shipping*
+**Shipping**
 
 Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec.The share of shipping that is assumed to be supplied by hydrogen can be selected in the config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
 
@@ -486,7 +506,7 @@ The consumed hydrogen comes from the general hydrogen bus where it can be produc
 
 The energy demand for liquefaction of the hydrogen used for shipping can be `included  <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
 
-
+.. _Carbon dioxide capture, usage and sequestration (CCU/S):
 
 Carbon dioxide capture, usage and sequestration (CCU/S)
 =========================================================

From ac3572542346c73b4a8dd3c8f16093baa2f2b478 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 15:30:36 +0200
Subject: [PATCH 139/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index d261b4f6..7ac51f1a 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -492,13 +492,13 @@ All land transport that is not specified to be either BEV or FCEV will be treate
 
 **Aviation**
 
-The ‘demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
+The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
 
 .. _Shipping:
 
 **Shipping**
 
-Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec.The share of shipping that is assumed to be supplied by hydrogen can be selected in the config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
+Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec.The share of shipping that is assumed to be supplied by hydrogen can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
 
 To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
 

From 8fae5178813ecf48eaa08b60b5aa9f82330ad514 Mon Sep 17 00:00:00 2001
From: Parisra <102019906+Parisra@users.noreply.github.com>
Date: Mon, 19 Sep 2022 16:17:30 +0200
Subject: [PATCH 140/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 7ac51f1a..9fd277a8 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -225,7 +225,7 @@ For the electrolysis, alkaline electrolysers are chosen since they have lower co
 
 *Transport*
 
-Hydrogen is transported by pipelines. H2 pipelines are endogenously generated, either via a  greenfield H2 network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in [link to H2 backbone study]) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
+Hydrogen is transported by pipelines. H2 pipelines are endogenously generated, either via a  greenfield H2 network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
 New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
 
 *Storage*

From d53f1fe03185f9531d74d6d29a36899a1cdf4d34 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 21 Sep 2022 07:36:06 +0200
Subject: [PATCH 141/243] update test configs to inclode cost year

---
 test/config.myopic.yaml    | 1 +
 test/config.overnight.yaml | 1 +
 2 files changed, 2 insertions(+)

diff --git a/test/config.myopic.yaml b/test/config.myopic.yaml
index 1a54b937..a44614e3 100644
--- a/test/config.myopic.yaml
+++ b/test/config.myopic.yaml
@@ -316,6 +316,7 @@ industry:
   # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
 
 costs:
+  year: 2030
   lifetime: 25 #default lifetime
   # From a Lion Hirth paper, also reflects average of Noothout et al 2016
   discountrate: 0.07
diff --git a/test/config.overnight.yaml b/test/config.overnight.yaml
index 3764c8fa..61dd9927 100644
--- a/test/config.overnight.yaml
+++ b/test/config.overnight.yaml
@@ -314,6 +314,7 @@ industry:
   # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
 
 costs:
+  year: 2030
   lifetime: 25 #default lifetime
   # From a Lion Hirth paper, also reflects average of Noothout et al 2016
   discountrate: 0.07

From f13902510010b734c510c38c4cae99356f683058 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 21 Sep 2022 07:41:31 +0200
Subject: [PATCH 142/243] update test configs to include recent biomass
 additions

---
 test/config.myopic.yaml    | 3 +++
 test/config.overnight.yaml | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/test/config.myopic.yaml b/test/config.myopic.yaml
index a44614e3..d0a6a918 100644
--- a/test/config.myopic.yaml
+++ b/test/config.myopic.yaml
@@ -262,6 +262,9 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
+  biomass_boiler: false
+  biomass_to_liquid: false
+  biosng: false
 
 
 industry:
diff --git a/test/config.overnight.yaml b/test/config.overnight.yaml
index 61dd9927..1dc314dd 100644
--- a/test/config.overnight.yaml
+++ b/test/config.overnight.yaml
@@ -260,6 +260,9 @@ sector:
   biomass_transport: false  # biomass transport between nodes
   conventional_generation: # generator : carrier
     OCGT: gas
+  biomass_boiler: false
+  biomass_to_liquid: false
+  biosng: false
 
 
 industry:

From ffb5a5a10d7dd6b73e4dc526892f178ffb0d9729 Mon Sep 17 00:00:00 2001
From: Ebbe Kyhl <69363603+ebbekyhl@users.noreply.github.com>
Date: Wed, 21 Sep 2022 09:08:39 +0200
Subject: [PATCH 143/243] Update myopic and extend carbon budget description

---
 doc/myopic.rst | 97 +++++++++++++++++++++++++++++---------------------
 1 file changed, 57 insertions(+), 40 deletions(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index 14f651dd..dd2802b4 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -6,13 +6,13 @@ Myopic transition path
 
 The myopic code can be used to investigate progressive changes in a network, for instance, those taking place throughout a transition path. The capacities installed in a certain time step are maintained in the network until their operational lifetime expires.
 
-The myopic approach was initially developed and used in the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__ but the current implementation complies with the pypsa-eur-sec standard working flow and is compatible with using the higher resolution electricity transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`__ rather than a one-node-per-country model.
+The myopic approach was initially developed and used in the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__ and later further extended in `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__. The current implementation complies with the PyPSA-Eur-Sec standard working flow and is compatible with using the higher resolution electricity transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`__ rather than a one-node-per-country model.
 
-The current code applies the myopic approach to generators, storage technologies and links in the power sector and the space and water heating sector.
+The current code applies the myopic approach to generators, storage technologies and links in the power sector. It furthermore applies it to the space and water heating sector (e.g., the share of district heating and reduced space heat demand), industry processes (e.g., steel, direct reduced iron, and aluminum production via primary route), the share of fuel cell and battery electric vehicles in land transport, and the hydrogen share in shipping (see :doc:`supply_demand` for further information).
 
-The transport sector and industry are not affected by the myopic code. In essence, the electrification of road and rail transport, the percentage of electric vehicles that allow demand-side management and vehicle-to-grid services, and the transformation in the different industrial subsectors do not evolve with time. They are kept fixed at the values specified in the configuration file. Including the transport sector and industry in the myopic code is planned for the near future.
-
-See also other `outstanding issues <https://github.com/PyPSA/pypsa-eur-sec/issues/19#issuecomment-678194802>`_.
+The following subjects within the land transport and biomass currently do not evolve with the myopic approach:
+- The percentage of electric vehicles that allow demand-side management and vehicle-to-grid services
+- The annual biomass potential (default year and scenario for which potential is taken is 2030, defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L109>`_)
 
 Configuration
 =================
@@ -23,24 +23,38 @@ The following options included in the config.yaml file  are relevant for the myo
 
 To activate the myopic option select ``foresight: 'myopic'`` in ``config.yaml``.
 
-To set the investment years which are sequentially simulated for the myopic investment planning, select for example ``planning_horizons : [2020, 2030, 2040, 2050]`` in ``config.yaml``.
+The {planning_horizons} wildcard indicates the year in which the network is optimized. For a myopic optimization, this is equivalent to the investment year. To set the investment years which are sequentially simulated for the myopic investment planning, select for example:
 
+    planning_horizons:
+    
+    \- 2020
+    
+    \- 2030
+    
+    \- 2040
+    
+    \- 2050
+
+in ``config.yaml``.
 
 
 **existing capacities**
 
-Grouping years indicates the bins limits for grouping the existing capacities of different technologies
+Grouping years indicates the bins limits for grouping the existing capacities of different technologies. Note that separate bins are defined for the power and heating plants due to different data sources. 
+
+``grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]``
+
+``grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]``
 
-grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
 
 
 
 
 **threshold capacity**
 
-if for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored
+If for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored
 
-threshold_capacity: 10
+``threshold_capacity: 10``
 
 
 
@@ -49,30 +63,33 @@ threshold_capacity: 10
 
 conventional carriers indicate carriers used in the existing conventional technologies
 
-conventional_carriers: ['lignite', 'coal', 'oil', 'uranium']
+    conventional_carriers:
+    
+    \- lignite
+    
+    \- coal
+    
+    \- oil
+    
+    \- uranium
 
 
 
-Wildcards
-==============================
-
-**{planning_horizons} wildcard**
-
-The {planning_horizons} wildcard indicates the timesteps in which the network is optimized, e.g. planning_horizons: [2020, 2030, 2040, 2050]
-
 
 Options
 =============
-The total carbon budget for the entire transition path can be indicated in the ``scenario.sector_opts`` in ``config.yaml``.
-The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay. 
-E.g. ``'cb40ex0'`` splits the a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero
+The total carbon budget for the entire transition path can be indicated in the `sector_opts <https://github.com/PyPSA/pypsa-eur-sec/blob/f13902510010b734c510c38c4cae99356f683058/config.default.yaml#L25>`_ in ``config.yaml``. The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay. 
+E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero
+They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
+The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed hroughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
 
 $e(t) = e_0 (1+ (r+m)t) e^(-mt)$
 
-See details in Supplementary Note 1 of the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__
+where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the ``scenario.sector_opts``
+E.g.  ``-cb25.7ex0`` (1.5C increase)
+Or ``cb73.9ex0`` (2C increase)
+See details in Supplemental Note S1 `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__
 
-Rules overview
-=================
 
 General myopic code structure
 ===============================
@@ -89,32 +106,32 @@ The base year is the first element in ``planning_horizons``. Step 1 is implement
 
 Steps 2 and 3 are solved recursively for all the planning_horizons included in ``config.yaml``.
 
+Rule overview
+===============================
 
-rule add_existing baseyear
-==========================
+- rule add_existing baseyear
 
-The rule add_existing_baseyear loads the network in ‘results/run_name/networks/prenetworks’ and performs the following operations:
+  The rule add_existing_baseyear loads the network in ‘results/run_name/networks/prenetworks’ and performs the following operations:
 
-1.Add the conventional, wind and solar power generators that were installed before the base year.
+  1. Add the conventional, wind and solar power generators that were installed before the base year.
 
-2.Add the heating capacities that were installed before the base year.
+  2. Add the heating capacities that were installed before the base year.
 
-The existing conventional generators are retrieved from the `powerplants.csv file <https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__ generated by pypsa-eur which, in turn, is based on the `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ database.
+  The existing conventional generators are retrieved from the `powerplants.csv file <https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__ generated by pypsa-eur which, in turn, is based on the `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ database.
 
-Existing wind and solar capacities are retrieved from `IRENA annual statistics <https://www.irena.org/Statistics/Download-Data>`__ and distributed among the nodes in a country proportional to capacity factor. (This will be updated to include capacity distributions closer to reality.)
+  Existing wind and solar capacities are retrieved from `IRENA annual statistics <https://www.irena.org/Statistics/Download-Data>`__ and distributed among the nodes in a country proportional to capacity factor. (This will be updated to include capacity distributions closer to reality.)
 
-Existing heating capacities are retrieved from the report `Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)
-<https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__
+  Existing heating capacities are retrieved from the report `Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)
+  <https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__
 
-The heating capacities are assumed to have a lifetime indicated by the parameter lifetime in the configuration file, e.g 25 years. They are assumed to be decommissioned linearly starting on the base year, e.g., from 2020 to 2045.
+  The heating capacities are assumed to have a lifetime indicated by the parameter lifetime in the configuration file, e.g 25 years. They are assumed to be decommissioned linearly starting on the base year, e.g., from 2020 to 2045.
 
-Then, the resulting network is saved in ``results/run_name/networks/prenetworks-brownfield``.
+  Then, the resulting network is saved in ``results/run_name/networks/prenetworks-brownfield``.
 
-rule add_brownfield
-===================
+- rule add_brownfield
 
-The rule add_brownfield loads the network in ``results/run_name/networks/prenetworks`` and performs the following operation:
+  The rule add_brownfield loads the network in ``results/run_name/networks/prenetworks`` and performs the following operation:
 
-1.Read the capacities optimized in the previous time step and add them to the network if they are still in operation (i.e., if they fulfill planning horizon < commissioned year + lifetime)
+  1. Read the capacities optimized in the previous time step and add them to the network if they are still in operation (i.e., if they fulfill planning horizon < commissioned year + lifetime)
 
-Then, the resulting network is saved in ``results/run_name/networks/prenetworks_brownfield``.
+  Then, the resulting network is saved in ``results/run_name/networks/prenetworks_brownfield``.

From 76065624496203a49c8d41b9aa751fc49114438f Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Wed, 28 Sep 2022 16:09:53 +0200
Subject: [PATCH 144/243] update sector opt for co2 seq potential

---
 config.default.yaml      | 2 +-
 scripts/solve_network.py | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 9824b1f5..3e6576e3 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -33,7 +33,7 @@ scenario:
   # A for agriculture, forestry and fishing
   # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
   # solar+p3 multiplies the available installable potential by factor 3
-  # co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
+  # seq2 (or e.g. seq0p5) multiplies the potential of CO2 sequestration by a factor 2 (or e.g. 0.5)
   # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
   # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
   # emissions throughout the transition path in the timeframe determined by the
diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index 563d8c29..626cc2a7 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -227,7 +227,8 @@ def add_co2_sequestration_limit(n, sns):
     limit = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
     for o in opts:
         if not "seq" in o: continue
-        limit = float(o[o.find("seq")+3:])
+        factor = float(o[o.find("seq")+3:])
+        limit *= factor
         break
 
     name = 'co2_sequestration_limit'

From 9a74e3d699e9addd1476435dc2796251056c0b5d Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Thu, 29 Sep 2022 09:12:36 +0200
Subject: [PATCH 145/243] set co2seq to limit of sequestration potential

---
 config.default.yaml      | 2 +-
 scripts/solve_network.py | 3 +--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 3e6576e3..bab9d483 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -33,7 +33,7 @@ scenario:
   # A for agriculture, forestry and fishing
   # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
   # solar+p3 multiplies the available installable potential by factor 3
-  # seq2 (or e.g. seq0p5) multiplies the potential of CO2 sequestration by a factor 2 (or e.g. 0.5)
+  # seq400 sets the potential of CO2 sequestration to 400 Mt CO2 per year
   # dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
   # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
   # emissions throughout the transition path in the timeframe determined by the
diff --git a/scripts/solve_network.py b/scripts/solve_network.py
index 626cc2a7..93442a41 100644
--- a/scripts/solve_network.py
+++ b/scripts/solve_network.py
@@ -227,8 +227,7 @@ def add_co2_sequestration_limit(n, sns):
     limit = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
     for o in opts:
         if not "seq" in o: continue
-        factor = float(o[o.find("seq")+3:])
-        limit *= factor
+        limit = float(o[o.find("seq")+3:]) * 1e6
         break
 
     name = 'co2_sequestration_limit'

From 4f7b2b9868d14864f2dcf8f8b674c6118a777590 Mon Sep 17 00:00:00 2001
From: nnhjy <jiangyi.huang@outlook.com>
Date: Thu, 6 Oct 2022 18:43:41 +0200
Subject: [PATCH 146/243] untrack *.json

---
 .gitignore | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.gitignore b/.gitignore
index aa531c3d..a7049efe 100644
--- a/.gitignore
+++ b/.gitignore
@@ -46,3 +46,5 @@ config.yaml
 doc/_build
 
 *.xls
+
+*.geojson

From 374a7900f3c3ef2c8456801931aa8e0d30882536 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 16:17:54 +0200
Subject: [PATCH 147/243] Linking test

---
 doc/supply_demand.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 9fd277a8..62d41b8f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -354,7 +354,7 @@ The overarching modelling procedure can be described as follows. First, the ener
 
 Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_,  `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_. 
 
-
+:ref:`process-emissions`	
 Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane. 
 
 Hydrogen for high-temperature process heat is not part of the model currently. 
@@ -369,6 +369,8 @@ The following figure shows the final consumption of energy and non-energy feedst
 The following figure shows the process emissions in industry today (top bar) and in 2050 without
 carbon capture (bottom bar) assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_. 
 
+. _process-emissions:
+
 .. image:: ../graphics/process-emissions.png
 
 

From e13292da719989b0f54e5ffbe15a3beea972a8ed Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 16:22:09 +0200
Subject: [PATCH 148/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 62d41b8f..103b7222 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -354,7 +354,8 @@ The overarching modelling procedure can be described as follows. First, the ener
 
 Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_,  `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_. 
 
-:ref:`process-emissions`	
+:ref:`Process`	
+
 Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane. 
 
 Hydrogen for high-temperature process heat is not part of the model currently. 
@@ -369,7 +370,8 @@ The following figure shows the final consumption of energy and non-energy feedst
 The following figure shows the process emissions in industry today (top bar) and in 2050 without
 carbon capture (bottom bar) assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_. 
 
-. _process-emissions:
+.. _Process:
+
 
 .. image:: ../graphics/process-emissions.png
 

From f3f08340af9548b47123faca0932cdd203ecb04e Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 16:23:39 +0200
Subject: [PATCH 149/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 103b7222..329f9683 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -354,7 +354,7 @@ The overarching modelling procedure can be described as follows. First, the ener
 
 Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_,  `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_. 
 
-:ref:`Process`	
+
 
 Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane. 
 
@@ -370,7 +370,7 @@ The following figure shows the final consumption of energy and non-energy feedst
 The following figure shows the process emissions in industry today (top bar) and in 2050 without
 carbon capture (bottom bar) assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_. 
 
-.. _Process:
+
 
 
 .. image:: ../graphics/process-emissions.png

From 9cd701496c9aa930a476df7cc7a36ba73f95ef07 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 16:38:50 +0200
Subject: [PATCH 150/243] Adding chemistry section

---
 doc/supply_demand.rst | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 329f9683..dcec8079 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -451,6 +451,21 @@ $$
 
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
+The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWhel/t of chlorine and yield hydrogen at 0.937 MWhH2/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWhel/t of methanol and methane at 10.25 MWhCH4/t of methanol.
+
+		 	 	 		
+			
+				
+					
+The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWhnaphtha/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWhCH4 /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWhel/t of HVC).
+					
+The process emissions from feedstock in the chemical industry are as high as 0.369 tCO2 /t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO2 will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO2 emissions. 
+		 	 	 		
+			
+				
+					
+Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWhel/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWhel/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
+
 Agriculture demand
 =========================
 

From 9e40a0f5f2b2c483f00d025c17a53d4cbf125715 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 16:42:43 +0200
Subject: [PATCH 151/243] Add non-metallic mineral products title

---
 doc/supply_demand.rst | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index dcec8079..9adbb9cf 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -452,20 +452,23 @@ $$
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
 The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWhel/t of chlorine and yield hydrogen at 0.937 MWhH2/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWhel/t of methanol and methane at 10.25 MWhCH4/t of methanol.
-
 		 	 	 		
-			
-				
-					
+						
 The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWhnaphtha/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWhCH4 /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWhel/t of HVC).
 					
 The process emissions from feedstock in the chemical industry are as high as 0.369 tCO2 /t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO2 will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO2 emissions. 
-		 	 	 		
-			
-				
+		 	 	 					
 					
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWhel/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWhel/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
 
+*Non-metallic Mineral Products*
+
+This subsector includes the manufacturing of cement, ceramics, and glass. 
+
+
+
+
+
 Agriculture demand
 =========================
 

From 59027615c82e3f6f6303e4a605336658dd8de275 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 16:54:57 +0200
Subject: [PATCH 152/243] Replacing CO2

---
 doc/supply_demand.rst | 28 +++++++++++++++++-----------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 9adbb9cf..1ddab3d9 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -246,7 +246,7 @@ Methane supply
 
 In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modeled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
 Biogas can be upgraded to methane. 
-Synthetic methane can be produced by processing hydrogen and captures CO2 in the Sabatier reaction 
+Synthetic methane can be produced by processing hydrogen and captures CO$_2$ in the Sabatier reaction 
 
 $$                   
 CO_2 + 4H_2 → CH_4 + 2H_2O
@@ -321,7 +321,7 @@ Land transport [(see :ref:`Land transport`)  that is not electrified or converte
 
 Oil-based products supply 
 ======================== 							
-Oil-based products can be either of fossil origin or synthetically produced by combining H2 (see :ref:`Hydrogen supply`) and captured CO2 (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
+Oil-based products can be either of fossil origin or synthetically produced by combining H2 (see :ref:`Hydrogen supply`) and captured CO$_2$ (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
 
 $$
 𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
@@ -434,8 +434,6 @@ For the remaining subprocesses in this sector, the following transformations are
 
 *Chemicals Industry*
 
-The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
-
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
 				
 The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (see :ref:`Hydrogen supply`), for methane (see :ref:`Methane supply`), and for oil-based products (see :ref:`Oil-based products supply`). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the opti- misation.
@@ -456,18 +454,26 @@ The total production and specific energy consumption of chlorine and methanol is
 						
 The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWhnaphtha/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWhCH4 /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWhel/t of HVC).
 					
-The process emissions from feedstock in the chemical industry are as high as 0.369 tCO2 /t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO2 will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO2 emissions. 
+The process emissions from feedstock in the chemical industry are as high as 0.369 t$_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO$_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO$_2$ emissions. 
 		 	 	 					
 					
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWhel/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWhel/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
 
-*Non-metallic Mineral Products*
 
+Non-metallic Mineral Products
+=========================
 This subsector includes the manufacturing of cement, ceramics, and glass. 
 
+*Cement*
+
+Cement is used in construction to make concrete. The production of cement involves high energy consumption and large process emissions. The calcination of limestone to chemically reactive calcium oxide, also known as lime, involves process emissions of 0.54 t$_{CO_2}$ /t cement (see `Akhtar et al. <https://doi.org/10.1109/CITCON.2013.6525276>`_.		
 
 
+$$
+CaCO_3 → CaO + CO_2 
+$$
 
+Additionally, CO$_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
 
 Agriculture demand
 =========================
@@ -552,17 +558,17 @@ For the following point source emissions, carbon capture is applicable:
 Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
 is currently ignored.
 
-DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO2 prior to storage which consumes electricity and rejects heat. 
+DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO$_2$ prior to storage which consumes electricity and rejects heat. 
 
 *Carbon dioxide usage*
 
-Captured CO2 can be used to produce synthetic methane and synthetic oil products (e.g.
-naphtha). If captured carbon is used, the CO2 emissions of the synthetic fuels are net-neutral. 
+Captured CO$_2$ can be used to produce synthetic methane and synthetic oil products (e.g.
+naphtha). If captured carbon is used, the CO$_2$ emissions of the synthetic fuels are net-neutral. 
 
 *Carbon dioxide sequestration*
 
-Captured CO2 can also be sequestered underground up to an annual sequestration limit of 200 MtCO2/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO2 transport constraints are neglected. Since CO2 sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+Captured CO$_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO$_2$ transport constraints are neglected. Since CO$_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
 
 *Carbon dioxide transport*
 
-Carbon dioxide can be modelled as a single node for Europe (in this case, CO2 transport constraints are neglected). A network for modelling the transport of CO2 among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
+Carbon dioxide can be modelled as a single node for Europe (in this case, CO$_2$ transport constraints are neglected). A network for modelling the transport of CO$_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 

From dfd0d080ed16923de3c8972a0f67fc87db759500 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 17:05:34 +0200
Subject: [PATCH 153/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 1ddab3d9..67b9b0c2 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -346,7 +346,7 @@ The Subsection overview below provides a general description of the modelling ap
 
 .. _Overview:
 
-*Overview*
+**Overview**
 
 Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia (NH3), ethylene (C2H4) and methanol (CH3OH). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
 
@@ -383,7 +383,7 @@ Inside each country the industrial demand is then distributed using the `Hotmaps
 
 .. _Iron and Steel:
 
-*Iron and Steel*
+**Iron and Steel**
 
 Two alternative routes are used today to manufacture steel in Europe. The primary route (integrated steelworks) represents 60% of steel production, while the secondary route (electric arc furnaces, EAF), represents the other 40% `(Lechtenböhmer et. al) <https://doi.org/10.1016/j.energy.2016.07.110>`_.
 					
@@ -432,7 +432,7 @@ For the remaining subprocesses in this sector, the following transformations are
 
 .. _Chemicals Industry:
 
-*Chemicals Industry*
+**Chemicals Industry**
 
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
 				
@@ -460,8 +460,9 @@ The process emissions from feedstock in the chemical industry are as high as 0.3
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWhel/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWhel/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
 
 
-Non-metallic Mineral Products
-=========================
+
+**Non-metallic Mineral Products**
+
 This subsector includes the manufacturing of cement, ceramics, and glass. 
 
 *Cement*
@@ -475,6 +476,21 @@ $$
 
 Additionally, CO$_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
 
+Cement process emissions can be captured assuming a capture rate of 90%. Whether emissions are captured is decided by the model taking into account the capital costs of carbon capture modules. The electricity and heat demand of process emission carbon capture is currently ignored. For net-zero emission scenarios, the remaining process emissions need to be compensated by negative emissions. 		
+
+With the exception of electricity demand and biomass demand for low-temperature heat (0.06 MWh/t and 0.2 MWh/t), the final energy consumption of this subsector is assumed to be supplied by methane (0.52 MWh/t), which is capable of delivering the required high-temperature heat. ‘This implies a switch from burning solid fuels to burning gas which will require adjustments of the `kilns <10.1109/CITCON.2013.6525276>`_. The share of fossil vs. synthetic methane consumed is a result of the optimisation
+
+
+*Ceramics*
+
+The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw ma- terials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t$_{CO_2} $/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+
+*Glass*
+
+The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh$_{el}l/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t$_{CO_2} $/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MWh$_{el}$/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+
+
+**Non-ferrous Metals**
 Agriculture demand
 =========================
 

From 6bc99291c9429b82f58fe5e1bfdf304f5bdf310f Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:10:58 +0200
Subject: [PATCH 154/243] Other industry subsectors

---
 doc/supply_demand.rst | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 67b9b0c2..6f1cfb49 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -491,6 +491,29 @@ The production of glass is assumed to be fully electrified based on the current
 
 
 **Non-ferrous Metals**
+
+The non-ferrous metal subsector includes the manufacturing of base metals (aluminium, copper, lead, zink), precious metals (gold, silver), and technology metals (molybdenum, cobalt, silicon).					
+
+The manufacturing of aluminium accounts for more than half of the final energy con- sumption of this subsector. Two alternative processing routes are used today to manu- facture aluminium in Europe. The primary route represents 40% of the aluminium pro- duction, while the secondary route represents the remaining 60%.		
+
+The primary route involves two energy-intensive processes: the production of alumina from bauxite (aluminium ore) and the electrolysis to transform alumina into aluminium via the Hall-Héroult process
+
+$$
+2Al_2O_3 +3C → 4Al+3CO_2	
+$$
+
+The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina which is supplied by methane and causes process emissions of 1.5 t$_{CO_2}$/t aluminium. According to `Friedrichsen et al. <http://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, inert anodes might become commercially available by 2030 that would eliminate the process emissions, but they are not included in the model. Assuming all subprocesses are electrified, the primary route requires 15.4 MWh$_{el}$/t of aluminium.
+					
+In the secondary route, scrap aluminium is remelted. The energy demand for this process is only 10% of the primary route and there are no associated process emissions. Assuming all subprocesses are electrified, the secondary route requires 1.7 MWh/t of aluminium. The share of aliminum manufactured by the primary and secondary route can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L297>`_]
+	
+For the other non-ferrous metals, we assume the electrification of the entire manufactur- ing process with an average electricity demand of 3.2 MWh$_{el}$/t lead equivalent. 			
+
+**Other Industry Subsectors**
+
+The remaining industry subsectors include (a) pulp, paper, printing, (b) food, beverages, tobacco, (c) textiles and leather, (d) machinery equipment, (e) transport equipment, (f) wood and wood products, (g) others. Low- and mid-temperature process heat in these industries is assumed to be `supplied by biomass <https://doi.org/10.1016/j.rser.2021.110856>`_  while the remaining processes are electrified. None of the subsectors involve process emissions.
+
+Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC IDEES database <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_.  Missing countries are filled with `eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat) machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is for this sector is classified as services rural heat. Time series for demands are assumed to be constant and distributed inside countries in proportion to population. 
+
 Agriculture demand
 =========================
 

From c2cf87087693c17922bb6160d8c6faa8914c3e3d Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:24:52 +0200
Subject: [PATCH 155/243] Minor edits

---
 doc/supply_demand.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 6f1cfb49..03b3e198 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -63,7 +63,7 @@ Hot water demand is assumed to be constant throughout the year.
 
 *Urban and rural heating*
 
-For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
+For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
 
 *Cooling demand*
 Cooling is electrified and is included in the electricity demand. Cooling demand is assumed to remain at current levels.  An example of regional distribution of the total heat demand for network 181 regions is depicted below.
@@ -90,11 +90,11 @@ Heat supply
 
 Different supply options are available depending on whether demand is met centrally through district heating systems, or decentrally through appliances in individual buildings. 
 
-*Urban central heat*
+**Urban central heat**
 
 For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more setailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`
 
-*Residential and Urban decentral heat*
+**Residential and Urban decentral heat**
 
 Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
 Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `Technology-data repository <https://github.com/PyPSA/technology-data>`_.
@@ -103,7 +103,7 @@ Below are more detailed explanations for each heating supply component, all of w
 
 .. _Large-scale CHP:
 
-*Large-scale CHP*
+**Large-scale CHP**
 
 Large Combined Heat and Power plants are included in the model if it is specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
  
@@ -115,11 +115,11 @@ The methane CHP is modeled on the Danish Energy Agency (DEA) “Gas turbine simp
 
 NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the study by `Brown et al. <https://arxiv.org/abs/1801.05290>`_ We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
 
-*Micro-CHP*
+**Micro-CHP**
 
 Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
 
-*Heat pumps*
+**Heat pumps**
 
 The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter (refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
@@ -141,7 +141,7 @@ Resistive heaters produce heat with a fixed conversion efficiency (refer to `Tec
 *Gas, oil, and biomass boilers* 
 
 Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option.
-Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas ,oil or biomass.
+Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas, oil or biomass.
 
 *Solar thermal collectors* 
 
@@ -606,7 +606,7 @@ naphtha). If captured carbon is used, the CO$_2$ emissions of the synthetic fuel
 
 *Carbon dioxide sequestration*
 
-Captured CO$_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO$_2$ transport constraints are neglected. Since CO$_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+Captured CO$_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO$_2$ transport constraints are neglected. Since CO$_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
 
 *Carbon dioxide transport*
 

From 5ca89f895552ffc76f26a45a722f0319de5f3f91 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:31:36 +0200
Subject: [PATCH 156/243] Typos and modeled to modelled (UK version)

---
 doc/supply_demand.rst | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 03b3e198..e7efc226 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -99,7 +99,7 @@ For large-scale district heating systems the following options are available: co
 Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
 Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `Technology-data repository <https://github.com/PyPSA/technology-data>`_.
 
-Below are more detailed explanations for each heating supply component, all of which are modeled as `Links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_. in PyPSA-Eue-Sec.
+Below are more detailed explanations for each heating supply component, all of which are modelled as `links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_ in PyPSA-Eur-Sec.
 
 .. _Large-scale CHP:
 
@@ -160,7 +160,7 @@ For the myopic transition paths, capacities already existing for technologies su
 
 Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 
-Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modeled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
+Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modelled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
 A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
 *Retrofitting of the thermal envelope of buildings*
@@ -244,7 +244,7 @@ Methane is used in individual and large-scale gas boilers, in CHP plants with an
 Methane supply                          
 ===================================
 
-In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modeled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
+In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modelled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
 Biogas can be upgraded to methane. 
 Synthetic methane can be produced by processing hydrogen and captures CO$_2$ in the Sabatier reaction 
 
@@ -306,7 +306,7 @@ The transport of solid biomass can either be assumed unlimited between countries
 
 *Biogas transport and use*
 
-Biogas will be aggregated into a common European resources if a gas network is not modeled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
+Biogas will be aggregated into a common European resources if a gas network is not modelled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
 The model can only use biogas by first upgrading it to natural gas quality [see :ref:`Methane supply`] (bio methane) which is fed into the general gas network.
 
 .. _Oil-based products demand:
@@ -559,7 +559,7 @@ All land transport that is not specified to be either BEV or FCEV will be treate
 
 **Aviation**
 
-The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modeled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
+The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modelled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
 
 .. _Shipping:
 

From 0f86d52c038753d3d7f17d2d06205dab642b7ff4 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:37:43 +0200
Subject: [PATCH 157/243] Typos and formatting

---
 doc/supply_demand.rst | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index e7efc226..2a4d6fee 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -66,6 +66,7 @@ Hot water demand is assumed to be constant throughout the year.
 For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
 
 *Cooling demand*
+
 Cooling is electrified and is included in the electricity demand. Cooling demand is assumed to remain at current levels.  An example of regional distribution of the total heat demand for network 181 regions is depicted below.
 
 .. image:: ../graphics/demand-map-heat.png
@@ -92,12 +93,12 @@ Different supply options are available depending on whether demand is met centra
 
 **Urban central heat**
 
-For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large- scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more setailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`
+For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large-scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more detailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`
 
 **Residential and Urban decentral heat**
 
 Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
-Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `Technology-data repository <https://github.com/PyPSA/technology-data>`_.
+Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `technology-data repository <https://github.com/PyPSA/technology-data>`_.
 
 Below are more detailed explanations for each heating supply component, all of which are modelled as `links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_ in PyPSA-Eur-Sec.
 
@@ -127,36 +128,36 @@ $$ &Delta; T = T_(sink) − T_(source) $$
 
 For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file] For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
 
-$$ COP (&Delta; T) = 6.81 + 0.121&Delta; T + 0.000630.&Delta; T^2; $$   
+$$ COP (&Delta; T) = 6.81 + 0.121&Delta; T + 0.000630&Delta; T^2; $$   
 
 for ground-sourced heat pumps (GSHP), we use the function:
 
 $$  COP(&Delta; T) = 8.77 + 0.150&Delta; T + 0.000734&Delta; T^2 $$     
 
-*Resistive heaters*
+**Resistive heaters**
 
-Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option
+Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option.
 Resistive heaters produce heat with a fixed conversion efficiency (refer to `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ). 
 
-*Gas, oil, and biomass boilers* 
+**Gas, oil, and biomass boilers**
 
 Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option.
 Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas, oil or biomass.
 
-*Solar thermal collectors* 
+**Solar thermal collectors** 
 
 Can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option.
 Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the Config file, which are then used by the atlite tool to calculate the solar resource time series.
 
-*Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants*
+**Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants**
 
 Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ , methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
 
-*Existing heating capacities and decommissioning*
+**Existing heating capacities and decommissioning**
 
 For the myopic transition paths, capacities already existing for technologies supplying heat are retrieved from `“Mapping and analyses of the current and future (2020 - 2030)” <https://ec.europa.eu/energy/en/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment>`_ . For the sake of simplicity, coal, oil and gas boiler capacities are assimilated to gas boilers. Besides that, existing capacities for heat resistors, air-sourced and ground-sourced heat pumps are included in the model. For heating capacities, 25% of existing capacities in 2015 are assumed to be decommissioned in every 5-year time step after 2020.
 
-*Thermal Energy Storage* 
+**Thermal Energy Storage** 
 
 Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 

From c3d221340238e70b074eea1882ce4100094a9ce0 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:39:07 +0200
Subject: [PATCH 158/243] Typos

---
 doc/supply_demand.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 2a4d6fee..888c066f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -164,10 +164,10 @@ Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modelled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
 A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
-*Retrofitting of the thermal envelope of buildings*
+**Retrofitting of the thermal envelope of buildings**
 
 Co-optimising building renovation is only enabled if in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_ file. To reduce the computational burden,
-default setting is set as false
+default setting is set as false.
 
 Renovation of the thermal envelope reduces the space heating demand and is
 optimised at each node for every heat bus. Renovation measures through additional

From 87c444c898ae01dff1173a7ddb3a99d193601905 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:46:39 +0200
Subject: [PATCH 159/243] formatting latex math

---
 doc/supply_demand.rst | 38 +++++++++++++++++++-------------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 888c066f..b3d28397 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -201,7 +201,7 @@ Hydrogen is also used for transport applications (see :ref:`Transportation`), wh
 Hydrogen supply
 =============================
 
-Today, most of the H2 consumed globally is produced from natural gas by steam methane reforming (SMR)
+Today, most of the H $_2$ consumed globally is produced from natural gas by steam methane reforming (SMR)
 
 $$
 CH_4 + H_2O → CO + 3H_2
@@ -214,7 +214,7 @@ CO + H_2O → CO_2 + H_2
 $$
 
 SMR is included `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245>`_. 
-PyPSA-Eur-Sec allows this route of H2 production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
+PyPSA-Eur-Sec allows this route of H $_2$ production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
 
 Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
 
@@ -224,12 +224,12 @@ $$
 
 For the electrolysis, alkaline electrolysers are chosen since they have lower cost and higher cumulative installed capacity than polymer electrolyte membrane (PEM) electrolysers. The techno-economic assumptions are taken from the technology-data repository. Waste heat from electrolysis is not leveraged in the model.
 
-*Transport*
+**Transport**
 
-Hydrogen is transported by pipelines. H2 pipelines are endogenously generated, either via a  greenfield H2 network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
+Hydrogen is transported by pipelines. H $_2$ pipelines are endogenously generated, either via a  greenfield H $_2$ network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
 New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
 
-*Storage*
+**Storage**
 
 Hydrogen can be stored in overground steel tanks or `underground salt caverns <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L250>`_. For the latter, energy storage capacities in every country are limited to the potential estimation for onshore salt caverns within `50 km <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L251>`_ of shore to avoid environmental issues associated with brine solution disposal. Underground storage potentials for hydrogen in European salt caverns is acquired from `Caglayan et al. <https://doi.org/10.1016/j.ijhydene.2019.12.161>`_
 
@@ -247,7 +247,7 @@ Methane supply
 
 In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modelled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
 Biogas can be upgraded to methane. 
-Synthetic methane can be produced by processing hydrogen and captures CO$_2$ in the Sabatier reaction 
+Synthetic methane can be produced by processing hydrogen and captures CO $_2$ in the Sabatier reaction 
 
 $$                   
 CO_2 + 4H_2 → CH_4 + 2H_2O
@@ -316,13 +316,13 @@ The model can only use biogas by first upgrading it to natural gas quality [see
 Oil-based products demand
 ========================
 Naphtha is used as a feedstock in the chemicals industry (see :ref:`Chemicals Industry`). Furthermore, kerosene is used as transport fuel in the aviation sector (see :ref:`Aviation`). Non-electrified agriculture machinery also consumes gasoline. 
-Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using H2-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
+Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using H $_2$-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
 
 .. _Oil-based products supply:
 
 Oil-based products supply 
 ======================== 							
-Oil-based products can be either of fossil origin or synthetically produced by combining H2 (see :ref:`Hydrogen supply`) and captured CO$_2$ (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
+Oil-based products can be either of fossil origin or synthetically produced by combining H $_2$ (see :ref:`Hydrogen supply`) and captured CO $_2$ (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
 
 $$
 𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
@@ -408,7 +408,7 @@ $$
 					
 The primary route of steelmaking implies large process emissions of 0.22 t $_{CO_2}$ /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_. 
 					
-In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the CO$_2$ emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t $_{CO_2}$ /t of steel.
+In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the CO $_2$ emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t $_{CO_2}$ /t of steel.
 					
 We assume that the primary route can be replaced by a third route in 2050, using direct reduced iron (DRI) and subsequent processing in an EAF.
 					
@@ -450,15 +450,15 @@ $$
 
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
-The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWhel/t of chlorine and yield hydrogen at 0.937 MWhH2/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWhel/t of methanol and methane at 10.25 MWhCH4/t of methanol.
+The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh $_{el}$/t of chlorine and yield hydrogen at 0.937 MWh $_{H_2}$/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh $_{el}$/t of methanol and methane at 10.25 MWhCH4/t of methanol.
 		 	 	 		
 						
-The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWhnaphtha/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWhCH4 /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWhel/t of HVC).
+The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWhnaphtha/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWhCH4 /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh $_{el}$/t of HVC).
 					
-The process emissions from feedstock in the chemical industry are as high as 0.369 t$_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO$_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO$_2$ emissions. 
+The process emissions from feedstock in the chemical industry are as high as 0.369 t$_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO $_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO $_2$ emissions. 
 		 	 	 					
 					
-Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWhel/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWhel/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
+Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWh $_{el}$/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWh $_{el}$/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
 
 
 
@@ -475,7 +475,7 @@ $$
 CaCO_3 → CaO + CO_2 
 $$
 
-Additionally, CO$_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
+Additionally, CO $_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
 
 Cement process emissions can be captured assuming a capture rate of 90%. Whether emissions are captured is decided by the model taking into account the capital costs of carbon capture modules. The electricity and heat demand of process emission carbon capture is currently ignored. For net-zero emission scenarios, the remaining process emissions need to be compensated by negative emissions. 		
 
@@ -598,17 +598,17 @@ For the following point source emissions, carbon capture is applicable:
 Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
 is currently ignored.
 
-DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO$_2$ prior to storage which consumes electricity and rejects heat. 
+DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO $_2$ prior to storage which consumes electricity and rejects heat. 
 
 *Carbon dioxide usage*
 
-Captured CO$_2$ can be used to produce synthetic methane and synthetic oil products (e.g.
-naphtha). If captured carbon is used, the CO$_2$ emissions of the synthetic fuels are net-neutral. 
+Captured CO $_2$ can be used to produce synthetic methane and synthetic oil products (e.g.
+naphtha). If captured carbon is used, the CO $_2$ emissions of the synthetic fuels are net-neutral. 
 
 *Carbon dioxide sequestration*
 
-Captured CO$_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO$_2$ transport constraints are neglected. Since CO$_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+Captured CO $_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO $_2$ transport constraints are neglected. Since CO $_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
 
 *Carbon dioxide transport*
 
-Carbon dioxide can be modelled as a single node for Europe (in this case, CO$_2$ transport constraints are neglected). A network for modelling the transport of CO$_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
+Carbon dioxide can be modelled as a single node for Europe (in this case, CO $_2$ transport constraints are neglected). A network for modelling the transport of CO $_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 

From b4457a75e3a79e7ec3bcd4198916bfe00a144a9d Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 18:49:37 +0200
Subject: [PATCH 160/243] latex formatting

---
 doc/supply_demand.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index b3d28397..a8dba4da 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -448,12 +448,12 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( $6.5 MWh_{H_2}$ / t $_{NH_3}$ ) and electricity ( $1.17 MWh_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh_${H_2}$ / t$_{NH_3}$ ) and electricity ( 1.17 MWh$_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
-The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh $_{el}$/t of chlorine and yield hydrogen at 0.937 MWh $_{H_2}$/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh $_{el}$/t of methanol and methane at 10.25 MWhCH4/t of methanol.
+The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh $_{el}$/t of chlorine and yield hydrogen at 0.937 MWh $_{H_2}$/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh $_{el}$/t of methanol and methane at 10.25 MWh $_{CH_4}$/t of methanol.
 		 	 	 		
 						
-The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWhnaphtha/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWhCH4 /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh $_{el}$/t of HVC).
+The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWh $_{naphtha}$/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWh $_{CH_4}$ /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh $_{el}$/t of HVC).
 					
 The process emissions from feedstock in the chemical industry are as high as 0.369 t$_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO $_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO $_2$ emissions. 
 		 	 	 					

From a3268604c26df3dc5dfc45fa5a480b9609827b8c Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 19:02:12 +0200
Subject: [PATCH 161/243] config file.

---
 doc/supply_demand.rst | 54 +++++++++++++++++++++----------------------
 1 file changed, 27 insertions(+), 27 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index a8dba4da..229276b0 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -45,7 +45,7 @@ Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europ
 
 *Space heating*
 
-The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec Config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
+The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
 
 .. literalinclude:: ../config.default.yaml
     :language: yaml
@@ -146,8 +146,8 @@ Similar to resistive heaters, boilers have a fixed efficiency and produce heat u
 
 **Solar thermal collectors** 
 
-Can be activated in the Config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option.
-Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the Config file, which are then used by the atlite tool to calculate the solar resource time series.
+Can be activated in the config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option.
+Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the config file, which are then used by the atlite tool to calculate the solar resource time series.
 
 **Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants**
 
@@ -162,7 +162,7 @@ For the myopic transition paths, capacities already existing for technologies su
 Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modelled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
-A thermal energy density of 46.8 kWhth/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1-exp(-1/24τ) is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in Config file. Charging and discharging efficiencies are 90% due to pipe losses.
+A thermal energy density of 46.8 kWh $_{th}$/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1- $e^{-1/24τ}$ is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
 **Retrofitting of the thermal envelope of buildings**
 
@@ -201,7 +201,7 @@ Hydrogen is also used for transport applications (see :ref:`Transportation`), wh
 Hydrogen supply
 =============================
 
-Today, most of the H $_2$ consumed globally is produced from natural gas by steam methane reforming (SMR)
+Today, most of the $H_2$ consumed globally is produced from natural gas by steam methane reforming (SMR)
 
 $$
 CH_4 + H_2O → CO + 3H_2
@@ -214,7 +214,7 @@ CO + H_2O → CO_2 + H_2
 $$
 
 SMR is included `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245>`_. 
-PyPSA-Eur-Sec allows this route of H $_2$ production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
+PyPSA-Eur-Sec allows this route of $H_2$ production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
 
 Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
 
@@ -226,7 +226,7 @@ For the electrolysis, alkaline electrolysers are chosen since they have lower co
 
 **Transport**
 
-Hydrogen is transported by pipelines. H $_2$ pipelines are endogenously generated, either via a  greenfield H $_2$ network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
+Hydrogen is transported by pipelines. $H_2$ pipelines are endogenously generated, either via a  greenfield $H_2$ network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
 New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
 
 **Storage**
@@ -247,7 +247,7 @@ Methane supply
 
 In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modelled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
 Biogas can be upgraded to methane. 
-Synthetic methane can be produced by processing hydrogen and captures CO $_2$ in the Sabatier reaction 
+Synthetic methane can be produced by processing hydrogen and captures $CO_2$ in the Sabatier reaction 
 
 $$                   
 CO_2 + 4H_2 → CH_4 + 2H_2O
@@ -316,13 +316,13 @@ The model can only use biogas by first upgrading it to natural gas quality [see
 Oil-based products demand
 ========================
 Naphtha is used as a feedstock in the chemicals industry (see :ref:`Chemicals Industry`). Furthermore, kerosene is used as transport fuel in the aviation sector (see :ref:`Aviation`). Non-electrified agriculture machinery also consumes gasoline. 
-Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using H $_2$-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
+Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using $H_2$-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
 
 .. _Oil-based products supply:
 
 Oil-based products supply 
 ======================== 							
-Oil-based products can be either of fossil origin or synthetically produced by combining H $_2$ (see :ref:`Hydrogen supply`) and captured CO $_2$ (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
+Oil-based products can be either of fossil origin or synthetically produced by combining $H_2$ (see :ref:`Hydrogen supply`) and captured $CO_2$ (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
 
 $$
 𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
@@ -349,13 +349,13 @@ The Subsection overview below provides a general description of the modelling ap
 
 **Overview**
 
-Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia (NH3), ethylene (C2H4) and methanol (CH3OH). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
+Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia ( $NH_3$), ethylene ( $C_2H_4$) and methanol ( $CH_3OH$). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
 
 The overarching modelling procedure can be described as follows. First, the energy demands and process emissions for every unit of material output are estimated based on data from the `JRC-IDEES database <https://data.europa.eu/doi/10.2760/182725>`_ and the fuel and process switching described in the subsequent sections. Second, the 2050 energy demands and process emissions are calculated using the per-unit-of-material ratios based on the industry transformations and the `country-level material production in 2015 <https://data.europa.eu/doi/10.2760/182725>`_, assuming constant material demand.
 
 Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_,  `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_. 
 
-
+a
 
 Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane. 
 
@@ -408,7 +408,7 @@ $$
 					
 The primary route of steelmaking implies large process emissions of 0.22 t $_{CO_2}$ /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_. 
 					
-In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the CO $_2$ emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t $_{CO_2}$ /t of steel.
+In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the $CO_2$ emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t $_{CO_2}$ /t of steel.
 					
 We assume that the primary route can be replaced by a third route in 2050, using direct reduced iron (DRI) and subsequent processing in an EAF.
 					
@@ -448,14 +448,14 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh_${H_2}$ / t$_{NH_3}$ ) and electricity ( 1.17 MWh$_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh $_{H_2}$ / t $_{NH_3}$ ) and electricity ( 1.17 MWh$_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
 The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh $_{el}$/t of chlorine and yield hydrogen at 0.937 MWh $_{H_2}$/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh $_{el}$/t of methanol and methane at 10.25 MWh $_{CH_4}$/t of methanol.
 		 	 	 		
 						
 The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWh $_{naphtha}$/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWh $_{CH_4}$ /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh $_{el}$/t of HVC).
 					
-The process emissions from feedstock in the chemical industry are as high as 0.369 t$_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded CO $_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated CO $_2$ emissions. 
+The process emissions from feedstock in the chemical industry are as high as 0.369 t $_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded $CO_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated $CO_2$ emissions. 
 		 	 	 					
 					
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWh $_{el}$/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWh $_{el}$/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
@@ -475,20 +475,20 @@ $$
 CaCO_3 → CaO + CO_2 
 $$
 
-Additionally, CO $_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
+Additionally, $CO_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
 
 Cement process emissions can be captured assuming a capture rate of 90%. Whether emissions are captured is decided by the model taking into account the capital costs of carbon capture modules. The electricity and heat demand of process emission carbon capture is currently ignored. For net-zero emission scenarios, the remaining process emissions need to be compensated by negative emissions. 		
 
-With the exception of electricity demand and biomass demand for low-temperature heat (0.06 MWh/t and 0.2 MWh/t), the final energy consumption of this subsector is assumed to be supplied by methane (0.52 MWh/t), which is capable of delivering the required high-temperature heat. ‘This implies a switch from burning solid fuels to burning gas which will require adjustments of the `kilns <10.1109/CITCON.2013.6525276>`_. The share of fossil vs. synthetic methane consumed is a result of the optimisation
+With the exception of electricity demand and biomass demand for low-temperature heat (0.06 MWh/t and 0.2 MWh/t), the final energy consumption of this subsector is assumed to be supplied by methane (0.52 MWh/t), which is capable of delivering the required high-temperature heat. This implies a switch from burning solid fuels to burning gas which will require adjustments of the `kilns <10.1109/CITCON.2013.6525276>`_. The share of fossil vs. synthetic methane consumed is a result of the optimisation
 
 
 *Ceramics*
 
-The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw ma- terials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t$_{CO_2} $/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw ma- terials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t $_{CO_2} $/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 *Glass*
 
-The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh$_{el}l/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t$_{CO_2} $/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MWh$_{el}$/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh$_{el}l/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t $_{CO_2} $/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW $_{el}$/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 
 **Non-ferrous Metals**
@@ -503,11 +503,11 @@ $$
 2Al_2O_3 +3C → 4Al+3CO_2	
 $$
 
-The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina which is supplied by methane and causes process emissions of 1.5 t$_{CO_2}$/t aluminium. According to `Friedrichsen et al. <http://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, inert anodes might become commercially available by 2030 that would eliminate the process emissions, but they are not included in the model. Assuming all subprocesses are electrified, the primary route requires 15.4 MWh$_{el}$/t of aluminium.
+The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina which is supplied by methane and causes process emissions of 1.5 t $_{CO_2}$/t aluminium. According to `Friedrichsen et al. <http://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, inert anodes might become commercially available by 2030 that would eliminate the process emissions, but they are not included in the model. Assuming all subprocesses are electrified, the primary route requires 15.4 MWh $_{el}$/t of aluminium.
 					
 In the secondary route, scrap aluminium is remelted. The energy demand for this process is only 10% of the primary route and there are no associated process emissions. Assuming all subprocesses are electrified, the secondary route requires 1.7 MWh/t of aluminium. The share of aliminum manufactured by the primary and secondary route can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L297>`_]
 	
-For the other non-ferrous metals, we assume the electrification of the entire manufactur- ing process with an average electricity demand of 3.2 MWh$_{el}$/t lead equivalent. 			
+For the other non-ferrous metals, we assume the electrification of the entire manufactur- ing process with an average electricity demand of 3.2 MWh $_{el}$/t lead equivalent. 			
 
 **Other Industry Subsectors**
 
@@ -532,7 +532,7 @@ Annual energy demands for land transport, aviation and shipping for every countr
 
 Both road and rail transport is combined as `land transport demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_transport_demand.py#L74>`_ although electrified rail transport is excluded because that demand is included in the current electricity demand.
 
-The most important settings for land transport are the exogenously fixed fuel mix (an option enabling the endogeous optimization of transport electrification is planned but not yet implemented) . In the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_, the share of battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCEV) can be set. The remaining percentage will be treated as internal combustion engines (ICE) that consume oil products.
+The most important settings for land transport are the exogenously fixed fuel mix (an option enabling the endogeous optimization of transport electrification is planned but not yet implemented). In the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_, the share of battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCEV) can be set. The remaining percentage will be treated as internal combustion engines (ICE) that consume oil products.
 
 *Battery Electric vehicles (BEV)*
 
@@ -598,17 +598,17 @@ For the following point source emissions, carbon capture is applicable:
 Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
 is currently ignored.
 
-DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of CO $_2$ prior to storage which consumes electricity and rejects heat. 
+DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of $CO_2$ prior to storage which consumes electricity and rejects heat. 
 
 *Carbon dioxide usage*
 
-Captured CO $_2$ can be used to produce synthetic methane and synthetic oil products (e.g.
-naphtha). If captured carbon is used, the CO $_2$ emissions of the synthetic fuels are net-neutral. 
+Captured $CO_2$ can be used to produce synthetic methane and synthetic oil products (e.g.
+naphtha). If captured carbon is used, the $CO_2$ emissions of the synthetic fuels are net-neutral. 
 
 *Carbon dioxide sequestration*
 
-Captured CO $_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `Config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, CO $_2$ transport constraints are neglected. Since CO $_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+Captured $CO_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, $CO_2$ transport constraints are neglected. Since $CO_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
 
 *Carbon dioxide transport*
 
-Carbon dioxide can be modelled as a single node for Europe (in this case, CO $_2$ transport constraints are neglected). A network for modelling the transport of CO $_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
+Carbon dioxide can be modelled as a single node for Europe (in this case, $CO_2$ transport constraints are neglected). A network for modelling the transport of $CO_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 

From e9ffaac223da70dd43158437a0384a28858b31fe Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 7 Oct 2022 19:10:05 +0200
Subject: [PATCH 162/243] Small edits

---
 doc/supply_demand.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 229276b0..5d1a6ca3 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -162,7 +162,7 @@ For the myopic transition paths, capacities already existing for technologies su
 Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modelled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
-A thermal energy density of 46.8 kWh $_{th}$/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1- $e^{-1/24τ}$ is assumed to have a time constant  of  t=180 days for central TES and  t=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in config file. Charging and discharging efficiencies are 90% due to pipe losses.
+A thermal energy density of 46.8 kWh $_{th}$/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1- $e^{-1/24τ}$ is assumed to have a time constant  of  τ=180 days for central TES and  τ=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
 **Retrofitting of the thermal envelope of buildings**
 
@@ -468,7 +468,7 @@ This subsector includes the manufacturing of cement, ceramics, and glass.
 
 *Cement*
 
-Cement is used in construction to make concrete. The production of cement involves high energy consumption and large process emissions. The calcination of limestone to chemically reactive calcium oxide, also known as lime, involves process emissions of 0.54 t$_{CO_2}$ /t cement (see `Akhtar et al. <https://doi.org/10.1109/CITCON.2013.6525276>`_.		
+Cement is used in construction to make concrete. The production of cement involves high energy consumption and large process emissions. The calcination of limestone to chemically reactive calcium oxide, also known as lime, involves process emissions of 0.54 t $_{CO_2}$ /t cement (see `Akhtar et al. <https://doi.org/10.1109/CITCON.2013.6525276>`_.		
 
 
 $$
@@ -484,18 +484,18 @@ With the exception of electricity demand and biomass demand for low-temperature
 
 *Ceramics*
 
-The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw ma- terials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t $_{CO_2} $/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw materials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t $_{CO_2} $/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 *Glass*
 
-The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh$_{el}l/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t $_{CO_2} $/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW $_{el}$/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh $_{el}l/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t $_{CO_2} $/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW $_{el}$/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 
 **Non-ferrous Metals**
 
 The non-ferrous metal subsector includes the manufacturing of base metals (aluminium, copper, lead, zink), precious metals (gold, silver), and technology metals (molybdenum, cobalt, silicon).					
 
-The manufacturing of aluminium accounts for more than half of the final energy con- sumption of this subsector. Two alternative processing routes are used today to manu- facture aluminium in Europe. The primary route represents 40% of the aluminium pro- duction, while the secondary route represents the remaining 60%.		
+The manufacturing of aluminium accounts for more than half of the final energy consumption of this subsector. Two alternative processing routes are used today to manufacture aluminium in Europe. The primary route represents 40% of the aluminium pro- duction, while the secondary route represents the remaining 60%.		
 
 The primary route involves two energy-intensive processes: the production of alumina from bauxite (aluminium ore) and the electrolysis to transform alumina into aluminium via the Hall-Héroult process
 
@@ -507,7 +507,7 @@ The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina whi
 					
 In the secondary route, scrap aluminium is remelted. The energy demand for this process is only 10% of the primary route and there are no associated process emissions. Assuming all subprocesses are electrified, the secondary route requires 1.7 MWh/t of aluminium. The share of aliminum manufactured by the primary and secondary route can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L297>`_]
 	
-For the other non-ferrous metals, we assume the electrification of the entire manufactur- ing process with an average electricity demand of 3.2 MWh $_{el}$/t lead equivalent. 			
+For the other non-ferrous metals, we assume the electrification of the entire manufacturing process with an average electricity demand of 3.2 MWh $_{el}$/t lead equivalent. 			
 
 **Other Industry Subsectors**
 
@@ -536,7 +536,7 @@ The most important settings for land transport are the exogenously fixed fuel mi
 
 *Battery Electric vehicles (BEV)*
 
-For the electrified land transport, country-specific factors are computed by comparing the `current car final energy consumption per km in <https://www.sciencedirect.com/science/article/pii/S0360544216310295>`_ (average for Europe 0.7 kWh/km) to the 0.18 kWh/km value assumed for battery-to-wheels efficiency in EVs. The characteristic `weekly profile <https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/zaehl_node.html>`_ provided by the German Federal Highway Research Institute (BASt) is used to obtain hourly time series for European countries taking into account the corresponding local times. Furthermore, a temperature dependence is included in the time series to account for heating/cooling demand in transport. For temperatures `below <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L166>`_/`above <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L165>`_ certain threshold values, e.g. 15C/20C, `temperature coefficients <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L169>`_ of typically 0.98%/C and 0.63%/C are assumed, based on the `paper <https://www.sciencedirect.com/science/article/pii/S036054421831288X>`_.
+For the electrified land transport, country-specific factors are computed by comparing the `current car final energy consumption per km in <https://www.sciencedirect.com/science/article/pii/S0360544216310295>`_ (average for Europe 0.7 kWh/km) to the 0.18 kWh/km value assumed for battery-to-wheels efficiency in EVs. The characteristic `weekly profile <https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/zaehl_node.html>`_ provided by the German Federal Highway Research Institute (BASt) is used to obtain hourly time series for European countries taking into account the corresponding local times. Furthermore, a temperature dependence is included in the time series to account for heating/cooling demand in transport. For temperatures `below <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L166>`_/`above <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L165>`_ certain threshold values, e.g. 15 °C/20 °C, `temperature coefficients <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L169>`_ of typically 0.98%/°C and 0.63%/°C are assumed, based on the `paper <https://www.sciencedirect.com/science/article/pii/S036054421831288X>`_.
 
 For BEVs the user can define the `storage energy capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L173>`_, `charging power capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L176>`_, and `charging efficiency <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L174>`_.
 
@@ -607,7 +607,7 @@ naphtha). If captured carbon is used, the $CO_2$ emissions of the synthetic fuel
 
 *Carbon dioxide sequestration*
 
-Captured $CO_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt$_{CO_2}$/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, $CO_2$ transport constraints are neglected. Since $CO_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+Captured $CO_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt $_{CO_2}$/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, $CO_2$ transport constraints are neglected. Since $CO_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
 
 *Carbon dioxide transport*
 

From 9bc0e2afb7821d2a0204049af7c2299722ae808f Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Tue, 25 Oct 2022 09:23:35 +0200
Subject: [PATCH 163/243] update description of mechanical and chemical
 recycling of plastics

---
 doc/supply_demand.rst | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 5d1a6ca3..8e30346f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -437,7 +437,7 @@ For the remaining subprocesses in this sector, the following transformations are
 
 The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
 				
-The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (see :ref:`Hydrogen supply`), for methane (see :ref:`Methane supply`), and for oil-based products (see :ref:`Oil-based products supply`). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the opti- misation.
+The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (see :ref:`Hydrogen supply`), for methane (see :ref:`Methane supply`), and for oil-based products (see :ref:`Oil-based products supply`). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the optimisation.
 					
 The basic chemicals consumption data from the `JRC IDEES  <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ database comprises high- value chemicals (ethylene, propylene and BTX), chlorine, methanol and ammonia. However, it is necessary to separate out these chemicals because their current and future production routes are different.
 					
@@ -448,7 +448,7 @@ N_2 + 3H_2 → 2NH_3
 $$
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh $_{H_2}$ / t $_{NH_3}$ ) and electricity ( 1.17 MWh$_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh $_{H_2}$ / t $_{NH_3}$ ) and electricity ( 1.17 MWh $_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
 The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh $_{el}$/t of chlorine and yield hydrogen at 0.937 MWh $_{H_2}$/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh $_{el}$/t of methanol and methane at 10.25 MWh $_{CH_4}$/t of methanol.
 		 	 	 		
@@ -457,9 +457,8 @@ The production of ammonia, methanol, and chlorine production is deducted from th
 					
 The process emissions from feedstock in the chemical industry are as high as 0.369 t $_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded $CO_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated $CO_2$ emissions. 
 		 	 	 					
-					
-Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission (LINK TO PROCESS EMISSIONS FIGURE). We assume that 30% of plastics are mechanically recycled requiring 0.547 MWh $_{el}$/t of HVC (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), 15% of plastics are chemically recycled requiring 6.9 MWh $_{el}$/t of HVC based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, and 10% of plastics are reused (equivalent to reduction in demand). The remaining 45% need to be produced from primary feedstock. In comparison, Material Economics presents a scenario with circular economy scenario with 27% primary production, 18% mechanical recycling, 28% chemical recycling, and 27% reuse. Another new-processes scenario has 33% primary production, 14% mechanical recycling, 40% chemical recycling, and 13% reuse. 
-
+Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are asumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as 
+the percentage that is chemically recycled `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling process are respectively `0.547 MWh $_{el}$/t of HVC <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_ (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and `6.9 MWh $_{el}$/t of HVC of HVC <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_ based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, ). 
 
 
 **Non-metallic Mineral Products**

From 11585429b0d8e468b65a7887a748d1ad387c383d Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Tue, 25 Oct 2022 09:28:18 +0200
Subject: [PATCH 164/243] small spelling corrections

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 8e30346f..51bb1ea9 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -458,7 +458,7 @@ The production of ammonia, methanol, and chlorine production is deducted from th
 The process emissions from feedstock in the chemical industry are as high as 0.369 t $_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded $CO_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated $CO_2$ emissions. 
 		 	 	 					
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are asumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as 
-the percentage that is chemically recycled `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling process are respectively `0.547 MWh $_{el}$/t of HVC <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_ (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and `6.9 MWh $_{el}$/t of HVC of HVC <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_ based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report, ). 
+the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling process are respectively 0.547 MWh $_{el}$/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh $_{el}$/t of HVC (as indicated in the config file `<https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
 
 
 **Non-metallic Mineral Products**

From f6e017257e938ad126e5d1812f575f6d97b293f7 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Tue, 25 Oct 2022 09:29:49 +0200
Subject: [PATCH 165/243] correct small spelling mistakes

---
 doc/supply_demand.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 51bb1ea9..03160f8e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -457,8 +457,8 @@ The production of ammonia, methanol, and chlorine production is deducted from th
 					
 The process emissions from feedstock in the chemical industry are as high as 0.369 t $_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded $CO_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated $CO_2$ emissions. 
 		 	 	 					
-Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are asumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as 
-the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling process are respectively 0.547 MWh $_{el}$/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh $_{el}$/t of HVC (as indicated in the config file `<https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
+Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are assumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as 
+the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling processes are respectively 0.547 MWh $_{el}$/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh $_{el}$/t of HVC (as indicated in the config file `<https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
 
 
 **Non-metallic Mineral Products**

From 80868e5f0fbbfabe2b0a57bf677649c3b8561a75 Mon Sep 17 00:00:00 2001
From: "Huang, Jiangyi" <jiangyi.huang@outlook.com>
Date: Thu, 3 Nov 2022 13:46:20 +0100
Subject: [PATCH 166/243] Update make_summary.py

The value for `na_action` needs to be "ignore" or "None".
---
 scripts/make_summary.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/scripts/make_summary.py b/scripts/make_summary.py
index 8d5f4e48..f9c74c89 100644
--- a/scripts/make_summary.py
+++ b/scripts/make_summary.py
@@ -273,7 +273,7 @@ def calculate_supply(n, label, supply):
 
             for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
 
-                items = c.df.index[c.df["bus" + end].map(bus_map, na_action=False)]
+                items = c.df.index[c.df["bus" + end].map(bus_map, na_action=None)]
 
                 if len(items) == 0:
                     continue
@@ -318,7 +318,7 @@ def calculate_supply_energy(n, label, supply_energy):
 
             for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
 
-                items = c.df.index[c.df["bus" + str(end)].map(bus_map, na_action=False)]
+                items = c.df.index[c.df["bus" + str(end)].map(bus_map, na_action=None)]
 
                 if len(items) == 0:
                     continue

From 6c51152ab5280da05fd3a3eaa05d54ed0b67ad8b Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 10 Nov 2022 15:29:52 +0100
Subject: [PATCH 167/243] config: shipping now defaults to (synthetic) oil
 demand not hydrogen

---
 config.default.yaml | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 4cf7f5fa..4078ee01 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -195,14 +195,7 @@ sector:
   agriculture_machinery_electric_efficiency: 0.3 # electricity per use
   shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share:  # 1 means all hydrogen FC
-    2020: 0
-    2025: 0
-    2030: 0.05
-    2035: 0.15
-    2040: 0.3
-    2045: 0.6
-    2050: 1
+  shipping_hydrogen_share:  0
   time_dep_hp_cop: true #time dependent heat pump coefficient of performance
   heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
    # conservatively high to cover hot water and space heating in poorly-insulated buildings

From 3815d0da619ac1c4d3ae37ce48d6a2992862e652 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 10 Nov 2022 15:31:25 +0100
Subject: [PATCH 168/243] add release note

---
 doc/release_notes.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 1eb33652..51c533f4 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -80,6 +80,8 @@ incorporates retrofitting options to hydrogen.
 
 * Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials.
 
+* Shipping demand now defaults to (synthetic) oil rather than liquefied hydrogen until 2050.
+
 **Bugfixes**
 
 * The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)

From 84618b3ee1c72c32722cf89b73282cc26cc19422 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 13 Nov 2022 13:43:05 +0100
Subject: [PATCH 169/243] distribute international shipping by port trade
 volumes

---
 Snakefile                         |  13 +
 data/attributed_ports.json        | 861 ++++++++++++++++++++++++++++++
 scripts/build_shipping_demand.py  |  48 ++
 scripts/prepare_sector_network.py |  31 +-
 4 files changed, 940 insertions(+), 13 deletions(-)
 create mode 100644 data/attributed_ports.json
 create mode 100644 scripts/build_shipping_demand.py

diff --git a/Snakefile b/Snakefile
index 6b195d12..8285700e 100644
--- a/Snakefile
+++ b/Snakefile
@@ -441,6 +441,18 @@ rule build_population_weighted_energy_totals:
     script: "scripts/build_population_weighted_energy_totals.py"
 
 
+rule build_shipping_demand:
+    input:
+        ports="data/attributed_ports.json",
+        scope=pypsaeur("resources/europe_shape.geojson"),
+        regions=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
+        demand="resources/energy_totals.csv"
+    output: "resources/shipping_demand_s{simpl}_{clusters}.csv"
+    threads: 1
+    resources: mem_mb=2000
+    script: "scripts/build_shipping_demand.py"
+
+
 rule build_transport_demand:
     input:
         clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
@@ -466,6 +478,7 @@ rule prepare_sector_network:
         energy_totals_name='resources/energy_totals.csv',
         eurostat=input_eurostat,
         pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
+        shipping_demand="resources/shipping_demand_s{simpl}_{clusters}.csv",
         transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
         transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
         avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
diff --git a/data/attributed_ports.json b/data/attributed_ports.json
new file mode 100644
index 00000000..12344811
--- /dev/null
+++ b/data/attributed_ports.json
@@ -0,0 +1,861 @@
+{
+"type": "FeatureCollection",
+"features": [
+{ "type": "Feature", "properties": { "Country": "United Arab Emirates", "Function": "1-345---", "LOCODE": "AEAUH", "Name": "Abu Dhabi", "NameWoDiac": "Abu Dhabi", "Status": "AI", "outflows": 41597.142851999997 }, "geometry": { "type": "Point", "coordinates": [ 54.366666666666667, 24.466666666666665 ] } },
+{ "type": "Feature", "properties": { "Country": "United Arab Emirates", "Function": "1-------", "LOCODE": "AERUW", "Name": "Ar Ruways", "NameWoDiac": "Ar Ruways", "Status": "RL", "outflows": 166556.0 }, "geometry": { "type": "Point", "coordinates": [ 52.733333333333334, 24.116666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "United Arab Emirates", "Function": "1-------", "LOCODE": "AEKLF", "Name": "Khor al Fakkan", "NameWoDiac": "Khor al Fakkan", "Status": "RL", "outflows": 790406.5 }, "geometry": { "type": "Point", "coordinates": [ 56.35, 25.333333333333332 ] } },
+{ "type": "Feature", "properties": { "Country": "United Arab Emirates", "Function": "1-3-----", "LOCODE": "AEMKH", "Name": "Mina Khalid", "NameWoDiac": "Mina Khalid", "Status": "RL", "outflows": 646965.0 }, "geometry": { "type": "Point", "coordinates": [ 55.366666666666667, 25.35 ] } },
+{ "type": "Feature", "properties": { "Country": "United Arab Emirates", "Function": "1-------", "LOCODE": "AEKHL", "Name": "Mina Khalifa\/Abu Dhabi", "NameWoDiac": "Mina Khalifa\/Abu Dhabi", "Status": "RL", "outflows": 18341458.820419993 }, "geometry": { "type": "Point", "coordinates": [ 54.666666666666664, 24.833333333333332 ] } },
+{ "type": "Feature", "properties": { "Country": "United Arab Emirates", "Function": "1--4----", "LOCODE": "AEQIW", "Name": "Umm al Qaiwain", "NameWoDiac": "Umm al Qaiwain", "Status": "AI", "outflows": 14196.0 }, "geometry": { "type": "Point", "coordinates": [ 55.55, 25.566666666666666 ] } },
+{ "type": "Feature", "properties": { "Country": "Antigua and Barbuda", "Function": "1-------", "LOCODE": "AGSJO", "Name": "Saint John's", "NameWoDiac": "Saint John's", "Status": "AI", "outflows": 208663.0 }, "geometry": { "type": "Point", "coordinates": [ -61.85, 17.116666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1--4----", "LOCODE": "ARBHI", "Name": "Bah�a Blanca", "NameWoDiac": "Bahia Blanca", "Status": "AI", "outflows": 677327.625 }, "geometry": { "type": "Point", "coordinates": [ -62.283333333333331, -38.716666666666669 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "12345---", "LOCODE": "ARBUE", "Name": "Buenos Aires", "NameWoDiac": "Buenos Aires", "Status": "AI", "outflows": 11083411.036479998 }, "geometry": { "type": "Point", "coordinates": [ -58.666666666666664, -34.583333333333336 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1-345---", "LOCODE": "ARMDQ", "Name": "Mar del Plata", "NameWoDiac": "Mar del Plata", "Status": "AI", "outflows": 24960.0 }, "geometry": { "type": "Point", "coordinates": [ -57.533333333333331, -38.05 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1--4----", "LOCODE": "ARPUD", "Name": "Puerto Deseado", "NameWoDiac": "Puerto Deseado", "Status": "AI", "outflows": 24960.0 }, "geometry": { "type": "Point", "coordinates": [ -65.9, -47.75 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1--4----", "LOCODE": "ARPMY", "Name": "Puerto Madryn", "NameWoDiac": "Puerto Madryn", "Status": "AI", "outflows": 671555.625 }, "geometry": { "type": "Point", "coordinates": [ -65.033333333333331, -42.75 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "12345---", "LOCODE": "ARROS", "Name": "Rosario", "NameWoDiac": "Rosario", "Status": "AI", "outflows": 110227.0 }, "geometry": { "type": "Point", "coordinates": [ -60.65, -32.95 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1-------", "LOCODE": "ARSAE", "Name": "San Antonio Este", "NameWoDiac": "San Antonio Este", "Status": "RQ", "outflows": 23075.0 }, "geometry": { "type": "Point", "coordinates": [ -64.733333333333334, -40.8 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1--4----", "LOCODE": "ARUSH", "Name": "Ushuaia", "NameWoDiac": "Ushuaia", "Status": "AI", "outflows": 30732.0 }, "geometry": { "type": "Point", "coordinates": [ -68.3, -54.8 ] } },
+{ "type": "Feature", "properties": { "Country": "Argentina", "Function": "1-------", "LOCODE": "ARZAE", "Name": "Z�rate", "NameWoDiac": "Zarate", "Status": "AI", "outflows": 164645.0 }, "geometry": { "type": "Point", "coordinates": [ -59.033333333333331, -34.1 ] } },
+{ "type": "Feature", "properties": { "Country": "American Samoa", "Function": "1--45---", "LOCODE": "ASPPG", "Name": "Pago Pago", "NameWoDiac": "Pago Pago", "Status": "AI", "outflows": 338184.5 }, "geometry": { "type": "Point", "coordinates": [ -170.7, 14.266666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "Australia", "Function": "12345---", "LOCODE": "AUADL", "Name": "Adelaide", "NameWoDiac": "Adelaide", "Status": "AC", "outflows": 5338947.2004299983 }, "geometry": { "type": "Point", "coordinates": [ 138.583333333333343, -34.916666666666664 ] } },
+{ "type": "Feature", "properties": { "Country": "Australia", "Function": "12345---", "LOCODE": "AUBNE", "Name": "Brisbane", "NameWoDiac": "Brisbane", "Status": "AC", "outflows": 8402703.6401499975 }, "geometry": { "type": "Point", "coordinates": [ 153.01666666666668, -27.466666666666665 ] } },
+{ "type": "Feature", "properties": { "Country": "Australia", "Function": "12345---", "LOCODE": "AUDRW", "Name": "Darwin", "NameWoDiac": "Darwin", "Status": "AC", "outflows": 88640.416664000004 }, "geometry": { "type": "Point", "coordinates": [ 130.833333333333343, -12.45 ] } },
+{ "type": "Feature", "properties": { "Country": "Australia", "Function": "12345---", "LOCODE": "AUMEL", "Name": "Melbourne", "NameWoDiac": "Melbourne", "Status": "AC", "outflows": 9957826.0957300067 }, "geometry": { "type": "Point", "coordinates": [ 144.966666666666669, -37.81666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "Australia", "Function": "12345---", "LOCODE": "AUSYD", "Name": "Sydney", "NameWoDiac": "Sydney", "Status": "AC", "outflows": 10352110.143530006 }, "geometry": { "type": "Point", "coordinates": [ 151.2, -33.85 ] } },
+{ "type": "Feature", "properties": { "Country": "Aruba", "Function": "1-------", "LOCODE": "AWBAR", "Name": "Barcadera", "NameWoDiac": "Barcadera", "Status": "RL", "outflows": 65431.8 }, "geometry": { "type": "Point", "coordinates": [ -69.983333333333334, 12.483333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "Bangladesh", "Function": "1--45---", "LOCODE": "BDCGP", "Name": "Chattogram", "NameWoDiac": "Chattogram", "Status": "AI", "outflows": 1379549.0523300001 }, "geometry": { "type": "Point", "coordinates": [ 91.833333333333329, 22.333333333333332 ] } },
+{ "type": "Feature", "properties": { "Country": "Belgium", "Function": "12345---", "LOCODE": "BEANR", "Name": "Antwerpen", "NameWoDiac": "Antwerpen", "Status": "AI", "outflows": 51827814.560638025 }, "geometry": { "type": "Point", "coordinates": [ 4.416666666666667, 51.216666666666669 ] } },
+{ "type": "Feature", "properties": { "Country": "Belgium", "Function": "1234----", "LOCODE": "BEGNE", "Name": "Gent (Ghent)", "NameWoDiac": "Gent (Ghent)", "Status": "AI", "outflows": 13260.0 }, "geometry": { "type": "Point", "coordinates": [ 3.716666666666667, 51.05 ] } },
+{ "type": "Feature", "properties": { "Country": "Belgium", "Function": "1-3-----", "LOCODE": "BEZEE", "Name": "Zeebrugge", "NameWoDiac": "Zeebrugge", "Status": "AI", "outflows": 5650583.2502299985 }, "geometry": { "type": "Point", "coordinates": [ 3.2, 51.333333333333336 ] } },
+{ "type": "Feature", "properties": { "Country": "Bahrain", "Function": "1-------", "LOCODE": "BHMIN", "Name": "Mina Sulman Port", "NameWoDiac": "Mina Sulman Port", "Status": "AA", "outflows": 632118.5 }, "geometry": { "type": "Point", "coordinates": [ 50.616666666666667, 26.2 ] } },
+{ "type": "Feature", "properties": { "Country": "Bonaire, Sint Eustatius and Saba", "Function": "1-------", "LOCODE": "BQKRA", "Name": "Kralendijk", "NameWoDiac": "Kralendijk", "Status": "AI", "outflows": 117162.5 }, "geometry": { "type": "Point", "coordinates": [ -68.266666666666666, 12.15 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "-23-----", "LOCODE": "BRIGI", "Name": "Itagua�", "NameWoDiac": "Itaguai", "Status": "RL", "outflows": 2879859.0476199985 }, "geometry": { "type": "Point", "coordinates": [ -43.766666666666666, -22.866666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1-3-----", "LOCODE": "BRIOA", "Name": "Itapo�", "NameWoDiac": "Itapoa", "Status": "RL", "outflows": 9027277.19 }, "geometry": { "type": "Point", "coordinates": [ -48.6, -26.116666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1-34----", "LOCODE": "BRNVT", "Name": "Navegantes", "NameWoDiac": "Navegantes", "Status": "AI", "outflows": 10279036.91334 }, "geometry": { "type": "Point", "coordinates": [ -48.65, -26.9 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1--4----", "LOCODE": "BRPNG", "Name": "Paranagu�", "NameWoDiac": "Paranagua", "Status": "AI", "outflows": 13802196.524050001 }, "geometry": { "type": "Point", "coordinates": [ -48.5, -25.516666666666666 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "12------", "LOCODE": "BRPEC", "Name": "Pec�m Pt\/S�o Gon�alo do Amarante", "NameWoDiac": "Pecem Pt\/Sao Goncalo do Amarante", "Status": "AA", "outflows": 2174063.6046599997 }, "geometry": { "type": "Point", "coordinates": [ -38.866666666666667, -3.533333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "123-567-", "LOCODE": "BRRIO", "Name": "Rio de Janeiro", "NameWoDiac": "Rio de Janeiro", "Status": "AA", "outflows": 9600221.3041699976 }, "geometry": { "type": "Point", "coordinates": [ -43.233333333333334, -22.883333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1-------", "LOCODE": "BRMCP", "Name": "Santana Pt.\/Macap�", "NameWoDiac": "Santana Pt.\/Macapa", "Status": "AA", "outflows": 837504.77784000011 }, "geometry": { "type": "Point", "coordinates": [ -51.166666666666664, -0.05 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1234----", "LOCODE": "BRSSZ", "Name": "Santos", "NameWoDiac": "Santos", "Status": "AI", "outflows": 17682777.003890004 }, "geometry": { "type": "Point", "coordinates": [ -46.333333333333336, -23.95 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1-34----", "LOCODE": "BRSLZ", "Name": "S�o Lu�s", "NameWoDiac": "Sao Luis", "Status": "AI", "outflows": 45240.0 }, "geometry": { "type": "Point", "coordinates": [ -44.3, -2.5 ] } },
+{ "type": "Feature", "properties": { "Country": "Brazil", "Function": "1-------", "LOCODE": "BRVIX", "Name": "Vit�ria Pt", "NameWoDiac": "Vitoria Pt", "Status": "AA", "outflows": 738129.52788000007 }, "geometry": { "type": "Point", "coordinates": [ -40.333333333333336, -20.316666666666666 ] } },
+{ "type": "Feature", "properties": { "Country": "Bahamas", "Function": "1--45---", "LOCODE": "BSNAS", "Name": "Nassau", "NameWoDiac": "Nassau", "Status": "AI", "outflows": 62842.0 }, "geometry": { "type": "Point", "coordinates": [ -77.35, 25.083333333333332 ] } },
+{ "type": "Feature", "properties": { "Country": "Belize", "Function": "1-34----", "LOCODE": "BZBGK", "Name": "Big Creek", "NameWoDiac": "Big Creek", "Status": "RL", "outflows": 199368.0 }, "geometry": { "type": "Point", "coordinates": [ -88.4, 16.516666666666666 ] } },
+{ "type": "Feature", "properties": { "Country": "Canada", "Function": "1-34----", "LOCODE": "CANWP", "Name": "Argentia", "NameWoDiac": "Argentia", "Status": "AI", "outflows": 27248.000001 }, "geometry": { "type": "Point", "coordinates": [ -54.0, 47.3 ] } },
+{ "type": "Feature", "properties": { "Country": "Canada", "Function": "1-34-6--", "LOCODE": "CASJB", "Name": "Saint-John", "NameWoDiac": "Saint-John", "Status": "AS", "outflows": 389420.2 }, "geometry": { "type": "Point", "coordinates": [ -66.066666666666663, 45.266666666666666 ] } },
+{ "type": "Feature", "properties": { "Country": "Canada", "Function": "1-3-----", "LOCODE": "CASJF", "Name": "Saint-John's", "NameWoDiac": "Saint-John's", "Status": "AS", "outflows": 26845.0 }, "geometry": { "type": "Point", "coordinates": [ -52.733333333333334, 47.56666666666667 ] } },
+{ "type": "Feature", "properties": { "Country": "Congo, The Democratic Republic of the", "Function": "1-3-----", "LOCODE": "CDBNW", "Name": "Banana", "NameWoDiac": "Banana", "Status": "RL", "outflows": 48681.0 }, "geometry": { "type": "Point", "coordinates": [ 12.401211892732039, -6.003633266930797 ] } },
+{ "type": "Feature", "properties": { "Country": "Congo", "Function": "1--45---", "LOCODE": "CGPNR", "Name": "Pointe Noire", "NameWoDiac": "Pointe Noire", "Status": "AI", "outflows": 3473713.5811700015 }, "geometry": { "type": "Point", "coordinates": [ 11.85, -4.8 ] } },
+{ "type": "Feature", "properties": { "Country": "C�te d'Ivoire", "Function": "1--45---", "LOCODE": "CIABJ", "Name": "Abidjan", "NameWoDiac": "Abidjan", "Status": "AI", "outflows": 3248845.4334399998 }, "geometry": { "type": "Point", "coordinates": [ -4.016666666666667, 5.333333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "C�te d'Ivoire", "Function": "1-34----", "LOCODE": "CISPY", "Name": "San-P�dro", "NameWoDiac": "San-Pedro", "Status": "AI", "outflows": 2312502.0286400001 }, "geometry": { "type": "Point", "coordinates": [ -6.616666666666667, 4.733333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1--4----", "LOCODE": "CLANF", "Name": "Antofagasta", "NameWoDiac": "Antofagasta", "Status": "AI", "outflows": 2136460.625 }, "geometry": { "type": "Point", "coordinates": [ -70.38333333333334, -23.633333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1-34----", "LOCODE": "CLARI", "Name": "Arica", "NameWoDiac": "Arica", "Status": "AI", "outflows": 1400600.825 }, "geometry": { "type": "Point", "coordinates": [ -70.316666666666663, -18.483333333333334 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "123-----", "LOCODE": "CLCNL", "Name": "Coronel", "NameWoDiac": "Coronel", "Status": "AI", "outflows": 5437390.8332000002 }, "geometry": { "type": "Point", "coordinates": [ -73.15, -37.016666666666666 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1--4----", "LOCODE": "CLIQQ", "Name": "Iquique", "NameWoDiac": "Iquique", "Status": "AI", "outflows": 1915647.5 }, "geometry": { "type": "Point", "coordinates": [ -70.13333333333334, -20.216666666666665 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "123-----", "LOCODE": "CLLQN", "Name": "Lirqu�n", "NameWoDiac": "Lirquen", "Status": "AI", "outflows": 3185838.4995000004 }, "geometry": { "type": "Point", "coordinates": [ -72.983333333333334, -36.7 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1-------", "LOCODE": "CLMJS", "Name": "Mejillones", "NameWoDiac": "Mejillones", "Status": "AI", "outflows": 32362.2 }, "geometry": { "type": "Point", "coordinates": [ -70.45, -23.1 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "123-----", "LOCODE": "CLPAG", "Name": "Puerto Angamos", "NameWoDiac": "Puerto Angamos", "Status": "RL", "outflows": 5408319.3663000017 }, "geometry": { "type": "Point", "coordinates": [ -70.45, -23.083333333333332 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1-34----", "LOCODE": "CLPUQ", "Name": "Punta Arenas", "NameWoDiac": "Punta Arenas", "Status": "AI", "outflows": 18174.0 }, "geometry": { "type": "Point", "coordinates": [ -70.933333333333337, -53.133333333333333 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "123-----", "LOCODE": "CLSAI", "Name": "San Antonio", "NameWoDiac": "San Antonio", "Status": "AI", "outflows": 9417069.025 }, "geometry": { "type": "Point", "coordinates": [ -71.6, -33.6 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1-3-----", "LOCODE": "CLSVE", "Name": "San Vicente", "NameWoDiac": "San Vicente", "Status": "AI", "outflows": 945075.625 }, "geometry": { "type": "Point", "coordinates": [ -73.13333333333334, -36.7 ] } },
+{ "type": "Feature", "properties": { "Country": "Chile", "Function": "1234----", "LOCODE": "CLVAP", "Name": "Valparaiso", "NameWoDiac": "Valparaiso", "Status": "AI", "outflows": 2059925.0 }, "geometry": { "type": "Point", "coordinates": [ -71.63333333333334, -33.033333333333331 ] } },
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+{ "type": "Feature", "properties": { "Country": "Somalia", "Function": "1--4----", "LOCODE": "SOBBO", "Name": "Berbera", "NameWoDiac": "Berbera", "Status": "AI", "outflows": 215094.5 }, "geometry": { "type": "Point", "coordinates": [ 45.01432, 10.43959 ] } },
+{ "type": "Feature", "properties": { "Country": "Somalia", "Function": "1--4----", "LOCODE": "SOKMU", "Name": "Kismayu", "NameWoDiac": "Kismayu", "Status": "AI", "outflows": 188773.0 }, "geometry": { "type": "Point", "coordinates": [ 42.54536, -0.35817 ] } },
+{ "type": "Feature", "properties": { "Country": "Somalia", "Function": "1--45---", "LOCODE": "SOMGQ", "Name": "Mogadishu", "NameWoDiac": "Mogadishu", "Status": "AI", "outflows": 437501.99997999996 }, "geometry": { "type": "Point", "coordinates": [ 45.34375, 2.03711 ] } },
+{ "type": "Feature", "properties": { "Country": "Suriname", "Function": "1--45---", "LOCODE": "SRPBM", "Name": "Paramaribo", "NameWoDiac": "Paramaribo", "Status": "AI", "outflows": 538931.33332199999 }, "geometry": { "type": "Point", "coordinates": [ -55.16682, 5.86638 ] } },
+{ "type": "Feature", "properties": { "Country": "Suriname", "Function": "1--45---", "LOCODE": "SRPBM", "Name": "Paramaribo", "NameWoDiac": "Paramaribo", "Status": "AI", "outflows": 538931.33332199999 }, "geometry": { "type": "Point", "coordinates": [ -55.16682, 5.86638 ] } },
+{ "type": "Feature", "properties": { "Country": "El Salvador", "Function": "1-3-----", "LOCODE": "SVAQJ", "Name": "Acajutla", "NameWoDiac": "Acajutla", "Status": "AI", "outflows": 495947.4 }, "geometry": { "type": "Point", "coordinates": [ -89.8275, 13.59278 ] } },
+{ "type": "Feature", "properties": { "Country": "El Salvador", "Function": "1--45---", "LOCODE": "SVSAL", "Name": "San Salvador", "NameWoDiac": "San Salvador", "Status": "AI", "outflows": 388752.0 }, "geometry": { "type": "Point", "coordinates": [ -89.18718, 13.68935 ] } },
+{ "type": "Feature", "properties": { "Country": "Togo", "Function": "1--45---", "LOCODE": "TGLFW", "Name": "Lome", "NameWoDiac": "Lome", "Status": "AI", "outflows": 6445382.4134400021 }, "geometry": { "type": "Point", "coordinates": [ 1.22154, 6.12874 ] } },
+{ "type": "Feature", "properties": { "Country": "Thailand", "Function": "1-------", "LOCODE": "THSRI", "Name": "Sriracha", "NameWoDiac": "Sriracha", "Status": "QQ", "outflows": 58522.5 }, "geometry": { "type": "Point", "coordinates": [ 100.93111, 13.17372 ] } },
+{ "type": "Feature", "properties": { "Country": "Tunisia", "Function": "1234----", "LOCODE": "TNBIZ", "Name": "Bizerte", "NameWoDiac": "Bizerte", "Status": "QQ", "outflows": 106117.5 }, "geometry": { "type": "Point", "coordinates": [ 9.87391, 37.27442 ] } },
+{ "type": "Feature", "properties": { "Country": "Tunisia", "Function": "1234----", "LOCODE": "TNSFA", "Name": "Sfax", "NameWoDiac": "Sfax", "Status": "AI", "outflows": 72212.33334 }, "geometry": { "type": "Point", "coordinates": [ 10.76028, 34.74056 ] } },
+{ "type": "Feature", "properties": { "Country": "Tunisia", "Function": "1234----", "LOCODE": "TNSUS", "Name": "Sousse", "NameWoDiac": "Sousse", "Status": "QQ", "outflows": 43545.0 }, "geometry": { "type": "Point", "coordinates": [ 10.63699, 35.82539 ] } },
+{ "type": "Feature", "properties": { "Country": "Tunisia", "Function": "12345---", "LOCODE": "TNTUN", "Name": "Tunis", "NameWoDiac": "Tunis", "Status": "AI", "outflows": 59748.0 }, "geometry": { "type": "Point", "coordinates": [ 10.16579, 36.81897 ] } },
+{ "type": "Feature", "properties": { "Country": "Turkey", "Function": "1-------", "LOCODE": "TRALI", "Name": "Aliaga", "NameWoDiac": "Aliaga", "Status": "RL", "outflows": 8740753.932889998 }, "geometry": { "type": "Point", "coordinates": [ 26.97203, 38.79975 ] } },
+{ "type": "Feature", "properties": { "Country": "Turkey", "Function": "1-3-----", "LOCODE": "TRDRC", "Name": "Derince", "NameWoDiac": "Derince", "Status": "QQ", "outflows": 303927.0 }, "geometry": { "type": "Point", "coordinates": [ 29.81472, 40.75694 ] } },
+{ "type": "Feature", "properties": { "Country": "Turkey", "Function": "1-3-----", "LOCODE": "TRHAY", "Name": "Haydarpasa", "NameWoDiac": "Haydarpasa", "Status": "QQ", "outflows": 220592.66669000004 }, "geometry": { "type": "Point", "coordinates": [ 29.02459, 40.99596 ] } },
+{ "type": "Feature", "properties": { "Country": "Turkey", "Function": "1-------", "LOCODE": "TRYAR", "Name": "Yarimca", "NameWoDiac": "Yarimca", "Status": "QQ", "outflows": 895813.99998000008 }, "geometry": { "type": "Point", "coordinates": [ 31.14194, 39.08361 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--45---", "LOCODE": "USANC", "Name": "Anchorage", "NameWoDiac": "Anchorage", "Status": "AI", "outflows": 61671.99999 }, "geometry": { "type": "Point", "coordinates": [ -149.90028, 61.21806 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USBPT", "Name": "Beaumont", "NameWoDiac": "Beaumont", "Status": "AI", "outflows": 34041.0 }, "geometry": { "type": "Point", "coordinates": [ -94.10185, 30.08605 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--45---", "LOCODE": "USBOS", "Name": "Boston", "NameWoDiac": "Boston", "Status": "AI", "outflows": 1565893.3335999998 }, "geometry": { "type": "Point", "coordinates": [ -71.05977, 42.35843 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "--3-----", "LOCODE": "USBCK", "Name": "Brunswick", "NameWoDiac": "Brunswick", "Status": "RL", "outflows": 48681.0 }, "geometry": { "type": "Point", "coordinates": [ -74.45182, 40.48622 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "--3-----", "LOCODE": "USCAT", "Name": "Camden", "NameWoDiac": "Camden", "Status": "RQ", "outflows": 176498.0 }, "geometry": { "type": "Point", "coordinates": [ -75.11962, 39.92595 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USCHS", "Name": "Charleston", "NameWoDiac": "Charleston", "Status": "AI", "outflows": 23192728.69687001 }, "geometry": { "type": "Point", "coordinates": [ -79.924426675273111, 32.785017342562952 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "--3-----", "LOCODE": "USCAV", "Name": "Cleveland", "NameWoDiac": "Cleveland", "Status": "RQ", "outflows": 1974.0 }, "geometry": { "type": "Point", "coordinates": [ -81.69541, 41.4995 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USCRP", "Name": "Corpus Christi", "NameWoDiac": "Corpus Christi", "Status": "AI", "outflows": 53712.0 }, "geometry": { "type": "Point", "coordinates": [ -97.39638, 27.80058 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USDUT", "Name": "Dutch Harbor", "NameWoDiac": "Dutch Harbor", "Status": "AI", "outflows": 378795.99998999998 }, "geometry": { "type": "Point", "coordinates": [ -166.5422, 53.8898 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1-34----", "LOCODE": "USPAE", "Name": "Everett", "NameWoDiac": "Everett", "Status": "AI", "outflows": 152295.00003000002 }, "geometry": { "type": "Point", "coordinates": [ -122.20208, 47.97898 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "---4----", "LOCODE": "USFEP", "Name": "Freeport", "NameWoDiac": "Freeport", "Status": "AI", "outflows": 751787.11118999997 }, "geometry": { "type": "Point", "coordinates": [ -70.10311, 43.85702 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USGLS", "Name": "Galveston", "NameWoDiac": "Galveston", "Status": "AI", "outflows": 47326.5 }, "geometry": { "type": "Point", "coordinates": [ -94.7977, 29.30135 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1-3-----", "LOCODE": "USGLC", "Name": "Gloucester City", "NameWoDiac": "Gloucester City", "Status": "RN", "outflows": 59686.5 }, "geometry": { "type": "Point", "coordinates": [ -70.66313, 42.61405 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "---4----", "LOCODE": "USIJX", "Name": "Jacksonville", "NameWoDiac": "Jacksonville", "Status": "AI", "outflows": 5087986.3044199999 }, "geometry": { "type": "Point", "coordinates": [ -81.65565, 30.33218 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "---4----", "LOCODE": "USADQ", "Name": "Kodiak", "NameWoDiac": "Kodiak", "Status": "AI", "outflows": 61671.99999 }, "geometry": { "type": "Point", "coordinates": [ -152.40533, 57.78852 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--45---", "LOCODE": "USLAX", "Name": "Los Angeles", "NameWoDiac": "Los Angeles", "Status": "AI", "outflows": 12755714.048839999 }, "geometry": { "type": "Point", "coordinates": [ -118.24368, 34.05223 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--45---", "LOCODE": "USMIA", "Name": "Miami", "NameWoDiac": "Miami", "Status": "AI", "outflows": 6651073.40288 }, "geometry": { "type": "Point", "coordinates": [ -80.19366, 25.77427 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USMOB", "Name": "Mobile", "NameWoDiac": "Mobile", "Status": "AI", "outflows": 3378854.4003 }, "geometry": { "type": "Point", "coordinates": [ -88.04305, 30.69436 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1-3-----", "LOCODE": "USMRH", "Name": "Morehead City", "NameWoDiac": "Morehead City", "Status": "RN", "outflows": 44898.75 }, "geometry": { "type": "Point", "coordinates": [ -76.72604, 34.72294 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "12345---", "LOCODE": "USMSY", "Name": "New Orleans", "NameWoDiac": "New Orleans", "Status": "AI", "outflows": 8818359.6138159968 }, "geometry": { "type": "Point", "coordinates": [ -90.07507, 29.95465 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USPFN", "Name": "Panama City", "NameWoDiac": "Panama City", "Status": "AI", "outflows": 82722.0 }, "geometry": { "type": "Point", "coordinates": [ -85.65983, 30.15946 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USPWM", "Name": "Portland", "NameWoDiac": "Portland", "Status": "AI", "outflows": 27248.000001 }, "geometry": { "type": "Point", "coordinates": [ -122.67621, 45.52345 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USPDX", "Name": "Portland", "NameWoDiac": "Portland", "Status": "AI", "outflows": 336570.0 }, "geometry": { "type": "Point", "coordinates": [ -122.67621, 45.52345 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USSAV", "Name": "Savannah", "NameWoDiac": "Savannah", "Status": "AI", "outflows": 26558703.755599998 }, "geometry": { "type": "Point", "coordinates": [ -81.09983, 32.08354 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--45---", "LOCODE": "USSEA", "Name": "Seattle", "NameWoDiac": "Seattle", "Status": "AI", "outflows": 10283805.920580002 }, "geometry": { "type": "Point", "coordinates": [ -122.33207, 47.60621 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USTIW", "Name": "Tacoma", "NameWoDiac": "Tacoma", "Status": "AI", "outflows": 4139226.6189899999 }, "geometry": { "type": "Point", "coordinates": [ -122.44429, 47.25288 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--45---", "LOCODE": "USTPA", "Name": "Tampa", "NameWoDiac": "Tampa", "Status": "AI", "outflows": 1911998.4003 }, "geometry": { "type": "Point", "coordinates": [ -82.45843, 27.94752 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1-3-----", "LOCODE": "USVAN", "Name": "Vancouver", "NameWoDiac": "Vancouver", "Status": "RN", "outflows": 65700.0 }, "geometry": { "type": "Point", "coordinates": [ -122.66149, 45.63873 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USPBI", "Name": "West Palm Beach", "NameWoDiac": "West Palm Beach", "Status": "AI", "outflows": 222144.0 }, "geometry": { "type": "Point", "coordinates": [ -80.05337, 26.71534 ] } },
+{ "type": "Feature", "properties": { "Country": "United States", "Function": "1--4----", "LOCODE": "USILG", "Name": "Wilmington", "NameWoDiac": "Wilmington", "Status": "AI", "outflows": 290589.0 }, "geometry": { "type": "Point", "coordinates": [ -75.54659, 39.74595 ] } },
+{ "type": "Feature", "properties": { "Country": "Uruguay", "Function": "1--45---", "LOCODE": "UYMVD", "Name": "Montevideo", "NameWoDiac": "Montevideo", "Status": "AF", "outflows": 11543641.215 }, "geometry": { "type": "Point", "coordinates": [ -56.18816, -34.90328 ] } },
+{ "type": "Feature", "properties": { "Country": "Virgin Islands, U.S.", "Function": "1-------", "LOCODE": "VICHA", "Name": "Charlotte Amalie, Saint Thomas", "NameWoDiac": "Charlotte Amalie, Saint Thomas", "Status": "AI", "outflows": 307918.0 }, "geometry": { "type": "Point", "coordinates": [ -64.9307, 18.3419 ] } },
+{ "type": "Feature", "properties": { "Country": "Viet Nam", "Function": "1-------", "LOCODE": "VNHPH", "Name": "Haiphong", "NameWoDiac": "Haiphong", "Status": "AI", "outflows": 10072807.932540001 }, "geometry": { "type": "Point", "coordinates": [ 106.68345, 20.86481 ] } },
+{ "type": "Feature", "properties": { "Country": "Vanuatu", "Function": "1--45---", "LOCODE": "VUVLI", "Name": "Port Vila", "NameWoDiac": "Port Vila", "Status": "AI", "outflows": 453739.0 }, "geometry": { "type": "Point", "coordinates": [ 168.31366, -17.73648 ] } },
+{ "type": "Feature", "properties": { "Country": "Vanuatu", "Function": "1-------", "LOCODE": "VUSAN", "Name": "Santo", "NameWoDiac": "Santo", "Status": "RQ", "outflows": 206498.5 }, "geometry": { "type": "Point", "coordinates": [ 167.16235, -15.51989 ] } },
+{ "type": "Feature", "properties": { "Country": "Samoa", "Function": "1--45---", "LOCODE": "WSAPW", "Name": "Apia", "NameWoDiac": "Apia", "Status": "AI", "outflows": 339021.5 }, "geometry": { "type": "Point", "coordinates": [ -171.76666, -13.83333 ] } },
+{ "type": "Feature", "properties": { "Country": "Yemen", "Function": "1--45---", "LOCODE": "YEADE", "Name": "Aden", "NameWoDiac": "Aden", "Status": "AI", "outflows": 126082.5 }, "geometry": { "type": "Point", "coordinates": [ 45.03667, 12.77944 ] } },
+{ "type": "Feature", "properties": { "Country": "Yemen", "Function": "1--4----", "LOCODE": "YEMKX", "Name": "Mukalla", "NameWoDiac": "Mukalla", "Status": "AI", "outflows": 30745.0 }, "geometry": { "type": "Point", "coordinates": [ 49.12424, 14.54248 ] } },
+{ "type": "Feature", "properties": { "Country": "South Africa", "Function": "1234----", "LOCODE": "ZAELS", "Name": "East London", "NameWoDiac": "East London", "Status": "AF", "outflows": 15600.0 }, "geometry": { "type": "Point", "coordinates": [ 27.91162, -33.01529 ] } },
+{ "type": "Feature", "properties": { "Country": "South Africa", "Function": "1--45---", "LOCODE": "ZAPLZ", "Name": "Port Elizabeth", "NameWoDiac": "Port Elizabeth", "Status": "AF", "outflows": 2557154.4621100002 }, "geometry": { "type": "Point", "coordinates": [ 25.61494, -33.96109 ] } },
+{ "type": "Feature", "properties": { "Country": "South Africa", "Function": "1--4----", "LOCODE": "ZARCB", "Name": "Richards Bay", "NameWoDiac": "Richards Bay", "Status": "AF", "outflows": 164538.86664000002 }, "geometry": { "type": "Point", "coordinates": [ 32.03768, -28.78301 ] } }
+]
+}
diff --git a/scripts/build_shipping_demand.py b/scripts/build_shipping_demand.py
new file mode 100644
index 00000000..18335c9f
--- /dev/null
+++ b/scripts/build_shipping_demand.py
@@ -0,0 +1,48 @@
+"""Build regional demand for international navigation based on outflow volume of ports."""
+
+import pandas as pd
+import geopandas as gpd
+import json
+
+if __name__ == '__main__':
+    if 'snakemake' not in globals():
+        from helper import mock_snakemake
+        snakemake = mock_snakemake(
+            'build_shipping_demand_per_node',
+            simpl='',
+            clusters=48,
+        )
+
+    scope = gpd.read_file(snakemake.input.scope).geometry[0]
+    regions = gpd.read_file(snakemake.input.regions).set_index('name')
+    demand = pd.read_csv(snakemake.input.demand, index_col=0)["total international navigation"]
+
+    # read port data into GeoDataFrame
+    with open(snakemake.input.ports, 'r', encoding='latin_1') as f:
+      ports = json.load(f)
+    ports = pd.json_normalize(ports, "features", sep="_")
+    coordinates = ports.geometry_coordinates
+    geometry = gpd.points_from_xy(coordinates.str[0], coordinates.str[1])
+    ports = gpd.GeoDataFrame(ports, geometry=geometry, crs=4326)
+    
+    # filter global port data by European ports
+    european_ports = ports[ports.within(scope)]
+    
+    # assign ports to nearest region
+    p = european_ports.to_crs(3857)
+    r = regions.to_crs(3857)
+    outflows = p.sjoin_nearest(r).groupby("index_right").properties_outflows.sum().div(1e3)
+    
+    # calculate fraction of each country's port outflows
+    countries = outflows.index.str[:2]
+    outflows_per_country = outflows.groupby(countries).sum()
+    fraction = outflows / countries.map(outflows_per_country)
+    
+    # distribute per-country demands to nodes based on these fractions
+    nodal_demand = demand.loc[countries].fillna(0.)
+    nodal_demand.index = fraction.index
+    nodal_demand = nodal_demand.multiply(fraction, axis=0)
+    nodal_demand = nodal_demand.reindex(regions.index, fill_value=0)
+
+    # export nodal international navigation demands
+    nodal_demand.to_csv(snakemake.output[0])
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 244f615b..2d8189f2 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2154,24 +2154,29 @@ def add_industry(n, costs):
     else:
         shipping_bus = nodes + " H2"
 
-    all_navigation = ["total international navigation", "total domestic navigation"]
-    efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
-    shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
-    p_set = shipping_hydrogen_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
+    domestic_navigation = pop_weighted_energy_totals.loc[nodes, "total domestic navigation"]
+    international_navigation = pd.read_csv(snakemake.input.shipping_demand, index_col=0)
+    all_navigation = domestic_navigation + international_navigation
 
-    n.madd("Load",
-        nodes,
-        suffix=" H2 for shipping",
-        bus=shipping_bus,
-        carrier="H2 for shipping",
-        p_set=p_set
-    )
+    if shipping_hydrogen_share > 0:
+
+        efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
+        shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
+        p_set = shipping_hydrogen_share * all_navigation * 1e6 * efficiency / 8760
+
+        n.madd("Load",
+            nodes,
+            suffix=" H2 for shipping",
+            bus=shipping_bus,
+            carrier="H2 for shipping",
+            p_set=p_set
+        )
 
     if shipping_hydrogen_share < 1:
 
         shipping_oil_share = 1 - shipping_hydrogen_share
 
-        p_set = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
+        p_set = shipping_oil_share * all_navigation * 1e6 / 8760.
 
         n.madd("Load",
             nodes,
@@ -2181,7 +2186,7 @@ def add_industry(n, costs):
             p_set=p_set
         )
 
-        co2 = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
+        co2 = shipping_oil_share * all_navigation.sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
 
         n.add("Load",
             "shipping oil emissions",

From 16a92477f0ffd6b31bba7d6bf1cd96e045ccfef3 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 13 Nov 2022 18:25:32 +0100
Subject: [PATCH 170/243] methanol in shipping

---
 config.default.yaml               |  4 ++++
 scripts/prepare_sector_network.py | 37 ++++++++++++++++++++++++++++++-
 2 files changed, 40 insertions(+), 1 deletion(-)

diff --git a/config.default.yaml b/config.default.yaml
index 4cf7f5fa..1cd4c271 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -193,6 +193,10 @@ sector:
   agriculture_machinery_electric_share: 0
   agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
   agriculture_machinery_electric_efficiency: 0.3 # electricity per use
+  shipping_methanol: true
+  MWh_MeOH_per_MWh_H2: 0.8787 # https://github.com/euronion/trace/blob/main/data/efficiencies.csv
+  MWh_MeOH_per_tCO2: 4.0321 # https://github.com/euronion/trace/blob/main/data/efficiencies.csv
+  MWh_MeOH_per_MWh_e: 3.6907 # https://github.com/euronion/trace/blob/main/data/efficiencies.csv
   shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
   shipping_hydrogen_share:  # 1 means all hydrogen FC
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 244f615b..3a2a7de9 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -106,6 +106,16 @@ def define_spatial(nodes, options):
 
         spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
 
+    # hydrogen
+    spatial.h2 = SimpleNamespace()
+    spatial.h2.nodes = nodes + " H2"
+    spatial.h2.locations = nodes
+
+    # methanol
+    spatial.methanol = SimpleNamespace()
+    spatial.methanol.nodes = ["EU methanol"]
+    spatial.methanol.locations = ["EU"]
+
     # oil
     spatial.oil = SimpleNamespace()
     spatial.oil.nodes = ["EU oil"]
@@ -2154,6 +2164,31 @@ def add_industry(n, costs):
     else:
         shipping_bus = nodes + " H2"
 
+    if options.get("shipping_methanol"):
+
+        n.madd("Bus",
+            spatial.methanol.nodes,
+            carrier="methanol",
+            location=spatial.methanol.locations,
+            unit="MWh_LHV"
+        )
+
+        # methanolisation
+        n.madd(
+            spatial.h2.locations + "methanolisation",
+            bus0=spatial.h2.nodes,
+            bus1=spatial.methanol.nodes,
+            bus2=nodes,
+            bus3=spatial.co2.nodes,
+            carrier="methanolisation",
+            p_nom_extendable=True,
+            capital_cost=costs.at["methanolisation", 'fixed'] * 0.8787, # EUR/MW_H2/a
+            lifetime=costs.at["methanolisation", 'lifetime'],
+            efficiency=options["MWh_MeOH_per_MWh_H2"],
+            efficiency2=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_tCO2"],
+            efficiency3=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_MWh_e"],
+        )
+
     all_navigation = ["total international navigation", "total domestic navigation"]
     efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
     shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
@@ -2245,7 +2280,7 @@ def add_industry(n, costs):
         bus2=spatial.co2.nodes,
         carrier="Fischer-Tropsch",
         efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
-        capital_cost=costs.at["Fischer-Tropsch", 'fixed'],
+        capital_cost=costs.at["Fischer-Tropsch", 'fixed'] * costs.at["Fischer-Tropsch", 'efficiency'], # EUR/MW_H2/a
         efficiency2=-costs.at["oil", 'CO2 intensity'] * costs.at["Fischer-Tropsch", 'efficiency'],
         p_nom_extendable=True,
         lifetime=costs.at['Fischer-Tropsch', 'lifetime']

From ae8f601f0a1ed492da67a9986cd35e702a5e35d4 Mon Sep 17 00:00:00 2001
From: euronion <42553970+euronion@users.noreply.github.com>
Date: Mon, 14 Nov 2022 09:38:09 +0100
Subject: [PATCH 171/243] Fix relative imports from subworkflow PyPSA-EUR

---
 Snakefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Snakefile b/Snakefile
index 6b195d12..0b95d30d 100644
--- a/Snakefile
+++ b/Snakefile
@@ -256,9 +256,9 @@ rule build_biomass_potentials:
         enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
         nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
         regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
-        nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
-        swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
-        swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
+        nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
+        swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
+        swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
         country_shapes=pypsaeur('resources/country_shapes.geojson')
     output:
         biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',

From e1e18a50e75fa7b88638b7fdcf595a1b103ce456 Mon Sep 17 00:00:00 2001
From: Fabian Hofmann <hofmann@fias.uni-frankfurt.de>
Date: Wed, 16 Nov 2022 12:18:58 +0100
Subject: [PATCH 172/243] Fix input listing in Snakefile

---
 Snakefile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Snakefile b/Snakefile
index 5ecba666..5dc26dd8 100644
--- a/Snakefile
+++ b/Snakefile
@@ -598,8 +598,8 @@ if config["foresight"] == "overnight":
             overrides="data/override_component_attrs",
             network=RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
             costs=CDIR + "costs_{}.csv".format(config['costs']['year']),
-            config=SDIR + '/configs/config.yaml'
-            env=SDIR + '/configs/environment.yaml'
+            config=SDIR + '/configs/config.yaml',
+            env=SDIR + '/configs/environment.yaml',
         output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
         shadow: "shallow"
         log:

From e25fb02e9cff7249ae2f058e5686b815c0a0b3f5 Mon Sep 17 00:00:00 2001
From: Fabian Hofmann <hofmann@fias.uni-frankfurt.de>
Date: Wed, 16 Nov 2022 12:44:28 +0100
Subject: [PATCH 173/243] CI: make coincbc and ipopt installed by conda

---
 .github/workflows/ci.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
index f528c28d..da984fd3 100644
--- a/.github/workflows/ci.yaml
+++ b/.github/workflows/ci.yaml
@@ -64,7 +64,7 @@ jobs:
 
       - name: Add solver to environment
         run: |
-          echo -e "  - coincbc\n  - ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
+          echo -e "- coincbc\n- ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
 
       - name: Setup Mambaforge
         uses: conda-incubator/setup-miniconda@v2

From df4eb85a19078e32709d66f9b868222bdebc925c Mon Sep 17 00:00:00 2001
From: Fabian <fab.hof@gmx.de>
Date: Fri, 18 Nov 2022 09:08:07 +0100
Subject: [PATCH 174/243] fix typos

---
 doc/data.csv                            |  4 ++--
 doc/installation.rst                    |  2 +-
 doc/release_notes.rst                   |  2 +-
 doc/spatial_resolution.rst              |  2 +-
 scripts/add_existing_baseyear.py        |  6 +++---
 scripts/build_industry_sector_ratios.py |  4 ++--
 scripts/build_retro_cost.py             | 16 ++++++++--------
 scripts/helper.py                       |  2 +-
 scripts/plot_summary.py                 |  4 ++--
 scripts/prepare_sector_network.py       |  6 +++---
 10 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/doc/data.csv b/doc/data.csv
index 2c6ac8c8..49fbc536 100644
--- a/doc/data.csv
+++ b/doc/data.csv
@@ -23,7 +23,7 @@ Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown
 Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
 Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
 Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
-Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
+Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unknown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
 District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
-District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,,
+District heating missing countries,district_heat_share.csv,unknown,https://www.euroheat.org/knowledge-hub/country-profiles,,
 
diff --git a/doc/installation.rst b/doc/installation.rst
index 6637cdfd..f5cb7c7a 100644
--- a/doc/installation.rst
+++ b/doc/installation.rst
@@ -54,7 +54,7 @@ The requirements are the same as `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>
 xarray version >= 0.15.1, you will need the latest master branch of
 atlite version 0.0.2.
 
-You can create an enviroment using the environment.yaml file in pypsa-eur/envs:
+You can create an environment using the environment.yaml file in pypsa-eur/envs:
 
 .. code:: bash
 
diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 51c533f4..51e9b43d 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -28,7 +28,7 @@ incorporates retrofitting options to hydrogen.
 
 * New rule ``cluster_gas_network`` that clusters the gas transmission network
   data to the model resolution. Cross-regional pipeline capacities are aggregated
-  (while pressure and diameter compability is ignored), intra-regional pipelines
+  (while pressure and diameter compatibility is ignored), intra-regional pipelines
   are dropped. Lengths are recalculated based on the regions' centroids.
 
 * With the option ``sector: gas_network:``, the existing gas network is
diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index d410065a..ff11cc39 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -15,7 +15,7 @@ The total number of nodes for Europe is set in the ``config.yaml`` file
 under ``clusters``. The number of nodes can vary between 37, the number
 of independent countries / synchronous areas, and several
 hundred. With 200-300 nodes the model needs 100-150 GB RAM to solve
-with a commerical solver like Gurobi.
+with a commercial solver like Gurobi.
 
 
 Not all of the sectors are at the full nodal resolution, and some
diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index 0c60288a..e5b7e1d1 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -165,11 +165,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
     df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
 
 
-    # drop assets which are already phased out / decomissioned
+    # drop assets which are already phased out / decommissioned
     phased_out = df_agg[df_agg["DateOut"]<baseyear].index
     df_agg.drop(phased_out, inplace=True)
 
-    # calculate remaining lifetime before phase-out (+1 because assumming
+    # calculate remaining lifetime before phase-out (+1 because assuming
     # phase out date at the end of the year)
     df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
 
@@ -251,7 +251,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
                     # existing capacities are split evenly among regions in every country
                     inv_ind = [i for i in inv_busmap[ind]]
 
-                    # for offshore the spliting only inludes coastal regions
+                    # for offshore the splitting only includes coastal regions
                     inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
 
                     p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
diff --git a/scripts/build_industry_sector_ratios.py b/scripts/build_industry_sector_ratios.py
index d1dbe9d8..d2a9ca48 100644
--- a/scripts/build_industry_sector_ratios.py
+++ b/scripts/build_industry_sector_ratios.py
@@ -618,7 +618,7 @@ def nonmetalic_mineral_products():
     # (c) clinker production (kilns),
     # (d) Grinding, packaging.
     # (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
-    # Temperatures above 1400C are required for procesing limestone and sand into clinker.
+    # Temperatures above 1400C are required for processing limestone and sand into clinker.
     # Everything (except current electricity and heat consumption and existing biomass)
     # is transformed into methane for high T.
 
@@ -1110,7 +1110,7 @@ def non_ferrous_metals():
 
     # Aluminium secondary route
 
-    # All is coverted into secondary route fully electrified.
+    # All is converted into secondary route fully electrified.
 
     sector = "Aluminium - secondary production"
 
diff --git a/scripts/build_retro_cost.py b/scripts/build_retro_cost.py
index bec71a53..129c4156 100644
--- a/scripts/build_retro_cost.py
+++ b/scripts/build_retro_cost.py
@@ -33,7 +33,7 @@ The basic equations:
 
         E_space = H_losses - H_gains
 
-    Heat losses constitute from the losses through heat trasmission (H_tr [W/m²K])
+    Heat losses constitute from the losses through heat transmission (H_tr [W/m²K])
     (this includes heat transfer through building elements and thermal bridges)
     and losses by ventilation (H_ve [W/m²K]):
 
@@ -71,7 +71,7 @@ import xarray as xr
 
 # thermal conductivity standard value
 k = 0.035
-# strenght of relative retrofitting depending on the component
+# strength of relative retrofitting depending on the component
 # determined by historical data of insulation thickness for retrofitting
 l_weight = pd.DataFrame({"weight": [1.95, 1.48, 1.]},
                         index=["Roof", "Wall", "Floor"])
@@ -89,8 +89,8 @@ tau_H_0 = 30
 # constant parameter alpha_H_0 [-] according to EN 13790 seasonal method
 alpha_H_0 = 0.8
 
-# paramter for solar heat load during heating season -------------------------
-# tabular standard values table p.8 in documenation
+# parameter for solar heat load during heating season -------------------------
+# tabular standard values table p.8 in documentation
 external_shading = 0.6     # vertical orientation: fraction of window area shaded [-]
 frame_area_fraction = 0.3  # fraction of frame area of window [-]
 non_perpendicular = 0.9    # reduction factor, considering radiation non perpendicular to the glazing[-]
@@ -279,7 +279,7 @@ def prepare_building_stock_data():
 def prepare_building_topology(u_values, same_building_topology=True):
     """
     reads in typical building topologies (e.g. average surface of building elements)
-    and typical losses trough thermal bridging and air ventilation
+    and typical losses through thermal bridging and air ventilation
     """
 
     data_tabula = pd.read_csv(snakemake.input.data_tabula,
@@ -585,7 +585,7 @@ def map_to_lstrength(l_strength, df):
 
 def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor):
     """
-    calculates total annual heat losses Q_ht for different insulation thiknesses
+    calculates total annual heat losses Q_ht for different insulation thicknesses
     (l_strength), depening on current insulation state (u_values), standard
     building topologies and air ventilation from TABULA (data_tabula) and
     the accumulated difference between internal and external temperature
@@ -790,7 +790,7 @@ def sample_dE_costs_area(area, area_tot, costs, dE_space, countries,
 
     # drop not considered countries
     cost_dE = cost_dE.reindex(countries,level=0)
-    # get share of residential and sevice floor area
+    # get share of residential and service floor area
     sec_w = area_tot.value / area_tot.value.groupby(level=0).sum()
     # get the total cost-energy-savings weight by sector area
     tot = (cost_dE.mul(sec_w, axis=0).groupby(level="country_code").sum()
@@ -863,7 +863,7 @@ if __name__ == "__main__":
     data_tabula = prepare_building_topology(u_values)
     # costs for retrofitting -------------------------------------------------
     cost_retro, window_assumptions, cost_w, tax_w = prepare_cost_retro(country_iso_dic)
-    # temperature dependend parameters
+    # temperature dependent parameters
     d_heat, temperature_factor = prepare_temperature_data()
 
 
diff --git a/scripts/helper.py b/scripts/helper.py
index 48e28e71..e6ddfd4a 100644
--- a/scripts/helper.py
+++ b/scripts/helper.py
@@ -25,7 +25,7 @@ def override_component_attrs(directory):
 
     Returns
     -------
-    Dictionary of overriden component attributes.
+    Dictionary of overridden component attributes.
     """
 
     attrs = Dict({k : v.copy() for k,v in component_attrs.items()})
diff --git a/scripts/plot_summary.py b/scripts/plot_summary.py
index b36c2ec5..be08a3af 100644
--- a/scripts/plot_summary.py
+++ b/scripts/plot_summary.py
@@ -401,7 +401,7 @@ def plot_carbon_budget_distribution(input_eurostat):
 
     ax1.plot(emissions, color='black', linewidth=3, label=None)
 
-    #plot commited and uder-discussion targets
+    #plot committed and uder-discussion targets
     #(notice that historical emissions include all countries in the
     # network, but targets refer to EU)
     ax1.plot([2020],[0.8*emissions[1990]],
@@ -427,7 +427,7 @@ def plot_carbon_budget_distribution(input_eurostat):
 
     ax1.plot([2050],[0.125*emissions[1990]],'ro',
                      marker='*', markersize=12, markerfacecolor='black',
-                     markeredgecolor='black', label='EU commited target')
+                     markeredgecolor='black', label='EU committed target')
 
     ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
                        facecolor='white', frameon=True)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 244f615b..6c153f16 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1382,7 +1382,7 @@ def add_land_transport(n, costs):
 
 def build_heat_demand(n):
 
-    # copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
+    # copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile
     daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
 
     intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
@@ -1724,7 +1724,7 @@ def add_heat(n, costs):
 
             # minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
             dE = retro_data.loc[(ct, sec), ("dE")]
-            # get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
+            # get additional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
             dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
             # convert costs Euro/m^2 -> Euro/MWh
             capital_cost =  retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
@@ -2565,7 +2565,7 @@ def set_temporal_aggregation(n, opts, solver_name):
         if m is not None:
             n = average_every_nhours(n, m.group(0))
             break
-        # representive snapshots
+        # representative snapshots
         m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
         if m is not None:
             sn = int(m[1])

From 6b5b8b2d3ba7c91f2c6b88a20201b6f04788d572 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:09:36 +0100
Subject: [PATCH 175/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index dd2802b4..3bca1ea9 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -52,7 +52,7 @@ Grouping years indicates the bins limits for grouping the existing capacities of
 
 **threshold capacity**
 
-If for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored
+If for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored.
 
 ``threshold_capacity: 10``
 

From b20130eb2f6eeec3f683ea12235528c584f6ebd2 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:09:51 +0100
Subject: [PATCH 176/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index 3bca1ea9..ef36f025 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -61,7 +61,7 @@ If for a technology, node, and grouping bin, the capacity is lower than threshol
 
 **conventional carriers**
 
-conventional carriers indicate carriers used in the existing conventional technologies
+Conventional carriers indicate carriers used in the existing conventional technologies.
 
     conventional_carriers:
     

From 24ea8448dac92334a024f0802bf1ef43f4adb724 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:10:11 +0100
Subject: [PATCH 177/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index ef36f025..d207e8af 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -79,7 +79,7 @@ Conventional carriers indicate carriers used in the existing conventional techno
 Options
 =============
 The total carbon budget for the entire transition path can be indicated in the `sector_opts <https://github.com/PyPSA/pypsa-eur-sec/blob/f13902510010b734c510c38c4cae99356f683058/config.default.yaml#L25>`_ in ``config.yaml``. The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay. 
-E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero
+E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero.
 They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
 The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed hroughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
 

From cfcf5cf4900baeda664ba1430950263489911d4c Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:10:31 +0100
Subject: [PATCH 178/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index d207e8af..40326516 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -81,7 +81,7 @@ Options
 The total carbon budget for the entire transition path can be indicated in the `sector_opts <https://github.com/PyPSA/pypsa-eur-sec/blob/f13902510010b734c510c38c4cae99356f683058/config.default.yaml#L25>`_ in ``config.yaml``. The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay. 
 E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero.
 They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
-The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed hroughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
+The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed throughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
 
 $e(t) = e_0 (1+ (r+m)t) e^(-mt)$
 

From bf374c75be6c6bb9bc5ae8c48abac6d86b3c2f54 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:10:48 +0100
Subject: [PATCH 179/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 24efcb12..3df4dc28 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -32,7 +32,7 @@ Here are some examples of how spatial resolution is set for different sectors in
 •	Industry demand (heat, chemicals, etc.) : Modeled as nodal, distributed in each country based on locations of industry from HotMaps database.
 •	Hydrogen network: Modeled as nodal (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L260>`_ file).
 
-•	Methane network: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_ . One node can be considered reasonable since future demand is expected to be low and no bottlenecks are expected. Also, the nodally resolved methane grid is based on SciGRID_gas data.
+•	Methane network: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_. One node can be considered reasonable since future demand is expected to be low and no bottlenecks are expected. Also, the nodally resolved methane grid is based on SciGRID_gas data.
 
 •	Solid biomass: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. Nodal modeling includes modeling biomass potential per country (given per country, then distributed by population density within) and the transport of solid biomass between countries.
 

From ab71254aeb3fb5afc33de7cda419853251bc6c5f Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:11:03 +0100
Subject: [PATCH 180/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 3df4dc28..9b0f9f4c 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -34,7 +34,7 @@ Here are some examples of how spatial resolution is set for different sectors in
 
 •	Methane network: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_. One node can be considered reasonable since future demand is expected to be low and no bottlenecks are expected. Also, the nodally resolved methane grid is based on SciGRID_gas data.
 
-•	Solid biomass: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. Nodal modeling includes modeling biomass potential per country (given per country, then distributed by population density within) and the transport of solid biomass between countries.
+•	Solid biomass: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. Nodal modeling includes modeling biomass potential per country (given per country, then distributed by population density within) and the transport of solid biomass between countries.
 
 •	CO2: It can be modeled as a single node for Europe or it can be nodally resolved with CO2 transport pipelines if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_ . It should mentioned that in single node mode a transport and storage cost is added for sequestered CO2, the cost of which can be adjusted in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_ file. 
 

From c935ed151bf53dff3023669bf846cbfcce51d886 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:11:20 +0100
Subject: [PATCH 181/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 9b0f9f4c..726038c9 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -36,7 +36,7 @@ Here are some examples of how spatial resolution is set for different sectors in
 
 •	Solid biomass: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. Nodal modeling includes modeling biomass potential per country (given per country, then distributed by population density within) and the transport of solid biomass between countries.
 
-•	CO2: It can be modeled as a single node for Europe or it can be nodally resolved with CO2 transport pipelines if activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_ . It should mentioned that in single node mode a transport and storage cost is added for sequestered CO2, the cost of which can be adjusted in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_ file. 
+•	CO2: It can be modeled as a single node for Europe or it can be nodally resolved with CO2 transport pipelines if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. It should mentioned that in single node mode a transport and storage cost is added for sequestered CO2, the cost of which can be adjusted in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_. 
 
 •	Liquid hydrocarbons: Modeled as a single node for Europe, since transport costs for liquids are low and no bottlenecks are expected.
 

From 8ab2d5f0e63b7c4cf9106ff8bfea27732b7faad0 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:11:33 +0100
Subject: [PATCH 182/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 726038c9..8e3239da 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -42,6 +42,6 @@ Here are some examples of how spatial resolution is set for different sectors in
 
 **Electricity distribution network**
 
-Contrary to the transmission grid, the grid topology at the distribution level (at and below 110 kV) is not included due to the very high computational burden. However, a link per node can be used (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L257>`_ file) to represent energy transferred between distribution and transmission levels at every node. In essence, the total energy capacity connecting the transmission grid and the low-voltage level is optimized. The cost assumptions for this link can be adjusted in Config file `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L258>`_ , and is currently assumed to be 500 eur/kW. 
+Contrary to the transmission grid, the grid topology at the distribution level (at and below 110 kV) is not included due to the very high computational burden. However, a link per node can be used (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L257>`_ file) to represent energy transferred between distribution and transmission levels at every node. In essence, the total energy capacity connecting the transmission grid and the low-voltage level is optimized. The cost assumptions for this link can be adjusted in Config file `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L258>`_ , and is currently assumed to be 500 Eur/kW. 
 
 Rooftop PV, heat pumps, resistive heater, home batteries chargers for passenger EVs, as well as individual heating technologies (heat pumps and resistive heaters)  are connected to low-voltage level. All the remaining generation and storage technologies are connected to the transmission grid. In practice, this means that the distribution grid capacity is only extended if it is necessary to balance the mismatch between local generation and demand.

From f83be559e4c95bea2a527e460e305e74a00e1604 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Mon, 21 Nov 2022 18:12:00 +0100
Subject: [PATCH 183/243] Update doc/technology_assumptions.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/technology_assumptions.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/technology_assumptions.rst b/doc/technology_assumptions.rst
index 2a42420e..82edf3c3 100644
--- a/doc/technology_assumptions.rst
+++ b/doc/technology_assumptions.rst
@@ -6,4 +6,4 @@ Technology and cost assumptions
 
 *Techno-Economic Assumptions*
 
-For the technological assumptions (cost,efficiency, lifetime, etc.), we take estimates for the investment year specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L43>`_. Many of those come from a database published by the Danish Energy Agency (`DEA <https://ens.dk/en/our-services/projections-and-models/technology-data>`_). Assumptions are maintained at the `technology data repository <https://github.com/PyPSA/technology-data>`_.
+For the technological assumptions (cost, efficiency, lifetime, etc.), we take estimates for the investment year specified in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L43>`_. Many of those come from a database published by the Danish Energy Agency (`DEA <https://ens.dk/en/our-services/projections-and-models/technology-data>`_). Assumptions are maintained at the `technology data repository <https://github.com/PyPSA/technology-data>`_.

From 7cc336e89e95bf9b06d6d8001cf57d4dd853ef03 Mon Sep 17 00:00:00 2001
From: martavp <martavictoriaperez@gmail.com>
Date: Mon, 21 Nov 2022 18:21:53 +0100
Subject: [PATCH 184/243] Update myopic.rst

---
 doc/myopic.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index 40326516..0fe72388 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -11,7 +11,9 @@ The myopic approach was initially developed and used in the paper `Early decarbo
 The current code applies the myopic approach to generators, storage technologies and links in the power sector. It furthermore applies it to the space and water heating sector (e.g., the share of district heating and reduced space heat demand), industry processes (e.g., steel, direct reduced iron, and aluminum production via primary route), the share of fuel cell and battery electric vehicles in land transport, and the hydrogen share in shipping (see :doc:`supply_demand` for further information).
 
 The following subjects within the land transport and biomass currently do not evolve with the myopic approach:
-- The percentage of electric vehicles that allow demand-side management and vehicle-to-grid services
+
+- The percentage of electric vehicles that allow demand-side management and vehicle-to-grid services.
+
 - The annual biomass potential (default year and scenario for which potential is taken is 2030, defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L109>`_)
 
 Configuration

From bfdc333858e16d6458d08cec0301239638ff6e6a Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 13:52:11 +0100
Subject: [PATCH 185/243] Testing math format

---
 doc/supply_demand.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 03160f8e..3162644f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -124,7 +124,8 @@ Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https:
 
 The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter (refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
-$$ &Delta; T = T_(sink) − T_(source) $$ 
+.. math::
+	&Delta; T = T_(sink) − T_(source) 
 
 For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file] For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
 

From 14f88652bfa11a95a5b98643205bf065e83517cb Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 13:57:02 +0100
Subject: [PATCH 186/243] Test math

---
 doc/supply_demand.rst | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 3162644f..e99ae532 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -612,3 +612,8 @@ Captured $CO_2$ can also be sequestered underground up to an annual sequestratio
 *Carbon dioxide transport*
 
 Carbon dioxide can be modelled as a single node for Europe (in this case, $CO_2$ transport constraints are neglected). A network for modelling the transport of $CO_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
+
+
+Test |H20|
+
+|H2O| replace:: H\ :sub:`2`\ O

From 2eef75e0f91857de458dad8967fddd05baa5d338 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 13:57:28 +0100
Subject: [PATCH 187/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index e99ae532..f3e2a293 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -614,6 +614,6 @@ Captured $CO_2$ can also be sequestered underground up to an annual sequestratio
 Carbon dioxide can be modelled as a single node for Europe (in this case, $CO_2$ transport constraints are neglected). A network for modelling the transport of $CO_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
 
 
-Test |H20|
+Test |H2O|
 
 |H2O| replace:: H\ :sub:`2`\ O

From 63ad6d42bf0b406424a4bdb1c56a8fb71a9f4471 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 13:58:04 +0100
Subject: [PATCH 188/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index f3e2a293..e43b69c5 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -616,4 +616,4 @@ Carbon dioxide can be modelled as a single node for Europe (in this case, $CO_2$
 
 Test |H2O|
 
-|H2O| replace:: H\ :sub:`2`\ O
+..|H2O| replace:: H\ :sub:`2`\ O

From cb8a64503672b2e90290a46b1bb2324597f27f7e Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 13:58:35 +0100
Subject: [PATCH 189/243] Test

---
 doc/supply_demand.rst | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index e43b69c5..ec316959 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -4,6 +4,10 @@
 Supply and demand
 ##########################################
 
+
+Test |H2O|
+
+.. |H2O| replace:: H\ :sub:`2`\ O
 An initial orientation to the supply and demand options in the model
 PyPSA-Eur-Sec can be found in the description of the model
 PyPSA-Eur-Sec-30 in the paper `Synergies of sector coupling and
@@ -614,6 +618,4 @@ Captured $CO_2$ can also be sequestered underground up to an annual sequestratio
 Carbon dioxide can be modelled as a single node for Europe (in this case, $CO_2$ transport constraints are neglected). A network for modelling the transport of $CO_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
 
 
-Test |H2O|
 
-..|H2O| replace:: H\ :sub:`2`\ O

From 98cac560acc5c0a88d5df890024855a488f0d889 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:01:28 +0100
Subject: [PATCH 190/243] Test

---
 doc/supply_demand.rst | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ec316959..c7542dd2 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -6,8 +6,16 @@ Supply and demand
 
 
 Test |H2O|
+|CH4|
+3|H2|
 
 .. |H2O| replace:: H\ :sub:`2`\ O
+.. |CH4| replace:: CH\ :sub:`4`
+.. |H2| replace:: H\ :sub:`2`
+
+
+
+CH_4 + H_2O → CO + 3H_2
 An initial orientation to the supply and demand options in the model
 PyPSA-Eur-Sec can be found in the description of the model
 PyPSA-Eur-Sec-30 in the paper `Synergies of sector coupling and

From 6c7727e3aa8fbbdf873fa23e65ba5d559193dbbd Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:01:57 +0100
Subject: [PATCH 191/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index c7542dd2..ddc2f2af 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -7,7 +7,9 @@ Supply and demand
 
 Test |H2O|
 |CH4|
-3|H2|
+3 |H2|
+
+This is a test
 
 .. |H2O| replace:: H\ :sub:`2`\ O
 .. |CH4| replace:: CH\ :sub:`4`

From ecf1df8b5004772c5a53c66d2e4b78bfce4679f4 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:07:45 +0100
Subject: [PATCH 192/243] Test

---
 doc/supply_demand.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ddc2f2af..d90c1fb7 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -11,6 +11,8 @@ Test |H2O|
 
 This is a test
 
+\ce{(2Na+,SO4^2- ) + (Ba^2+, 2Cl- ) -> BaSO4 v + 2NaCl}
+
 .. |H2O| replace:: H\ :sub:`2`\ O
 .. |CH4| replace:: CH\ :sub:`4`
 .. |H2| replace:: H\ :sub:`2`

From ba184321b148784ee3ffba59b03ffd28a47a31ae Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:08:59 +0100
Subject: [PATCH 193/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index d90c1fb7..b0692cb5 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -11,7 +11,7 @@ Test |H2O|
 
 This is a test
 
-\ce{(2Na+,SO4^2- ) + (Ba^2+, 2Cl- ) -> BaSO4 v + 2NaCl}
+.. math:: \ce{(2Na+,SO4^2- ) + (Ba^2+, 2Cl- ) -> BaSO4 v + 2NaCl}
 
 .. |H2O| replace:: H\ :sub:`2`\ O
 .. |CH4| replace:: CH\ :sub:`4`

From 556cec5c171891d896e15612e0a9d262cd98ef06 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:09:21 +0100
Subject: [PATCH 194/243] test

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index b0692cb5..e786f76f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -11,7 +11,7 @@ Test |H2O|
 
 This is a test
 
-.. math:: \ce{(2Na+,SO4^2- ) + (Ba^2+, 2Cl- ) -> BaSO4 v + 2NaCl}
+.. chemistry:: \ce{(2Na+,SO4^2- ) + (Ba^2+, 2Cl- ) -> BaSO4 v + 2NaCl}
 
 .. |H2O| replace:: H\ :sub:`2`\ O
 .. |CH4| replace:: CH\ :sub:`4`

From 2ba44b1749f9b8486f2c5a3e64c2e60b07196349 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:11:51 +0100
Subject: [PATCH 195/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index e786f76f..fd1d5ba9 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -11,7 +11,8 @@ Test |H2O|
 
 This is a test
 
-.. chemistry:: \ce{(2Na+,SO4^2- ) + (Ba^2+, 2Cl- ) -> BaSO4 v + 2NaCl}
+.. math:: 
+	CH_4 + H_2O → CO + 3H_2
 
 .. |H2O| replace:: H\ :sub:`2`\ O
 .. |CH4| replace:: CH\ :sub:`4`

From 928420ef3e0a8f1555c770c72a64a0f00101c653 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:12:51 +0100
Subject: [PATCH 196/243] Update supply_demand.rst

---
 doc/supply_demand.rst | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index fd1d5ba9..95a6de84 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -12,7 +12,10 @@ Test |H2O|
 This is a test
 
 .. math:: 
-	CH_4 + H_2O → CO + 3H_2
+	CH_4 + H_2O→ CO + 3H_2
+	
+.. math::
+   \frac{ \sum_{t=0}^{N}f(t,k) }{N}
 
 .. |H2O| replace:: H\ :sub:`2`\ O
 .. |CH4| replace:: CH\ :sub:`4`

From 12b5ad1e3d544a20b3b7871c5f69e8ed5bcb64f4 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Wed, 23 Nov 2022 15:53:39 +0100
Subject: [PATCH 197/243] Replacing $$ with rst format (.. math ::) etc

---
 .DS_Store             | Bin 6148 -> 8196 bytes
 doc/supply_demand.rst | 175 +++++++++++++++++++-----------------------
 2 files changed, 79 insertions(+), 96 deletions(-)

diff --git a/.DS_Store b/.DS_Store
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diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 95a6de84..a341eef5 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -1,29 +1,8 @@
-.. _supply_demand:
 
 ##########################################
 Supply and demand
 ##########################################
 
-
-Test |H2O|
-|CH4|
-3 |H2|
-
-This is a test
-
-.. math:: 
-	CH_4 + H_2O→ CO + 3H_2
-	
-.. math::
-   \frac{ \sum_{t=0}^{N}f(t,k) }{N}
-
-.. |H2O| replace:: H\ :sub:`2`\ O
-.. |CH4| replace:: CH\ :sub:`4`
-.. |H2| replace:: H\ :sub:`2`
-
-
-
-CH_4 + H_2O → CO + 3H_2
 An initial orientation to the supply and demand options in the model
 PyPSA-Eur-Sec can be found in the description of the model
 PyPSA-Eur-Sec-30 in the paper `Synergies of sector coupling and
@@ -145,15 +124,17 @@ Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https:
 The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter (refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
 
 .. math::
-	&Delta; T = T_(sink) − T_(source) 
+	\Delta T = T_{sink} − T_{source}
 
 For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file] For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
 
-$$ COP (&Delta; T) = 6.81 + 0.121&Delta; T + 0.000630&Delta; T^2; $$   
+.. math::
+   COP (\Delta T) = 6.81 + 0.121\Delta T + 0.000630\Delta T^2 
 
 for ground-sourced heat pumps (GSHP), we use the function:
 
-$$  COP(&Delta; T) = 8.77 + 0.150&Delta; T + 0.000734&Delta; T^2 $$     
+.. math::
+   COP(\Delta T) = 8.77 + 0.150\Delta T + 0.000734\Delta T^2     
 
 **Resistive heaters**
 
@@ -183,7 +164,7 @@ For the myopic transition paths, capacities already existing for technologies su
 Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
 
 Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modelled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store,  which are connected to heat demand buses through water charger/discharger links>`_.
-A thermal energy density of 46.8 kWh $_{th}$/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1- $e^{-1/24τ}$ is assumed to have a time constant  of  τ=180 days for central TES and  τ=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in config file. Charging and discharging efficiencies are 90% due to pipe losses.
+A thermal energy density of 46.8 kWh :math:`_{th}`/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1- :math:`e^{-1/24τ}` is assumed to have a time constant  of  τ=180 days for central TES and  τ=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in config file. Charging and discharging efficiencies are 90% due to pipe losses.
 
 **Retrofitting of the thermal envelope of buildings**
 
@@ -222,32 +203,34 @@ Hydrogen is also used for transport applications (see :ref:`Transportation`), wh
 Hydrogen supply
 =============================
 
-Today, most of the $H_2$ consumed globally is produced from natural gas by steam methane reforming (SMR)
+Today, most of the :math:`H_2` consumed globally is produced from natural gas by steam methane reforming (SMR)
 
-$$
-CH_4 + H_2O → CO + 3H_2
-$$
+.. math:: 
+
+   CH_4 + H_2O \xrightarrow{} CO + 3H_2
 
 combined with a water-gas shift reaction
 
-$$
-CO + H_2O → CO_2 + H_2
-$$
+.. math::
+
+   CO + H_2O \xrightarrow{} CO_2 + H_2
+
 
 SMR is included `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245>`_. 
-PyPSA-Eur-Sec allows this route of $H_2$ production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
+PyPSA-Eur-Sec allows this route of :math:`H_2` production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
 
 Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
 
-$$
-2H_2O → 2H_2 + O_2
-$$
+.. math:: 
+
+   2H_2O \xrightarrow{} 2H_2 + O_2
+
 
 For the electrolysis, alkaline electrolysers are chosen since they have lower cost and higher cumulative installed capacity than polymer electrolyte membrane (PEM) electrolysers. The techno-economic assumptions are taken from the technology-data repository. Waste heat from electrolysis is not leveraged in the model.
 
 **Transport**
 
-Hydrogen is transported by pipelines. $H_2$ pipelines are endogenously generated, either via a  greenfield $H_2$ network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
+Hydrogen is transported by pipelines. :math:`H_2` pipelines are endogenously generated, either via a  greenfield :math:`H_2` network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by  `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
 New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
 
 **Storage**
@@ -268,11 +251,11 @@ Methane supply
 
 In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modelled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki. 
 Biogas can be upgraded to methane. 
-Synthetic methane can be produced by processing hydrogen and captures $CO_2$ in the Sabatier reaction 
+Synthetic methane can be produced by processing hydrogen and captures :math:`CO_2` in the Sabatier reaction 
+
+.. math::              
+   CO_2 + 4H_2 \xrightarrow{} CH_4 + 2H_2O
 
-$$                   
-CO_2 + 4H_2 → CH_4 + 2H_2O
-$$
 
 Direct power-to-methane conversion with efficient heat integration developed in the HELMETH project is also an option. The share of synthetic, biogenic and fossil methane is an optimisation result depending on the techno-economic assumptions.
                 
@@ -337,17 +320,17 @@ The model can only use biogas by first upgrading it to natural gas quality [see
 Oil-based products demand
 ========================
 Naphtha is used as a feedstock in the chemicals industry (see :ref:`Chemicals Industry`). Furthermore, kerosene is used as transport fuel in the aviation sector (see :ref:`Aviation`). Non-electrified agriculture machinery also consumes gasoline. 
-Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using $H_2$-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
+Land transport [(see :ref:`Land transport`)  that is not electrified or converted into using :math:`H_2`-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected. 
 
 .. _Oil-based products supply:
 
 Oil-based products supply 
 ======================== 							
-Oil-based products can be either of fossil origin or synthetically produced by combining $H_2$ (see :ref:`Hydrogen supply`) and captured $CO_2$ (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
+Oil-based products can be either of fossil origin or synthetically produced by combining :math:`H_2` (see :ref:`Hydrogen supply`) and captured :math:`CO_2` (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants		
+
+.. math::
+   𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
 
-$$
-𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2}  +𝑛H_2O
-$$
 
 with costs as included from the `technology-data repository <https://github.com/PyPSA/technology-data/blob/master/latex_tables/tables_in_latex.pdf>`_. The waste heat from the Fischer-Tropsch process is supplied to `district heating networks <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_. The share of fossil and synthetic oil is an optimisation result depending on the techno-economic assumptions.
 
@@ -370,7 +353,7 @@ The Subsection overview below provides a general description of the modelling ap
 
 **Overview**
 
-Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia ( $NH_3$), ethylene ( $C_2H_4$) and methanol ( $CH_3OH$). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
+Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia ( :math:`NH_3`), ethylene ( :math:`C_2H_4`) and methanol ( :math:`CH_3OH`). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
 
 The overarching modelling procedure can be described as follows. First, the energy demands and process emissions for every unit of material output are estimated based on data from the `JRC-IDEES database <https://data.europa.eu/doi/10.2760/182725>`_ and the fuel and process switching described in the subsequent sections. Second, the 2050 energy demands and process emissions are calculated using the per-unit-of-material ratios based on the industry transformations and the `country-level material production in 2015 <https://data.europa.eu/doi/10.2760/182725>`_, assuming constant material demand.
 
@@ -411,41 +394,41 @@ Two alternative routes are used today to manufacture steel in Europe. The primar
 					
 The primary route uses blast furnaces in which coke is used to reduce iron ore into molten iron, which is then converted into steel:
 					
-$$
-CO_2 + C→ 2 CO
-$$ 
+.. math::
+CO_2 + C \xrightarrow{} 2 CO
 
-$$ 
-3 Fe_2O_3 + CO → 2 Fe_3O_4 + CO
-$$ 
 
-$$
-Fe_3O_4 + CO → 3 FeO + CO_2
-$$
+.. math::
+3 Fe_2O_3 + CO  \xrightarrow{} 2 Fe_3O_4 + CO
+
+
+.. math::
+Fe_3O_4 + CO  \xrightarrow{} 3 FeO + CO_2
+
+
+.. math::
+FeO + CO  \xrightarrow{} Fe + CO_2
 
-$$
-FeO + CO→ Fe + CO_2
-$$
 					
-The primary route of steelmaking implies large process emissions of 0.22 t $_{CO_2}$ /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_. 
+The primary route of steelmaking implies large process emissions of 0.22 t :math:`_{CO_2}` /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_. 
 					
-In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the $CO_2$ emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t $_{CO_2}$ /t of steel.
+In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the :math:`CO_2` emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t :math:`_{CO_2}` /t of steel.
 					
 We assume that the primary route can be replaced by a third route in 2050, using direct reduced iron (DRI) and subsequent processing in an EAF.
 					
-$$
-3 Fe_2O_3 + H_2→ 2 Fe_3O_4 + H_2O
-$$
+.. math::
+   3 Fe_2O_3 + H_2 \xrightarrow{} 2 Fe_3O_4 + H_2O
 
-$$
-Fe_3O_4 +H_2 →3FeO+H_2O
-$$
 
-$$
-FeO + H_2 → Fe + H_2O
-$$	
+.. math::
+   Fe_3O_4 +H_2  \xrightarrow{} 3FeO+H_2O
 
-This circumvents the process emissions associated with the use of coke. For hydrogen- based DRI, we assume energy requirements of 1.7 MWh $_{H_2}$ /t steel `(Vogl et. al) <https://doi.org/10.1016/j.jclepro.2018.08.279>`_ and 0.322 MWh $_{el}$ /t steel `(HYBRIT 2016) <https://dh5k8ug1gwbyz.cloudfront.net/uploads/2021/02/Hybrit-broschure-engelska.pdf>`_. 
+
+.. math::
+   FeO + H_2  \xrightarrow{} Fe + H_2O
+
+
+This circumvents the process emissions associated with the use of coke. For hydrogen- based DRI, we assume energy requirements of 1.7 MWh :math:`_{H_2}` /t steel (Vogl et. al) <https://doi.org/10.1016/j.jclepro.2018.08.279>`_ and 0.322 MWh :math:`_{el}`/t steel `(HYBRIT 2016) <https://dh5k8ug1gwbyz.cloudfront.net/uploads/2021/02/Hybrit-broschure-engelska.pdf>`_. 
 								
 					
 The share of steel produced via the primary route is exogenously set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L279>`_. The share of steel obtained via hydrogen-based DRI plus EAF is also set exogenously in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L287>`_. The remaining share is manufactured through the secondary route using scrap metal in EAF. Bioenergy as alternative to coke in blast furnaces is not considered in the model (`Mandova et.al <https://doi.org/10.1016/j.biombioe.2018.04.021>`_, `Suopajärvi et.al <https://doi.org/10.1016/j.apenergy.2018.01.060>`_).
@@ -464,22 +447,22 @@ The basic chemicals consumption data from the `JRC IDEES  <https://op.europa.eu/
 					
 Statistics for the production of ammonia, which is commonly used as a fertilizer, are taken from the `USGS <https://www.usgs.gov/media/files/nitrogen-2017-xlsx>`_ for every country. Ammonia can be made from hydrogen and nitrogen using the Haber-Bosch process.
 					
-$$
-N_2 + 3H_2 → 2NH_3
-$$
+.. math::
+   N_2 + 3H_2 \xrightarrow{}  2NH_3
 
 
-The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh $_{H_2}$ / t $_{NH_3}$ ) and electricity ( 1.17 MWh $_{el}$ /t $_{NH_3}$ ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
-The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh $_{el}$/t of chlorine and yield hydrogen at 0.937 MWh $_{H_2}$/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh $_{el}$/t of methanol and methane at 10.25 MWh $_{CH_4}$/t of methanol.
+The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh :math:`_{H_2}` / t :math:`_{NH_3}` ) and electricity ( 1.17 MWh :math:`_{el}` /t :math:`_{NH_3}` ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
+
+The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh `:math:`_{el}`/t of chlorine and yield hydrogen at 0.937 MWh :math:`_{H_2}`/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh :math:`_{el}`/t of methanol and methane at 10.25 MWh :math:`_{CH_4}`/t of methanol.
 		 	 	 		
 						
-The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWh $_{naphtha}$/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWh $_{CH_4}$ /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh $_{el}$/t of HVC).
+The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWh :math:`_{naphtha}`/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWh :math:`_{CH_4}` /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh :math:`_{el}`/t of HVC).
 					
-The process emissions from feedstock in the chemical industry are as high as 0.369 t $_{CO_2}$/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded $CO_2$ will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated $CO_2$ emissions. 
+The process emissions from feedstock in the chemical industry are as high as 0.369 t :math:`_{CO_2}`/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded :math:`CO_2` will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated :math:`CO_2` emissions. 
 		 	 	 					
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are assumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as 
-the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling processes are respectively 0.547 MWh $_{el}$/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh $_{el}$/t of HVC (as indicated in the config file `<https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
+the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling processes are respectively 0.547 MWh :math:`_{el}`/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh :math:`_{el}`/t of HVC (as indicated in the config file `<https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
 
 
 **Non-metallic Mineral Products**
@@ -488,14 +471,14 @@ This subsector includes the manufacturing of cement, ceramics, and glass.
 
 *Cement*
 
-Cement is used in construction to make concrete. The production of cement involves high energy consumption and large process emissions. The calcination of limestone to chemically reactive calcium oxide, also known as lime, involves process emissions of 0.54 t $_{CO_2}$ /t cement (see `Akhtar et al. <https://doi.org/10.1109/CITCON.2013.6525276>`_.		
+Cement is used in construction to make concrete. The production of cement involves high energy consumption and large process emissions. The calcination of limestone to chemically reactive calcium oxide, also known as lime, involves process emissions of 0.54 t :math:`_{CO_2}` /t cement (see `Akhtar et al. <https://doi.org/10.1109/CITCON.2013.6525276>`_.		
 
 
-$$
-CaCO_3 → CaO + CO_2 
-$$
+.. math::
+   CaCO_3 \xrightarrow{}  CaO + CO_2 
 
-Additionally, $CO_2$ is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
+
+Additionally, :math:`CO_2` is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
 
 Cement process emissions can be captured assuming a capture rate of 90%. Whether emissions are captured is decided by the model taking into account the capital costs of carbon capture modules. The electricity and heat demand of process emission carbon capture is currently ignored. For net-zero emission scenarios, the remaining process emissions need to be compensated by negative emissions. 		
 
@@ -504,11 +487,11 @@ With the exception of electricity demand and biomass demand for low-temperature
 
 *Ceramics*
 
-The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw materials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t $_{CO_2} $/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw materials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t :math:`_{CO_2} `/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 *Glass*
 
-The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh $_{el}l/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t $_{CO_2} $/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW $_{el}$/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh :math:`_{el}l/t` of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t :math:`_{CO_2} `/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW :math:`_{el}`/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 
 **Non-ferrous Metals**
@@ -519,15 +502,15 @@ The manufacturing of aluminium accounts for more than half of the final energy c
 
 The primary route involves two energy-intensive processes: the production of alumina from bauxite (aluminium ore) and the electrolysis to transform alumina into aluminium via the Hall-Héroult process
 
-$$
-2Al_2O_3 +3C → 4Al+3CO_2	
-$$
+.. math::
+   2Al_2O_3 +3C \xrightarrow{}  4Al+3CO_2	
 
-The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina which is supplied by methane and causes process emissions of 1.5 t $_{CO_2}$/t aluminium. According to `Friedrichsen et al. <http://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, inert anodes might become commercially available by 2030 that would eliminate the process emissions, but they are not included in the model. Assuming all subprocesses are electrified, the primary route requires 15.4 MWh $_{el}$/t of aluminium.
+
+The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina which is supplied by methane and causes process emissions of 1.5 t :math:`_{CO_2}`/t aluminium. According to `Friedrichsen et al. <http://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, inert anodes might become commercially available by 2030 that would eliminate the process emissions, but they are not included in the model. Assuming all subprocesses are electrified, the primary route requires 15.4 MWh :math:`_{el}`/t of aluminium.
 					
 In the secondary route, scrap aluminium is remelted. The energy demand for this process is only 10% of the primary route and there are no associated process emissions. Assuming all subprocesses are electrified, the secondary route requires 1.7 MWh/t of aluminium. The share of aliminum manufactured by the primary and secondary route can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L297>`_]
 	
-For the other non-ferrous metals, we assume the electrification of the entire manufacturing process with an average electricity demand of 3.2 MWh $_{el}$/t lead equivalent. 			
+For the other non-ferrous metals, we assume the electrification of the entire manufacturing process with an average electricity demand of 3.2 MWh :math:`_{el}`/t lead equivalent. 			
 
 **Other Industry Subsectors**
 
@@ -618,20 +601,20 @@ For the following point source emissions, carbon capture is applicable:
 Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
 is currently ignored.
 
-DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of $CO_2$ prior to storage which consumes electricity and rejects heat. 
+DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of :math:`CO_2` prior to storage which consumes electricity and rejects heat. 
 
 *Carbon dioxide usage*
 
-Captured $CO_2$ can be used to produce synthetic methane and synthetic oil products (e.g.
-naphtha). If captured carbon is used, the $CO_2$ emissions of the synthetic fuels are net-neutral. 
+Captured :math:`CO_2` can be used to produce synthetic methane and synthetic oil products (e.g.
+naphtha). If captured carbon is used, the :math:`CO_2` emissions of the synthetic fuels are net-neutral. 
 
 *Carbon dioxide sequestration*
 
-Captured $CO_2$ can also be sequestered underground up to an annual sequestration limit of 200 Mt $_{CO_2}$/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, $CO_2$ transport constraints are neglected. Since $CO_2$ sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
+Captured :math:`CO_2` can also be sequestered underground up to an annual sequestration limit of 200 Mt :math:`_{CO_2}`/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, :math:`CO_2` transport constraints are neglected. Since :math:`CO_2` sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
 
 *Carbon dioxide transport*
 
-Carbon dioxide can be modelled as a single node for Europe (in this case, $CO_2$ transport constraints are neglected). A network for modelling the transport of $CO_2$ among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
+Carbon dioxide can be modelled as a single node for Europe (in this case, :math:`CO_2` transport constraints are neglected). A network for modelling the transport of :math:`CO_2` among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. 
 
 
 

From 436c7cd086f4961a174cb24fe23bf3a1779cdbe4 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 12:58:51 +0100
Subject: [PATCH 198/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index a341eef5..a31bd49e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -62,7 +62,7 @@ Hot water demand is assumed to be constant throughout the year.
 
 *Urban and rural heating*
 
-For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percent of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
+For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percentage of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_. 
 
 *Cooling demand*
 

From d0237356d4c5d349b0ff94a5fff0e004b1e8bc9d Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 12:59:14 +0100
Subject: [PATCH 199/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index a31bd49e..28b00bac 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -92,7 +92,7 @@ Different supply options are available depending on whether demand is met centra
 
 **Urban central heat**
 
-For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large-scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more detailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`
+For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large-scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_  and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more detailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`.
 
 **Residential and Urban decentral heat**
 

From 28f4fa1b56ee07871853598aa508b0264ffbcf48 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 12:59:29 +0100
Subject: [PATCH 200/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 28b00bac..6cf1512f 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -117,7 +117,7 @@ NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coa
 
 **Micro-CHP**
 
-Pypsa-eur-sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
+PyPSA-Eur-Sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
 
 **Heat pumps**
 

From f3ef8011f42d7cba64fee5e4db81cffe567a68d4 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 12:59:58 +0100
Subject: [PATCH 201/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 6cf1512f..e2597d1e 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -126,7 +126,7 @@ The coefficient of performance (COP) of air- and ground-sourced heat pumps depen
 .. math::
 	\Delta T = T_{sink} − T_{source}
 
-For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file] For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
+For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file]. For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
 
 .. math::
    COP (\Delta T) = 6.81 + 0.121\Delta T + 0.000630\Delta T^2 

From f76562d53a43adbc0fac06e383cd3f27b4cbcc05 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:00:20 +0100
Subject: [PATCH 202/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index e2597d1e..916f0c17 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -153,7 +153,7 @@ Solar thermal profiles are built based on weather data and also have the `option
 
 **Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants**
 
-Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ , methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
+Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_, methanation, and Direct Air Capture (DAC) is dumped into  district heating networks. 
 
 **Existing heating capacities and decommissioning**
 

From 3ef935c2625fe3668d1e563f9cf8932a219f7985 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:00:55 +0100
Subject: [PATCH 203/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 916f0c17..2760bd22 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -543,7 +543,7 @@ For the electrified land transport, country-specific factors are computed by com
 
 For BEVs the user can define the `storage energy capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L173>`_, `charging power capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L176>`_, and `charging efficiency <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L174>`_.
 
-For BEV, smart charging is an option. A `certain share <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L172>`_ of the BEV fleet can shift their charging time. The BEV state of charge is forced to be higher than a `set percentage <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L163>`_, e.g. 75%, every day at a `specified hour <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L164>`_, e.g., 7 am, to ensure that the batteries are sufficiently charged for peak usage in the morning and they not behave as seasonal storage. They also have the option to participate in vehicle-to-grid (V2G) services to facilitate system operation if that `is enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L179>`_
+For BEV, smart charging is an option. A `certain share <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L172>`_ of the BEV fleet can shift their charging time. The BEV state of charge is forced to be higher than a `set percentage <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L163>`_, e.g. 75%, every day at a `specified hour <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L164>`_, e.g., 7 am, to ensure that the batteries are sufficiently charged for peak usage in the morning and they not behave as seasonal storage. They also have the option to participate in vehicle-to-grid (V2G) services to facilitate system operation if that `is enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L179>`_.
 
 The battery cost of BEV is not included in the model since it is assumed that BEV owners buy them to primarily satisfy their mobility needs.
 

From c4868cdf1906c937b6e4e3c4f9a902067670896f Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:01:16 +0100
Subject: [PATCH 204/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 1 -
 1 file changed, 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 2760bd22..b970c35b 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -516,7 +516,6 @@ For the other non-ferrous metals, we assume the electrification of the entire ma
 
 The remaining industry subsectors include (a) pulp, paper, printing, (b) food, beverages, tobacco, (c) textiles and leather, (d) machinery equipment, (e) transport equipment, (f) wood and wood products, (g) others. Low- and mid-temperature process heat in these industries is assumed to be `supplied by biomass <https://doi.org/10.1016/j.rser.2021.110856>`_  while the remaining processes are electrified. None of the subsectors involve process emissions.
 
-Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC IDEES database <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_.  Missing countries are filled with `eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat) machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is for this sector is classified as services rural heat. Time series for demands are assumed to be constant and distributed inside countries in proportion to population. 
 
 Agriculture demand
 =========================

From f3e36e9b9acbfd86627fe343ae4c6b90c4d23df2 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:01:26 +0100
Subject: [PATCH 205/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index b970c35b..95bcbbe6 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -568,7 +568,7 @@ The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c99
 
 **Shipping**
 
-Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec.The share of shipping that is assumed to be supplied by hydrogen can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
+Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec. The share of shipping that is assumed to be supplied by hydrogen can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
 
 To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
 

From bf74b17884c01dbd0124c2bf9d529c3672801e6b Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:45:13 +0100
Subject: [PATCH 206/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index 0fe72388..7ee20792 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -89,7 +89,7 @@ $e(t) = e_0 (1+ (r+m)t) e^(-mt)$
 
 where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the ``scenario.sector_opts``
 E.g.  ``-cb25.7ex0`` (1.5C increase)
-Or ``cb73.9ex0`` (2C increase)
+Or ``cb73.9ex0`` (2C increase).
 See details in Supplemental Note S1 `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__
 
 

From 79fe300a6a1ee69c1c5487218ec637b0ebc9b9f8 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:45:48 +0100
Subject: [PATCH 207/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index 7ee20792..7a6a27d1 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -90,7 +90,7 @@ $e(t) = e_0 (1+ (r+m)t) e^(-mt)$
 where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the ``scenario.sector_opts``
 E.g.  ``-cb25.7ex0`` (1.5C increase)
 Or ``cb73.9ex0`` (2C increase).
-See details in Supplemental Note S1 `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__
+See details in Supplemental Note S1 `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__.
 
 
 General myopic code structure

From d5da1e32e1dda2f974eed44e71175806ac1d5c1c Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:46:31 +0100
Subject: [PATCH 208/243] Update doc/myopic.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/myopic.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index 7a6a27d1..08ff1e16 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -124,7 +124,7 @@ Rule overview
   Existing wind and solar capacities are retrieved from `IRENA annual statistics <https://www.irena.org/Statistics/Download-Data>`__ and distributed among the nodes in a country proportional to capacity factor. (This will be updated to include capacity distributions closer to reality.)
 
   Existing heating capacities are retrieved from the report `Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)
-  <https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__
+  <https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__.
 
   The heating capacities are assumed to have a lifetime indicated by the parameter lifetime in the configuration file, e.g 25 years. They are assumed to be decommissioned linearly starting on the base year, e.g., from 2020 to 2045.
 

From dc937d6533013de901888d4bc830139fbfaae968 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:47:30 +0100
Subject: [PATCH 209/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 1 -
 1 file changed, 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 95bcbbe6..61d35ae2 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -359,7 +359,6 @@ The overarching modelling procedure can be described as follows. First, the ener
 
 Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_,  `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_. 
 
-a
 
 Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane. 
 

From 95273ea1eda3ccd4c930cd02565d410b9500353f Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:48:55 +0100
Subject: [PATCH 210/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 8e3239da..b065933f 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -4,7 +4,7 @@
 Spatial resolution
 ##########################################
 
-The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ , which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities).
+The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_, which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities).
 
 Exemplary unsolved network clustered to 512 nodes:
 

From 97a9365a22277987b559166310781d99b7a302dc Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:49:08 +0100
Subject: [PATCH 211/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 61d35ae2..cbee6947 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -302,7 +302,7 @@ Solid biomass can be used directly to provide process heat up to 500˚C in the i
 Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ).
 
 
-Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ).
+Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_).
 
 
 *Transport of solid biomass*

From e002804d26d9ec5fa6f3339952ce53cde4f43db8 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:49:30 +0100
Subject: [PATCH 212/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index b065933f..80f39200 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -14,7 +14,7 @@ Exemplary unsolved network clustered to 37 nodes:
 
 .. image:: ../graphics/elec_s_37.png
 
-The total number of nodes for Europe is set in the config.yaml file under `clusters <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L20>`_.  The number of nodes can vary between 37, the number of independent countries/ synchronous areas, and several hundred. With 200-300 nodes, the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
+The total number of nodes for Europe is set in the config.yaml file under `clusters <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L20>`_.  The number of nodes can vary between 37, the number of independent countries/synchronous areas, and several hundred. With 200-300 nodes, the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
 Not all of the sectors are at the full nodal resolution, and some demand for some sectors is distributed to nodes using heuristics that need to be corrected. Some networks are copper-plated to reduce computational times.
 
 Here are some examples of how spatial resolution is set for different sectors in PyPSA-Eur-Sec:

From dac1eb17928e7d049c8b68d1d6dcd2df7e6e733c Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:49:43 +0100
Subject: [PATCH 213/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 80f39200..855d6268 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -19,7 +19,7 @@ Not all of the sectors are at the full nodal resolution, and some demand for som
 
 Here are some examples of how spatial resolution is set for different sectors in PyPSA-Eur-Sec:
 
-•	Electricity network: Modeled as nodal
+•	Electricity network: Modeled as nodal.
 
 •	Electricity residential and commercial demand: Modeled as nodal, distributed in each country based on population and GDP.
 

From 55025e12344df0bf8b5dbeeb37085a8c0b2e16b2 Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 13:49:55 +0100
Subject: [PATCH 214/243] Update doc/spatial_resolution.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/spatial_resolution.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/spatial_resolution.rst b/doc/spatial_resolution.rst
index 855d6268..fa87ae99 100644
--- a/doc/spatial_resolution.rst
+++ b/doc/spatial_resolution.rst
@@ -30,7 +30,7 @@ Here are some examples of how spatial resolution is set for different sectors in
 •	Electricity demand in industry: Modeled as nodal, based on the location of industrial facilities from HotMaps database.
 
 •	Industry demand (heat, chemicals, etc.) : Modeled as nodal, distributed in each country based on locations of industry from HotMaps database.
-•	Hydrogen network: Modeled as nodal (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L260>`_ file).
+•	Hydrogen network: Modeled as nodal (if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L260>`_ file).
 
 •	Methane network: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_. One node can be considered reasonable since future demand is expected to be low and no bottlenecks are expected. Also, the nodally resolved methane grid is based on SciGRID_gas data.
 

From cfff99054aa988ea1cd66e8f12dc1b9bebdf20ed Mon Sep 17 00:00:00 2001
From: martavp <30744159+martavp@users.noreply.github.com>
Date: Sun, 27 Nov 2022 14:00:06 +0100
Subject: [PATCH 215/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index cbee6947..74ef9e71 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -299,7 +299,7 @@ A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be th
 Solid biomass can be used directly to provide process heat up to 500˚C in the industry. It can also be burned in CHP plants and boilers associated with heating systems. These technologies are described elsewhere (see :ref:`Large-scale CHP` and :ref:`Industry demand`).
 
 
-Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ).
+Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_).
 
 
 Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_).

From 7f1a0f7c9a04a5d1078688bb81b06f221c7f458c Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 27 Nov 2022 18:20:15 +0100
Subject: [PATCH 216/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 74ef9e71..c1641199 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -282,7 +282,7 @@ Biomass demand
 Biomass supply potentials for every NUTS2 region are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and different availability scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_. The data for NUTS2 regions is mapped to PyPSA-Eur-Sec model regions in proportion to the area overlap.
 
 
-The desired scenario can be selected in the pypsa-eur-sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JRC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
+The desired scenario can be selected in the PyPSA-Eur-Sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JRC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
 
 
 The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the following categories: biogas, solid biomass, and not included. Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas, which can be upgraded to biomethane. Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste, are available for combustion in combined-heat-and power (CHP) plants and for medium temperature heat (below 500 °C) applications in industry. It can also converted to gas or liquid fuels.

From f295064df2ed05de2c80b681399ecf5f00ce23d7 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 27 Nov 2022 18:21:17 +0100
Subject: [PATCH 217/243] Update doc/supply_demand.rst

Co-authored-by: energyLS <89515385+energyLS@users.noreply.github.com>
---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index c1641199..cf687cb4 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -242,7 +242,7 @@ Hydrogen can be stored in overground steel tanks or `underground salt caverns <h
 Methane demand  
 ====================================
                   
-Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat (see :ref:`Industry demand`). Methane is not used in the trans- port sector because of engine slippage.
+Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat (see :ref:`Industry demand`). Methane is not used in the transport sector because of engine slippage.
 
 .. _Methane supply:
 

From c44f1d61c7c6d7e7bb4a75b177b3ad09c35399b1 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 27 Nov 2022 18:28:57 +0100
Subject: [PATCH 218/243] remove hard-coded methanol efficiency

---
 scripts/prepare_sector_network.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 3a2a7de9..7787d007 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2182,7 +2182,7 @@ def add_industry(n, costs):
             bus3=spatial.co2.nodes,
             carrier="methanolisation",
             p_nom_extendable=True,
-            capital_cost=costs.at["methanolisation", 'fixed'] * 0.8787, # EUR/MW_H2/a
+            capital_cost=costs.at["methanolisation", 'fixed'] * options["MWh_MeOH_per_MWh_H2"], # EUR/MW_H2/a
             lifetime=costs.at["methanolisation", 'lifetime'],
             efficiency=options["MWh_MeOH_per_MWh_H2"],
             efficiency2=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_tCO2"],

From 88db7a458fb412c56d5275e8bd395945fed95dc7 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sun, 27 Nov 2022 18:38:32 +0100
Subject: [PATCH 219/243] minor fixes

---
 .DS_Store             | Bin 8196 -> 0 bytes
 doc/myopic.rst        |  10 +++++-----
 doc/supply_demand.rst |  12 ++++++------
 3 files changed, 11 insertions(+), 11 deletions(-)
 delete mode 100644 .DS_Store

diff --git a/.DS_Store b/.DS_Store
deleted file mode 100644
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GIT binary patch
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diff --git a/doc/myopic.rst b/doc/myopic.rst
index 08ff1e16..af67fac7 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -85,7 +85,8 @@ E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 GtCO_2 following an expone
 They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
 The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed throughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
 
-$e(t) = e_0 (1+ (r+m)t) e^(-mt)$
+.. math::
+  e(t) = e_0 (1+ (r+m)t) e^(-mt)
 
 where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the ``scenario.sector_opts``
 E.g.  ``-cb25.7ex0`` (1.5C increase)
@@ -98,13 +99,12 @@ General myopic code structure
 
 The myopic code solves the network for the time steps included in ``planning_horizons`` in a recursive loop, so that:
 
-1.The existing capacities (those installed before the base year are added as fixed capacities with p_nom=value, p_nom_extendable=False). E.g. for baseyear=2020, capacities installed before 2020 are added. In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
-
+1. The existing capacities (those installed before the base year are added as fixed capacities with p_nom=value, p_nom_extendable=False). E.g. for baseyear=2020, capacities installed before 2020 are added. In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``. 
 The base year is the first element in ``planning_horizons``. Step 1 is implemented with the rule add_baseyear for the base year and with the rule add_brownfield for the remaining planning_horizons.
 
-2.The 2020 network is optimized. The solved network is saved in ``results/run_name/networks/postnetworks``
+2. The 2020 network is optimized. The solved network is saved in ``results/run_name/networks/postnetworks``
 
-3.For the next planning horizon, e.g. 2030, the capacities from a previous time step are added if they are still in operation (i.e., if they fulfil planning horizon <= commissioned year + lifetime). In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
+3. For the next planning horizon, e.g. 2030, the capacities from a previous time step are added if they are still in operation (i.e., if they fulfil planning horizon <= commissioned year + lifetime). In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
 
 Steps 2 and 3 are solved recursively for all the planning_horizons included in ``config.yaml``.
 
diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index cf687cb4..71d79cf5 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -44,11 +44,11 @@ Annual heat demands per country are retrieved from `JRC-IDEES  <https://op.europ
 
 *Space heating*
 
-The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes [Refer to PyPSA-Eur-Sec config file, `line 212 <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L212>`_.
+The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes.
 
 .. literalinclude:: ../config.default.yaml
     :language: yaml
-    :lines: 212
+    :lines: 205
 
 Co-optimsing of building renovation is also possible, if it is activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_.
 Renovation of the thermal envelope reduces the space heating demand and is optimised at each node for every heat bus. Renovation measures through additional insulation material and replacement of energy inefficient windows are considered.
@@ -394,19 +394,19 @@ Two alternative routes are used today to manufacture steel in Europe. The primar
 The primary route uses blast furnaces in which coke is used to reduce iron ore into molten iron, which is then converted into steel:
 					
 .. math::
-CO_2 + C \xrightarrow{} 2 CO
+   CO_2 + C \xrightarrow{} 2 CO
 
 
 .. math::
-3 Fe_2O_3 + CO  \xrightarrow{} 2 Fe_3O_4 + CO
+   3 Fe_2O_3 + CO  \xrightarrow{} 2 Fe_3O_4 + CO
 
 
 .. math::
-Fe_3O_4 + CO  \xrightarrow{} 3 FeO + CO_2
+   Fe_3O_4 + CO  \xrightarrow{} 3 FeO + CO_2
 
 
 .. math::
-FeO + CO  \xrightarrow{} Fe + CO_2
+   FeO + CO  \xrightarrow{} Fe + CO_2
 
 					
 The primary route of steelmaking implies large process emissions of 0.22 t :math:`_{CO_2}` /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_. 

From 5b256bfa5db8b8c0f0663e7227191ed5dbe04e1c Mon Sep 17 00:00:00 2001
From: lisazeyen <lisa.zeyen@web.de>
Date: Mon, 28 Nov 2022 16:56:42 +0100
Subject: [PATCH 220/243] take VOM for RES from existing

---
 scripts/add_existing_baseyear.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/scripts/add_existing_baseyear.py b/scripts/add_existing_baseyear.py
index e5b7e1d1..8f2fdf24 100644
--- a/scripts/add_existing_baseyear.py
+++ b/scripts/add_existing_baseyear.py
@@ -234,7 +234,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
             # solar utility and rooftop as well, rather take cost assumptions
             # from existing network than from the cost database
             capital_cost = n.generators.loc[n.generators.carrier==generator+suffix, "capital_cost"].mean()
-
+            marginal_cost = n.generators.loc[n.generators.carrier==generator+suffix, "marginal_cost"].mean()
             # check if assets are already in network (e.g. for 2020)
             already_build = n.generators.index.intersection(asset_i)
             new_build = asset_i.difference(n.generators.index)
@@ -262,7 +262,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
                         bus=ind,
                         carrier=generator,
                         p_nom=new_capacity[ind] / len(inv_ind), # split among regions in a country
-                        marginal_cost=costs.at[generator,'VOM'],
+                        marginal_cost=marginal_cost,
                         capital_cost=capital_cost,
                         efficiency=costs.at[generator, 'efficiency'],
                         p_max_pu=p_max_pu,
@@ -281,7 +281,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
                         bus=new_capacity.index,
                         carrier=generator,
                         p_nom=new_capacity,
-                        marginal_cost=costs.at[generator, 'VOM'],
+                        marginal_cost=marginal_cost,
                         capital_cost=capital_cost,
                         efficiency=costs.at[generator, 'efficiency'],
                         p_max_pu=p_max_pu.rename(columns=n.generators.bus),
@@ -520,10 +520,10 @@ if __name__ == "__main__":
         snakemake = mock_snakemake(
             'add_existing_baseyear',
             simpl='',
-            clusters="45",
+            clusters="37",
             lv=1.0,
             opts='',
-            sector_opts='365H-T-H-B-I-A-solar+p3-dist1',
+            sector_opts='800sn-T-H-B-I-A-solar+p3-dist1-co2min-1p7',
             planning_horizons=2030,
         )
 

From 7208d9603964ef83b5f661c8d4117a796392d863 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 1 Dec 2022 17:18:48 +0100
Subject: [PATCH 221/243] Update config.default.yaml

Co-authored-by: euronion <42553970+euronion@users.noreply.github.com>
---
 config.default.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index 1cd4c271..e56a4dd6 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -194,9 +194,9 @@ sector:
   agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
   agriculture_machinery_electric_efficiency: 0.3 # electricity per use
   shipping_methanol: true
-  MWh_MeOH_per_MWh_H2: 0.8787 # https://github.com/euronion/trace/blob/main/data/efficiencies.csv
-  MWh_MeOH_per_tCO2: 4.0321 # https://github.com/euronion/trace/blob/main/data/efficiencies.csv
-  MWh_MeOH_per_MWh_e: 3.6907 # https://github.com/euronion/trace/blob/main/data/efficiencies.csv
+  MWh_MeOH_per_MWh_H2: 0.8787 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
+  MWh_MeOH_per_tCO2: 4.0321 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
+  MWh_MeOH_per_MWh_e: 3.6907 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
   shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
   shipping_hydrogen_share:  # 1 means all hydrogen FC

From 2eefba3b9520532eca4fa3d6e3039cf3867e090b Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 2 Dec 2022 15:58:57 +0100
Subject: [PATCH 222/243] Start refining

Fixing typos
---
 doc/supply_demand.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index 71d79cf5..ca7db739 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -427,7 +427,7 @@ We assume that the primary route can be replaced by a third route in 2050, using
    FeO + H_2  \xrightarrow{} Fe + H_2O
 
 
-This circumvents the process emissions associated with the use of coke. For hydrogen- based DRI, we assume energy requirements of 1.7 MWh :math:`_{H_2}` /t steel (Vogl et. al) <https://doi.org/10.1016/j.jclepro.2018.08.279>`_ and 0.322 MWh :math:`_{el}`/t steel `(HYBRIT 2016) <https://dh5k8ug1gwbyz.cloudfront.net/uploads/2021/02/Hybrit-broschure-engelska.pdf>`_. 
+This circumvents the process emissions associated with the use of coke. For hydrogen- based DRI, we assume energy requirements of 1.7 MWh :math:`_{H_2}` /t steel `(Vogl et. al) <https://doi.org/10.1016/j.jclepro.2018.08.279>`_ and 0.322 MWh :math:`_{el}`/t steel `(HYBRIT 2016) <https://dh5k8ug1gwbyz.cloudfront.net/uploads/2021/02/Hybrit-broschure-engelska.pdf>`_. 
 								
 					
 The share of steel produced via the primary route is exogenously set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L279>`_. The share of steel obtained via hydrogen-based DRI plus EAF is also set exogenously in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L287>`_. The remaining share is manufactured through the secondary route using scrap metal in EAF. Bioenergy as alternative to coke in blast furnaces is not considered in the model (`Mandova et.al <https://doi.org/10.1016/j.biombioe.2018.04.021>`_, `Suopajärvi et.al <https://doi.org/10.1016/j.apenergy.2018.01.060>`_).
@@ -453,7 +453,7 @@ Statistics for the production of ammonia, which is commonly used as a fertilizer
 
 The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh :math:`_{H_2}` / t :math:`_{NH_3}` ) and electricity ( 1.17 MWh :math:`_{el}` /t :math:`_{NH_3}` ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
 
-The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh `:math:`_{el}`/t of chlorine and yield hydrogen at 0.937 MWh :math:`_{H_2}`/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh :math:`_{el}`/t of methanol and methane at 10.25 MWh :math:`_{CH_4}`/t of methanol.
+The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh :math:`_{el}`/t of chlorine and yield hydrogen at 0.937 MWh :math:`_{H_2}`/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh :math:`_{el}`/t of methanol and methane at 10.25 MWh :math:`_{CH_4}`/t of methanol.
 		 	 	 		
 						
 The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWh :math:`_{naphtha}`/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWh :math:`_{CH_4}` /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh :math:`_{el}`/t of HVC).
@@ -461,7 +461,7 @@ The production of ammonia, methanol, and chlorine production is deducted from th
 The process emissions from feedstock in the chemical industry are as high as 0.369 t :math:`_{CO_2}`/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded :math:`CO_2` will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated :math:`CO_2` emissions. 
 		 	 	 					
 Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are assumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as 
-the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling processes are respectively 0.547 MWh :math:`_{el}`/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh :math:`_{el}`/t of HVC (as indicated in the config file `<https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
+the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling processes are respectively 0.547 MWh :math:`_{el}`/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh :math:`_{el}`/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report). 
 
 
 **Non-metallic Mineral Products**
@@ -486,7 +486,7 @@ With the exception of electricity demand and biomass demand for low-temperature
 
 *Ceramics*
 
-The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw materials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t :math:`_{CO_2} `/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw materials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t :math:`_{CO_2}`/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 *Glass*
 

From bf0b52b8ae0b4304a5556898528629e860543b95 Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 2 Dec 2022 16:15:28 +0100
Subject: [PATCH 223/243] More refining

---
 doc/supply_demand.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/supply_demand.rst b/doc/supply_demand.rst
index ca7db739..ce9f21dc 100644
--- a/doc/supply_demand.rst
+++ b/doc/supply_demand.rst
@@ -490,7 +490,7 @@ The ceramics sector is assumed to be fully electrified based on the current effi
 
 *Glass*
 
-The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh :math:`_{el}l/t` of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t :math:`_{CO_2} `/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW :math:`_{el}`/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
+The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh :math:`_{el}`/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t :math:`_{CO_2}`/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW :math:`_{el}`/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
 
 
 **Non-ferrous Metals**

From 0c62bfc9ee1cb737f983fed54e842dd5fe7f4fd0 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sat, 3 Dec 2022 12:22:05 +0100
Subject: [PATCH 224/243] complete methanol for shipping

---
 config.default.yaml               |  15 +--
 scripts/prepare_sector_network.py | 169 +++++++++++++++++-------------
 2 files changed, 104 insertions(+), 80 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index e56a4dd6..ce078fb5 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -193,20 +193,15 @@ sector:
   agriculture_machinery_electric_share: 0
   agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
   agriculture_machinery_electric_efficiency: 0.3 # electricity per use
-  shipping_methanol: true
   MWh_MeOH_per_MWh_H2: 0.8787 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
   MWh_MeOH_per_tCO2: 4.0321 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
   MWh_MeOH_per_MWh_e: 3.6907 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
-  shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
-  shipping_hydrogen_share:  # 1 means all hydrogen FC
-    2020: 0
-    2025: 0
-    2030: 0.05
-    2035: 0.15
-    2040: 0.3
-    2045: 0.6
-    2050: 1
+  shipping_hydrogen_share: 0
+  shippint_methanol_share: 1
+  shipping_oil_share: 0
+  shipping_methanol_efficiency: 0.46 # 10-15% higher https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf, https://users.ugent.be/~lsileghe/documents/extended_abstract.pdf
+  shipping_oil_efficiency: 0.40 #For conversion of fuel oil to propulsion in 2011
   time_dep_hp_cop: true #time dependent heat pump coefficient of performance
   heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
    # conservatively high to cover hot water and space heating in poorly-insulated buildings
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 7787d007..39904a20 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2139,32 +2139,56 @@ def add_industry(n, costs):
         p_set=industrial_demand.loc[nodes, "hydrogen"] / 8760
     )
 
-    if options["shipping_hydrogen_liquefaction"]:
+    shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
+    shipping_methanol_share = get(options['shipping_methanol_share'], investment_year)
+    shipping_oil_share = get(options['shipping_oil_share'], investment_year)
 
-        n.madd("Bus",
+    total_share = shipping_hydrogen_share + shipping_methanol_share + shipping_oil_share
+    if total_share != 1:
+        logger.warning(f"Total shipping shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
+
+    all_navigation = ["total international navigation", "total domestic navigation"]
+    p_set = pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6  / 8760
+
+    if shipping_hydrogen_share:
+
+        if options["shipping_hydrogen_liquefaction"]:
+
+            n.madd("Bus",
+                nodes,
+                suffix=" H2 liquid",
+                carrier="H2 liquid",
+                location=nodes,
+                unit="MWh_LHV"
+            )
+
+            n.madd("Link",
+                nodes + " H2 liquefaction",
+                bus0=nodes + " H2",
+                bus1=nodes + " H2 liquid",
+                carrier="H2 liquefaction",
+                efficiency=costs.at["H2 liquefaction", 'efficiency'],
+                capital_cost=costs.at["H2 liquefaction", 'fixed'],
+                p_nom_extendable=True,
+                lifetime=costs.at['H2 liquefaction', 'lifetime']
+            )
+
+            shipping_bus = nodes + " H2 liquid"
+        else:
+            shipping_bus = nodes + " H2"
+
+        efficiency = options['shipping_oil_efficiency'] / costs.at["fuel cell", "efficiency"]
+        p_set_hydrogen = shipping_hydrogen_share * p_set * efficiency
+
+        n.madd("Load",
             nodes,
-            suffix=" H2 liquid",
-            carrier="H2 liquid",
-            location=nodes,
-            unit="MWh_LHV"
+            suffix=" H2 for shipping",
+            bus=shipping_bus,
+            carrier="H2 for shipping",
+            p_set=p_set_hydrogen
         )
 
-        n.madd("Link",
-            nodes + " H2 liquefaction",
-            bus0=nodes + " H2",
-            bus1=nodes + " H2 liquid",
-            carrier="H2 liquefaction",
-            efficiency=costs.at["H2 liquefaction", 'efficiency'],
-            capital_cost=costs.at["H2 liquefaction", 'fixed'],
-            p_nom_extendable=True,
-            lifetime=costs.at['H2 liquefaction', 'lifetime']
-        )
-
-        shipping_bus = nodes + " H2 liquid"
-    else:
-        shipping_bus = nodes + " H2"
-
-    if options.get("shipping_methanol"):
+    if shipping_methanol_share:
 
         n.madd("Bus",
             spatial.methanol.nodes,
@@ -2173,7 +2197,6 @@ def add_industry(n, costs):
             unit="MWh_LHV"
         )
 
-        # methanolisation
         n.madd(
             spatial.h2.locations + "methanolisation",
             bus0=spatial.h2.nodes,
@@ -2185,38 +2208,44 @@ def add_industry(n, costs):
             capital_cost=costs.at["methanolisation", 'fixed'] * options["MWh_MeOH_per_MWh_H2"], # EUR/MW_H2/a
             lifetime=costs.at["methanolisation", 'lifetime'],
             efficiency=options["MWh_MeOH_per_MWh_H2"],
-            efficiency2=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_tCO2"],
-            efficiency3=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_MWh_e"],
+            efficiency2=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_MWh_e"],
+            efficiency3=- options["MWh_MeOH_per_MWh_H2"] / options["MWh_MeOH_per_tCO2"],
         )
 
-    all_navigation = ["total international navigation", "total domestic navigation"]
-    efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
-    shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
-    p_set = shipping_hydrogen_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
-
-    n.madd("Load",
-        nodes,
-        suffix=" H2 for shipping",
-        bus=shipping_bus,
-        carrier="H2 for shipping",
-        p_set=p_set
-    )
-
-    if shipping_hydrogen_share < 1:
-
-        shipping_oil_share = 1 - shipping_hydrogen_share
-
-        p_set = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
+        efficiency = options["shipping_oil_efficiency"] / options["shipping_methanol_efficiency"]
+        p_set_methanol = shipping_methanol_share * p_set.sum() * efficiency
 
         n.madd("Load",
-            nodes,
+            spatial.methanol.nodes,
+            suffix=" shipping methanol",
+            bus=spatial.methanol.nodes,
+            carrier="shipping methanol",
+            p_set=p_set_methanol,
+        )
+
+        # CO2 intensity methanol based on stoichiometric calculation with 22.7 GJ/t methanol (32 g/mol), CO2 (44 g/mol), 277.78 MWh/TJ = 0.218 t/MWh
+        co2 = p_set_methanol / options["MWh_MeOH_per_tCO2"]
+
+        n.add("Load"
+            "shipping methanol emissions",
+            bus="co2 atmosphere",
+            carrier="shipping methanol emissions",
+            p_set=-co2,
+        )
+
+    if shipping_oil_share:
+
+        p_set_oil = shipping_oil_share * p_set.sum()
+
+        n.madd("Load",
+            spatial.oil.nodes,
             suffix=" shipping oil",
             bus=spatial.oil.nodes,
             carrier="shipping oil",
-            p_set=p_set
+            p_set=p_set_oil
         )
 
-        co2 = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
+        co2 = p_set_oil * costs.at["oil", "CO2 intensity"]
 
         n.add("Load",
             "shipping oil emissions",
@@ -2225,34 +2254,34 @@ def add_industry(n, costs):
             p_set=-co2
         )
 
-    if "oil" not in n.buses.carrier.unique():
-        n.madd("Bus",
-            spatial.oil.nodes,
-            location=spatial.oil.locations,
-            carrier="oil",
-            unit="MWh_LHV"
-        )
+        if "oil" not in n.buses.carrier.unique():
+            n.madd("Bus",
+                spatial.oil.nodes,
+                location=spatial.oil.locations,
+                carrier="oil",
+                unit="MWh_LHV"
+            )
 
-    if "oil" not in n.stores.carrier.unique():
+        if "oil" not in n.stores.carrier.unique():
 
-        #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
-        n.madd("Store",
-            [oil_bus + " Store" for oil_bus in spatial.oil.nodes],
-            bus=spatial.oil.nodes,
-            e_nom_extendable=True,
-            e_cyclic=True,
-            carrier="oil",
-        )
+            #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
+            n.madd("Store",
+                [oil_bus + " Store" for oil_bus in spatial.oil.nodes],
+                bus=spatial.oil.nodes,
+                e_nom_extendable=True,
+                e_cyclic=True,
+                carrier="oil",
+            )
 
-    if "oil" not in n.generators.carrier.unique():
+        if "oil" not in n.generators.carrier.unique():
 
-        n.madd("Generator",
-            spatial.oil.nodes,
-            bus=spatial.oil.nodes,
-            p_nom_extendable=True,
-            carrier="oil",
-            marginal_cost=costs.at["oil", 'fuel']
-        )
+            n.madd("Generator",
+                spatial.oil.nodes,
+                bus=spatial.oil.nodes,
+                p_nom_extendable=True,
+                carrier="oil",
+                marginal_cost=costs.at["oil", 'fuel']
+            )
 
     if options["oil_boilers"]:
 

From 985cd8bae118d9dd94da19749f5c283f2afacdce Mon Sep 17 00:00:00 2001
From: Adam-Dvorak1 <92300992+Adam-Dvorak1@users.noreply.github.com>
Date: Fri, 9 Dec 2022 10:37:41 +0100
Subject: [PATCH 225/243] Refine math in myopic.rst

---
 doc/myopic.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/myopic.rst b/doc/myopic.rst
index af67fac7..bd51331d 100644
--- a/doc/myopic.rst
+++ b/doc/myopic.rst
@@ -81,12 +81,12 @@ Conventional carriers indicate carriers used in the existing conventional techno
 Options
 =============
 The total carbon budget for the entire transition path can be indicated in the `sector_opts <https://github.com/PyPSA/pypsa-eur-sec/blob/f13902510010b734c510c38c4cae99356f683058/config.default.yaml#L25>`_ in ``config.yaml``. The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay. 
-E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 GtCO_2 following an exponential decay whose initial linear growth rate $r$ is zero.
+E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 Gt :math:`_{CO_2}` following an exponential decay whose initial linear growth rate r is zero.
 They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
 The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed throughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
 
 .. math::
-  e(t) = e_0 (1+ (r+m)t) e^(-mt)
+  e(t) = e_0 (1+ (r+m)t) e^{-mt}
 
 where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the ``scenario.sector_opts``
 E.g.  ``-cb25.7ex0`` (1.5C increase)

From 3999afdb5a71d69f98563fbebf67d94625c07f93 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 12:19:20 +0100
Subject: [PATCH 226/243] fixes to methanol implementation

---
 config.default.yaml               |  2 +-
 scripts/prepare_sector_network.py | 52 +++++++++++++++----------------
 2 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index ce078fb5..58be20ff 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -198,7 +198,7 @@ sector:
   MWh_MeOH_per_MWh_e: 3.6907 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
   shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
   shipping_hydrogen_share: 0
-  shippint_methanol_share: 1
+  shipping_methanol_share: 1
   shipping_oil_share: 0
   shipping_methanol_efficiency: 0.46 # 10-15% higher https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf, https://users.ugent.be/~lsileghe/documents/extended_abstract.pdf
   shipping_oil_efficiency: 0.40 #For conversion of fuel oil to propulsion in 2011
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 40578a1b..f16c26db 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2202,7 +2202,7 @@ def add_industry(n, costs):
             unit="MWh_LHV"
         )
 
-        n.madd(
+        n.madd("Link",
             spatial.h2.locations + "methanolisation",
             bus0=spatial.h2.nodes,
             bus1=spatial.methanol.nodes,
@@ -2231,7 +2231,7 @@ def add_industry(n, costs):
         # CO2 intensity methanol based on stoichiometric calculation with 22.7 GJ/t methanol (32 g/mol), CO2 (44 g/mol), 277.78 MWh/TJ = 0.218 t/MWh
         co2 = p_set_methanol / options["MWh_MeOH_per_tCO2"]
 
-        n.add("Load"
+        n.add("Load",
             "shipping methanol emissions",
             bus="co2 atmosphere",
             carrier="shipping methanol emissions",
@@ -2259,34 +2259,34 @@ def add_industry(n, costs):
             p_set=-co2
         )
 
-        if "oil" not in n.buses.carrier.unique():
-            n.madd("Bus",
-                spatial.oil.nodes,
-                location=spatial.oil.locations,
-                carrier="oil",
-                unit="MWh_LHV"
-            )
+    if "oil" not in n.buses.carrier.unique():
+        n.madd("Bus",
+            spatial.oil.nodes,
+            location=spatial.oil.locations,
+            carrier="oil",
+            unit="MWh_LHV"
+        )
 
-        if "oil" not in n.stores.carrier.unique():
+    if "oil" not in n.stores.carrier.unique():
 
-            #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
-            n.madd("Store",
-                [oil_bus + " Store" for oil_bus in spatial.oil.nodes],
-                bus=spatial.oil.nodes,
-                e_nom_extendable=True,
-                e_cyclic=True,
-                carrier="oil",
-            )
+        #could correct to e.g. 0.001 EUR/kWh * annuity and O&M
+        n.madd("Store",
+            [oil_bus + " Store" for oil_bus in spatial.oil.nodes],
+            bus=spatial.oil.nodes,
+            e_nom_extendable=True,
+            e_cyclic=True,
+            carrier="oil",
+        )
 
-        if "oil" not in n.generators.carrier.unique():
+    if "oil" not in n.generators.carrier.unique():
 
-            n.madd("Generator",
-                spatial.oil.nodes,
-                bus=spatial.oil.nodes,
-                p_nom_extendable=True,
-                carrier="oil",
-                marginal_cost=costs.at["oil", 'fuel']
-            )
+        n.madd("Generator",
+            spatial.oil.nodes,
+            bus=spatial.oil.nodes,
+            p_nom_extendable=True,
+            carrier="oil",
+            marginal_cost=costs.at["oil", 'fuel']
+        )
 
     if options["oil_boilers"]:
 

From 097d054f0604b62764f01e605153d48824e4cbbc Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 12:20:34 +0100
Subject: [PATCH 227/243] build_temperatur_profiles: parallelize

---
 Snakefile                             | 15 ++++--------
 scripts/build_temperature_profiles.py | 35 ++++++++++++++++-----------
 2 files changed, 26 insertions(+), 24 deletions(-)

diff --git a/Snakefile b/Snakefile
index 61bf072b..2f338813 100644
--- a/Snakefile
+++ b/Snakefile
@@ -177,19 +177,14 @@ rule build_heat_demands:
 
 rule build_temperature_profiles:
     input:
-        pop_layout_total="resources/pop_layout_total.nc",
-        pop_layout_urban="resources/pop_layout_urban.nc",
-        pop_layout_rural="resources/pop_layout_rural.nc",
+        pop_layout="resources/pop_layout_{scope}.nc",
         regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson")
     output:
-        temp_soil_total="resources/temp_soil_total_elec_s{simpl}_{clusters}.nc",
-        temp_soil_rural="resources/temp_soil_rural_elec_s{simpl}_{clusters}.nc",
-        temp_soil_urban="resources/temp_soil_urban_elec_s{simpl}_{clusters}.nc",
-        temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
-        temp_air_rural="resources/temp_air_rural_elec_s{simpl}_{clusters}.nc",
-        temp_air_urban="resources/temp_air_urban_elec_s{simpl}_{clusters}.nc"
+        temp_soil="resources/temp_soil_{scope}_elec_s{simpl}_{clusters}.nc",
+        temp_air="resources/temp_air_{scope}_elec_s{simpl}_{clusters}.nc",
     resources: mem_mb=20000
-    benchmark: "benchmarks/build_temperature_profiles/s{simpl}_{clusters}"
+    threads: 8
+    benchmark: "benchmarks/build_temperature_profiles/{scope}_s{simpl}_{clusters}"
     script: "scripts/build_temperature_profiles.py"
 
 
diff --git a/scripts/build_temperature_profiles.py b/scripts/build_temperature_profiles.py
index ebb9e843..a056fca0 100644
--- a/scripts/build_temperature_profiles.py
+++ b/scripts/build_temperature_profiles.py
@@ -5,6 +5,7 @@ import atlite
 import pandas as pd
 import xarray as xr
 import numpy as np
+from dask.distributed import Client, LocalCluster
 
 if __name__ == '__main__':
     if 'snakemake' not in globals():
@@ -15,6 +16,10 @@ if __name__ == '__main__':
             clusters=48,
         )
 
+    nprocesses = int(snakemake.threads)
+    cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
+    client = Client(cluster, asynchronous=True)
+
     time = pd.date_range(freq='h', **snakemake.config['snapshots'])
     cutout_config = snakemake.config['atlite']['cutout']
     cutout = atlite.Cutout(cutout_config).sel(time=time)
@@ -24,23 +29,25 @@ if __name__ == '__main__':
 
     I = cutout.indicatormatrix(clustered_regions)
 
-    for area in ["total", "rural", "urban"]:
+    pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
 
-        pop_layout = xr.open_dataarray(snakemake.input[f'pop_layout_{area}'])
+    stacked_pop = pop_layout.stack(spatial=('y', 'x'))
+    M = I.T.dot(np.diag(I.dot(stacked_pop)))
 
-        stacked_pop = pop_layout.stack(spatial=('y', 'x'))
-        M = I.T.dot(np.diag(I.dot(stacked_pop)))
+    nonzero_sum = M.sum(axis=0, keepdims=True)
+    nonzero_sum[nonzero_sum == 0.] = 1.
+    M_tilde = M / nonzero_sum
 
-        nonzero_sum = M.sum(axis=0, keepdims=True)
-        nonzero_sum[nonzero_sum == 0.] = 1.
-        M_tilde = M / nonzero_sum
+    temp_air = cutout.temperature(
+        matrix=M_tilde.T, index=clustered_regions.index,
+        dask_kwargs=dict(scheduler=client),
+        show_progress=False)
 
-        temp_air = cutout.temperature(
-            matrix=M_tilde.T, index=clustered_regions.index)
+    temp_air.to_netcdf(snakemake.output.temp_air)
 
-        temp_air.to_netcdf(snakemake.output[f"temp_air_{area}"])
+    temp_soil = cutout.soil_temperature(
+        matrix=M_tilde.T, index=clustered_regions.index,
+        dask_kwargs=dict(scheduler=client),
+        show_progress=False)
 
-        temp_soil = cutout.soil_temperature(
-            matrix=M_tilde.T, index=clustered_regions.index)
-
-        temp_soil.to_netcdf(snakemake.output[f"temp_soil_{area}"])
+    temp_soil.to_netcdf(snakemake.output.temp_soil)

From 494d3010ebb8cb0095cc9d1cf029e9283c3a1a86 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 12:21:00 +0100
Subject: [PATCH 228/243] build_heat_demand: parallelize

---
 Snakefile                    | 11 ++++-------
 scripts/build_heat_demand.py | 28 ++++++++++++----------------
 2 files changed, 16 insertions(+), 23 deletions(-)

diff --git a/Snakefile b/Snakefile
index 2f338813..7ce29b4f 100644
--- a/Snakefile
+++ b/Snakefile
@@ -162,16 +162,13 @@ else:
 
 rule build_heat_demands:
     input:
-        pop_layout_total="resources/pop_layout_total.nc",
-        pop_layout_urban="resources/pop_layout_urban.nc",
-        pop_layout_rural="resources/pop_layout_rural.nc",
+        pop_layout="resources/pop_layout_{scope}.nc",
         regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson")
     output:
-        heat_demand_urban="resources/heat_demand_urban_elec_s{simpl}_{clusters}.nc",
-        heat_demand_rural="resources/heat_demand_rural_elec_s{simpl}_{clusters}.nc",
-        heat_demand_total="resources/heat_demand_total_elec_s{simpl}_{clusters}.nc"
+        heat_demand="resources/heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"
     resources: mem_mb=20000
-    benchmark: "benchmarks/build_heat_demands/s{simpl}_{clusters}"
+    threads: 8
+    benchmark: "benchmarks/build_heat_demands/{scope}_s{simpl}_{clusters}"
     script: "scripts/build_heat_demand.py"
 
 
diff --git a/scripts/build_heat_demand.py b/scripts/build_heat_demand.py
index ed8a10b9..1c49f80d 100644
--- a/scripts/build_heat_demand.py
+++ b/scripts/build_heat_demand.py
@@ -5,6 +5,7 @@ import atlite
 import pandas as pd
 import xarray as xr
 import numpy as np
+from dask.distributed import Client, LocalCluster
 
 if __name__ == '__main__':
     if 'snakemake' not in globals():
@@ -15,14 +16,9 @@ if __name__ == '__main__':
             clusters=48,
         )
 
-    if 'snakemake' not in globals():
-        from vresutils import Dict
-        import yaml
-        snakemake = Dict()
-        with open('config.yaml') as f:
-            snakemake.config = yaml.safe_load(f)
-        snakemake.input = Dict()
-        snakemake.output = Dict()
+    nprocesses = int(snakemake.threads)
+    cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
+    client = Client(cluster, asynchronous=True)
 
     time = pd.date_range(freq='h', **snakemake.config['snapshots'])
     cutout_config = snakemake.config['atlite']['cutout']
@@ -33,14 +29,14 @@ if __name__ == '__main__':
 
     I = cutout.indicatormatrix(clustered_regions)
 
-    for area in ["rural", "urban", "total"]:
+    pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
 
-        pop_layout = xr.open_dataarray(snakemake.input[f'pop_layout_{area}'])
+    stacked_pop = pop_layout.stack(spatial=('y', 'x'))
+    M = I.T.dot(np.diag(I.dot(stacked_pop)))
 
-        stacked_pop = pop_layout.stack(spatial=('y', 'x'))
-        M = I.T.dot(np.diag(I.dot(stacked_pop)))
+    heat_demand = cutout.heat_demand(
+        matrix=M.T, index=clustered_regions.index,
+        dask_kwargs=dict(scheduler=client),
+        show_progress=False)
 
-        heat_demand = cutout.heat_demand(
-            matrix=M.T, index=clustered_regions.index)
-
-        heat_demand.to_netcdf(snakemake.output[f"heat_demand_{area}"])
+    heat_demand.to_netcdf(snakemake.output.heat_demand)

From d37ffddd4fd64c1479026cf860ab207c48b18075 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 12:21:46 +0100
Subject: [PATCH 229/243] build_solar_thermal_profiles: parallelize

---
 Snakefile                               | 11 +++-----
 scripts/build_solar_thermal_profiles.py | 34 +++++++++++--------------
 2 files changed, 19 insertions(+), 26 deletions(-)

diff --git a/Snakefile b/Snakefile
index 7ce29b4f..f22546ba 100644
--- a/Snakefile
+++ b/Snakefile
@@ -207,16 +207,13 @@ rule build_cop_profiles:
 
 rule build_solar_thermal_profiles:
     input:
-        pop_layout_total="resources/pop_layout_total.nc",
-        pop_layout_urban="resources/pop_layout_urban.nc",
-        pop_layout_rural="resources/pop_layout_rural.nc",
+        pop_layout="resources/pop_layout_{scope}.nc",
         regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson")
     output:
-        solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
-        solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
-        solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc"
+        solar_thermal="resources/solar_thermal_{scope}_elec_s{simpl}_{clusters}.nc",
     resources: mem_mb=20000
-    benchmark: "benchmarks/build_solar_thermal_profiles/s{simpl}_{clusters}"
+    threads: 16
+    benchmark: "benchmarks/build_solar_thermal_profiles/{scope}_s{simpl}_{clusters}"
     script: "scripts/build_solar_thermal_profiles.py"
 
 
diff --git a/scripts/build_solar_thermal_profiles.py b/scripts/build_solar_thermal_profiles.py
index f6d05859..0fb8b6e6 100644
--- a/scripts/build_solar_thermal_profiles.py
+++ b/scripts/build_solar_thermal_profiles.py
@@ -5,6 +5,7 @@ import atlite
 import pandas as pd
 import xarray as xr
 import numpy as np
+from dask.distributed import Client, LocalCluster
 
 if __name__ == '__main__':
     if 'snakemake' not in globals():
@@ -15,14 +16,9 @@ if __name__ == '__main__':
             clusters=48,
         )
 
-    if 'snakemake' not in globals():
-        from vresutils import Dict
-        import yaml
-        snakemake = Dict()
-        with open('config.yaml') as f:
-            snakemake.config = yaml.safe_load(f)
-        snakemake.input = Dict()
-        snakemake.output = Dict()
+    nprocesses = int(snakemake.threads)
+    cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
+    client = Client(cluster, asynchronous=True)
 
     config = snakemake.config['solar_thermal']
 
@@ -35,18 +31,18 @@ if __name__ == '__main__':
 
     I = cutout.indicatormatrix(clustered_regions)
 
-    for area in ["total", "rural", "urban"]:
+    pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
 
-        pop_layout = xr.open_dataarray(snakemake.input[f'pop_layout_{area}'])
+    stacked_pop = pop_layout.stack(spatial=('y', 'x'))
+    M = I.T.dot(np.diag(I.dot(stacked_pop)))
 
-        stacked_pop = pop_layout.stack(spatial=('y', 'x'))
-        M = I.T.dot(np.diag(I.dot(stacked_pop)))
+    nonzero_sum = M.sum(axis=0, keepdims=True)
+    nonzero_sum[nonzero_sum == 0.] = 1.
+    M_tilde = M / nonzero_sum
 
-        nonzero_sum = M.sum(axis=0, keepdims=True)
-        nonzero_sum[nonzero_sum == 0.] = 1.
-        M_tilde = M / nonzero_sum
+    solar_thermal = cutout.solar_thermal(**config, matrix=M_tilde.T,
+                                            index=clustered_regions.index,
+                                            dask_kwargs=dict(scheduler=client),
+                                            show_progress=False)
 
-        solar_thermal = cutout.solar_thermal(**config, matrix=M_tilde.T,
-                                             index=clustered_regions.index)
-
-        solar_thermal.to_netcdf(snakemake.output[f"solar_thermal_{area}"])
+    solar_thermal.to_netcdf(snakemake.output.solar_thermal)

From 138d151c8b403999f4f81569e5c464a722a33e3e Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 12:48:51 +0100
Subject: [PATCH 230/243] only compute solar thermal profiles if technology is
 included

---
 Snakefile                         | 6 +++---
 scripts/prepare_sector_network.py | 7 ++++---
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/Snakefile b/Snakefile
index f22546ba..254b1cb1 100644
--- a/Snakefile
+++ b/Snakefile
@@ -500,9 +500,9 @@ rule prepare_sector_network:
         cop_air_total="resources/cop_air_total_elec_s{simpl}_{clusters}.nc",
         cop_air_rural="resources/cop_air_rural_elec_s{simpl}_{clusters}.nc",
         cop_air_urban="resources/cop_air_urban_elec_s{simpl}_{clusters}.nc",
-        solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc",
-        solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc",
-        solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc",
+        solar_thermal_total="resources/solar_thermal_total_elec_s{simpl}_{clusters}.nc" if config["sector"]["solar_thermal"] else [],
+        solar_thermal_urban="resources/solar_thermal_urban_elec_s{simpl}_{clusters}.nc" if config["sector"]["solar_thermal"] else [],
+        solar_thermal_rural="resources/solar_thermal_rural_elec_s{simpl}_{clusters}.nc" if config["sector"]["solar_thermal"] else [],
         **build_retro_cost_output,
         **build_biomass_transport_costs_output,
         **gas_infrastructure
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 560d624e..011eb97e 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1452,9 +1452,10 @@ def add_heat(n, costs):
         "ground": xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
     }
 
-    solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
-    # 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
-    solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
+    if options["solar_thermal"]:
+        solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
+        # 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
+        solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
 
     for name in heat_systems:
 

From 292ec9d5a1210da4652247a35d2b5c31d41ba1ff Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 13:45:42 +0100
Subject: [PATCH 231/243] fix computation of shipping demands

---
 scripts/prepare_sector_network.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index d41bb184..b40c2084 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2148,8 +2148,8 @@ def add_industry(n, costs):
     if total_share != 1:
         logger.warning(f"Total shipping shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
 
-    domestic_navigation = pop_weighted_energy_totals.loc[nodes, "total domestic navigation"]
-    international_navigation = pd.read_csv(snakemake.input.shipping_demand, index_col=0)
+    domestic_navigation = pop_weighted_energy_totals.loc[nodes, "total domestic navigation"].squeeze()
+    international_navigation = pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze()
     all_navigation = domestic_navigation + international_navigation
     p_set = all_navigation * 1e6 / 8760
 
@@ -2204,7 +2204,7 @@ def add_industry(n, costs):
         )
 
         n.madd("Link",
-            spatial.h2.locations + "methanolisation",
+            spatial.h2.locations + " methanolisation",
             bus0=spatial.h2.nodes,
             bus1=spatial.methanol.nodes,
             bus2=nodes,

From c4717854489e82e48e8d490a3de56b3210c13a90 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 13:45:57 +0100
Subject: [PATCH 232/243] add colors for methanolisation

---
 config.default.yaml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/config.default.yaml b/config.default.yaml
index 58be20ff..6875c4ef 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -604,6 +604,9 @@ plotting:
     liquid: '#25c49a'
     kerosene for aviation: '#a1ffe6'
     naphtha for industry: '#57ebc4'
+    methanolisation: '#83d6d5'
+    methanol: '#468c8b'
+    shipping methanol: '#468c8b'
     # co2
     CC: '#f29dae'
     CCS: '#f29dae'
@@ -620,6 +623,7 @@ plotting:
     process emissions to atmosphere: '#888888'
     oil emissions: '#aaaaaa'
     shipping oil emissions: "#555555"
+    shipping methanol emissions: '#666666'
     land transport oil emissions: '#777777'
     agriculture machinery oil emissions: '#333333'
     # other

From b9c1fdb65ebd2cf454562db1e2b456992b4912a8 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 13:46:31 +0100
Subject: [PATCH 233/243] include methanolisation in PtL

---
 scripts/plot_network.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 11cfc84e..caedb335 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -29,7 +29,7 @@ def rename_techs_tyndp(tech):
         return "gas-to-power/heat"
     elif "solar" in tech:
         return "solar"
-    elif tech == "Fischer-Tropsch":
+    elif tech in ["Fischer-Tropsch", "methanolisation"]:
         return "power-to-liquid"
     elif "offshore wind" in tech:
         return "offshore wind"

From 8bab9300ec5197a0cfc36b24a005ce4d7538b24b Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 13:50:55 +0100
Subject: [PATCH 234/243] add release note

---
 doc/release_notes.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index be66a9ba..918f885c 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -78,6 +78,8 @@ incorporates retrofitting options to hydrogen.
   carrier can be nodally resolved or copperplated across Europe. This feature is
   controlled by ``sector: ammonia:``.
 
+* Add methanol as energy carrier, methanolisation as process, and option for methanol demand in shipping sector.
+
 * Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials.
 
 * Updated and extended documentation in <https://pypsa-eur-sec.readthedocs.io/en/latest/>
@@ -89,6 +91,8 @@ incorporates retrofitting options to hydrogen.
 * The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)
   caused a failure to read in the shadow prices of other global constraints.
 
+* Correct capital cost of Fischer-Tropsch according to new units in ``technology-data``.
+
 
 PyPSA-Eur-Sec 0.6.0 (4 October 2021)
 ====================================

From 520cad53a2e90d9af0b871f3360e8ff8df065d71 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 14:11:47 +0100
Subject: [PATCH 235/243] address sum(level) deprecation

---
 scripts/plot_network.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 521da448..257b565f 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -119,8 +119,6 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
 
     tech_colors = snakemake.config['plotting']['tech_colors']
 
-    tech_colors = snakemake.config['plotting']['tech_colors']
-
     n = network.copy()
     assign_location(n)
     # Drop non-electric buses so they don't clutter the plot
@@ -173,7 +171,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
     costs.index = pd.MultiIndex.from_tuples(costs.index.values)
 
     threshold = 100e6 # 100 mEUR/a
-    carriers = costs.sum(level=1)
+    carriers = costs.groupby(level=1).sum()
     carriers = carriers.where(carriers > threshold).dropna()
     carriers = list(carriers.index)
 

From 6f901f4c500ec53c614e6cca5ea79597ccacc33a Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 15:43:43 +0100
Subject: [PATCH 236/243] restore compatibility with latest master version

---
 Snakefile               |   2 +-
 matplotlibrc            |   1 -
 scripts/helper.py       |   2 +
 scripts/plot_network.py | 117 ++++++++++------------------------------
 4 files changed, 30 insertions(+), 92 deletions(-)

diff --git a/Snakefile b/Snakefile
index b21d01e7..77f5fdd6 100644
--- a/Snakefile
+++ b/Snakefile
@@ -517,7 +517,7 @@ rule plot_network:
     input:
         overrides="data/override_component_attrs",
         network=RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
-        regions='../pypsa-eur/resources/regions_onshore_elec_s{simpl}_{clusters}.geojson'
+        regions=pypsaeur('resources/regions_onshore_elec_s{simpl}_{clusters}.geojson')
     output:
         map=RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
         today=RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}-today.pdf"
diff --git a/matplotlibrc b/matplotlibrc
index db5e7ce8..57754c44 100644
--- a/matplotlibrc
+++ b/matplotlibrc
@@ -1,4 +1,3 @@
-backend: Agg
 font.family: sans-serif
 font.sans-serif: Ubuntu, DejaVu Sans
 image.cmap: viridis
\ No newline at end of file
diff --git a/scripts/helper.py b/scripts/helper.py
index 4161c126..e6ddfd4a 100644
--- a/scripts/helper.py
+++ b/scripts/helper.py
@@ -45,7 +45,9 @@ def mock_snakemake(rulename, **wildcards):
     This function is expected to be executed from the 'scripts'-directory of '
     the snakemake project. It returns a snakemake.script.Snakemake object,
     based on the Snakefile.
+
     If a rule has wildcards, you have to specify them in **wildcards.
+
     Parameters
     ----------
     rulename: str
diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 257b565f..386a45b7 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -1,14 +1,11 @@
 import pypsa
 
-import numpy as np
 import pandas as pd
 import geopandas as gpd
 import matplotlib.pyplot as plt
 import cartopy.crs as ccrs
 
-from matplotlib.legend_handler import HandlerPatch
-from matplotlib.patches import Circle, Patch
-from pypsa.plot import projected_area_factor
+from pypsa.plot import add_legend_circles, add_legend_patches, add_legend_lines
 
 from make_summary import assign_carriers
 from plot_summary import rename_techs, preferred_order
@@ -40,69 +37,6 @@ def rename_techs_tyndp(tech):
     else:
         return tech
 
-class HandlerCircle(HandlerPatch):
-    """
-    Legend Handler used to create circles for legend entries. 
-    
-    This handler resizes the circles in order to match the same dimensional 
-    scaling as in the applied axis.
-    """
-    def create_artists(
-        self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans
-    ):
-        fig = legend.get_figure()
-        ax = legend.axes
-
-        unit = np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[0][1]
-        radius = orig_handle.get_radius() * unit * (72 / fig.dpi)
-        center = 5 - xdescent, 3 - ydescent
-        p = plt.Circle(center, radius)
-        self.update_prop(p, orig_handle, legend)
-        p.set_transform(trans)
-        return [p]
-
-
-def add_legend_circles(ax, sizes, labels, scale=1, srid=None, patch_kw={}, legend_kw={}):
-    
-    if srid is not None:
-        area_correction = projected_area_factor(ax, n.srid)**2 
-        sizes = [s * area_correction for s in sizes]
-    
-    handles = make_legend_circles_for(sizes, scale, **patch_kw)
-    
-    legend = ax.legend(
-        handles, labels,
-        handler_map={Circle: HandlerCircle()},
-        **legend_kw
-    )
-
-    ax.add_artist(legend)
-    
-
-def add_legend_lines(ax, sizes, labels, scale=1, patch_kw={}, legend_kw={}):
-    
-    handles = [plt.Line2D([0], [0], linewidth=s/scale, **patch_kw) for s in sizes]
-    
-    legend = ax.legend(
-        handles, labels,
-        **legend_kw
-    )
-    
-    ax.add_artist(legend)
-
-
-def add_legend_patches(ax, colors, labels, patch_kw={}, legend_kw={}):
-    
-    handles = [Patch(facecolor=c, **patch_kw) for c in colors]
-    
-    legend = ax.legend(handles, labels, **legend_kw)
-
-    ax.add_artist(legend)
-
-
-def make_legend_circles_for(sizes, scale=1.0, **kw):
-    return [Circle((0, 0), radius=(s / scale)**0.5, **kw) for s in sizes]
-
 
 def assign_location(n):
     for c in n.iterate_components(n.one_port_components | n.branch_components):
@@ -186,23 +120,23 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         # should be zero
         line_widths = n.lines.s_nom_opt - n.lines.s_nom
         link_widths = n.links.p_nom_opt - n.links.p_nom
-        title = "Added grid"
+        title = "added grid"
 
         if transmission:
             line_widths = n.lines.s_nom_opt
             link_widths = n.links.p_nom_opt
             linewidth_factor = 2e3
             line_lower_threshold = 0.
-            title = "Today's grid"
+            title = "current grid"
     else:
         line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
         link_widths = n.links.p_nom_opt - n.links.p_nom_min
-        title = "Added grid"
+        title = "added grid"
 
         if transmission:
             line_widths = n.lines.s_nom_opt
             link_widths = n.links.p_nom_opt
-            title = "Total grid"
+            title = "total grid"
 
     line_widths[line_widths < line_lower_threshold] = 0.
     link_widths[link_widths < line_lower_threshold] = 0.
@@ -233,7 +167,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         labelspacing=0.8,
         frameon=False,
         handletextpad=0,
-        title='System cost',
+        title='system cost',
     )
 
     add_legend_circles(
@@ -247,6 +181,8 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
 
     sizes = [10, 5]
     labels = [f"{s} GW" for s in sizes]
+    scale = 1e3 / linewidth_factor
+    sizes = [s*scale for s in sizes]
 
     legend_kw = dict(
         loc="upper left",
@@ -261,7 +197,6 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
         ax,
         sizes,
         labels,
-        scale=linewidth_factor/1e3,
         patch_kw=dict(color='lightgrey'),
         legend_kw=legend_kw
     )
@@ -312,8 +247,6 @@ def group_pipes(df, drop_direction=False):
 
 def plot_h2_map(network, regions):
 
-    tech_colors = snakemake.config['plotting']['tech_colors']
-
     n = network.copy()
     if "H2 pipeline" not in n.links.carrier.unique():
         return
@@ -345,9 +278,11 @@ def plot_h2_map(network, regions):
 
     h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
     h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
-    # sum capacitiy for pipelines from different investment periods
-    h2_new = group_pipes(h2_new)
-    h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
+
+    if snakemake.config['foresight'] == 'myopic':
+        # sum capacitiy for pipelines from different investment periods
+        h2_new = group_pipes(h2_new)
+        h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
 
     if not h2_retro.empty:
 
@@ -419,6 +354,7 @@ def plot_h2_map(network, regions):
     )
 
     n.plot(
+        geomap=True,
         bus_sizes=0,
         link_colors=color_retrofit,
         link_widths=link_widths_retro,
@@ -461,8 +397,10 @@ def plot_h2_map(network, regions):
         legend_kw=legend_kw
     )
 
-    sizes = [50, 10]
+    sizes = [30, 10]
     labels = [f"{s} GW" for s in sizes]
+    scale = 1e3 / linewidth_factor
+    sizes = [s*scale for s in sizes]
 
     legend_kw = dict(
         loc="upper left",
@@ -476,7 +414,6 @@ def plot_h2_map(network, regions):
         ax,
         sizes,
         labels,
-        scale=linewidth_factor/1e3,
         patch_kw=dict(color='lightgrey'),
         legend_kw=legend_kw,
     )
@@ -498,7 +435,6 @@ def plot_h2_map(network, regions):
         legend_kw=legend_kw
     )
 
-    plt.gca().outline_patch.set_visible(False)
     ax.set_facecolor("white")
 
     fig.savefig(
@@ -564,7 +500,7 @@ def plot_ch4_map(network):
     pipe_colors = {
         "gas pipeline": "#f08080",
         "gas pipeline new": "#c46868",
-        "gas pipeline (2020)": 'lightgrey',
+        "gas pipeline (in 2020)": 'lightgrey',
         "gas pipeline (available)": '#e8d1d1',
     }
 
@@ -584,7 +520,7 @@ def plot_ch4_map(network):
     n.plot(
         bus_sizes=bus_sizes,
         bus_colors=bus_colors,
-        link_colors=pipe_colors['gas pipeline (2020)'],
+        link_colors=pipe_colors['gas pipeline (in 2020)'],
         link_widths=link_widths_orig,
         branch_components=["Link"],
         ax=ax,
@@ -621,7 +557,7 @@ def plot_ch4_map(network):
         labelspacing=0.8,
         frameon=False,
         handletextpad=1,
-        title='Gas Sources',
+        title='gas sources',
     )
     
     add_legend_circles(
@@ -635,6 +571,8 @@ def plot_ch4_map(network):
 
     sizes = [50, 10]
     labels = [f"{s} GW" for s in sizes]
+    scale = 1e3 / linewidth_factor
+    sizes = [s*scale for s in sizes]
     
     legend_kw = dict(
         loc="upper left",
@@ -642,14 +580,13 @@ def plot_ch4_map(network):
         frameon=False,
         labelspacing=0.8,
         handletextpad=1,
-        title='Gas Pipeline'
+        title='gas pipeline'
     )
     
     add_legend_lines(
         ax,
         sizes,
         labels,
-        scale=linewidth_factor/1e3,
         patch_kw=dict(color='lightgrey'),
         legend_kw=legend_kw,
     )
@@ -740,7 +677,7 @@ def plot_map_without(network):
     for s in (10, 5):
         handles.append(plt.Line2D([0], [0], color=ac_color,
                                   linewidth=s * 1e3 / linewidth_factor))
-        labels.append("{} GW".format(s))
+        labels.append(f"{s} GW")
     l1_1 = ax.legend(handles, labels,
                      loc="upper left", bbox_to_anchor=(0.05, 1.01),
                      frameon=False,
@@ -890,10 +827,10 @@ if __name__ == "__main__":
         snakemake = mock_snakemake(
             'plot_network',
             simpl='',
-            clusters="45",
-            lv=1.0,
+            clusters="181",
+            lv='opt',
             opts='',
-            sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
+            sector_opts='Co2L0-730H-T-H-B-I-A-solar+p3-linemaxext10',
             planning_horizons="2050",
         )
 

From 8edc9042b9db35c04b2253ed5021a609d9d8952a Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Wed, 28 Dec 2022 15:46:11 +0100
Subject: [PATCH 237/243] add release note

---
 doc/release_notes.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index 918f885c..ef92357a 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -86,6 +86,8 @@ incorporates retrofitting options to hydrogen.
 
 * Shipping demand now defaults to (synthetic) oil rather than liquefied hydrogen until 2050.
 
+* Improved network plots including better legends, hydrogen retrofitting network display, and change to EqualEarth projection.
+
 **Bugfixes**
 
 * The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)

From 27414ac0103fd314a519357802f7fcec4e57ecd6 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 29 Dec 2022 11:46:57 +0100
Subject: [PATCH 238/243] new config options for demand in aviation + HVC,
 control of ICE shares in land transport and agriculture

---
 config.default.yaml               | 12 ++++++++--
 doc/release_notes.rst             |  5 ++++
 scripts/prepare_sector_network.py | 39 ++++++++++++++++++++-----------
 3 files changed, 41 insertions(+), 15 deletions(-)

diff --git a/config.default.yaml b/config.default.yaml
index fecf222a..ee1c5059 100644
--- a/config.default.yaml
+++ b/config.default.yaml
@@ -178,19 +178,25 @@ sector:
   bev_avail_mean: 0.8
   v2g: true #allows feed-in to grid from EV battery
   #what is not EV or FCEV is oil-fuelled ICE
-  land_transport_fuel_cell_share:  # 1 means all FCEVs
+  land_transport_fuel_cell_share:
     2020: 0
     2030: 0.05
     2040: 0.1
     2050: 0.15
-  land_transport_electric_share:  # 1 means all EVs
+  land_transport_electric_share:
     2020: 0
     2030: 0.25
     2040: 0.6
     2050: 0.85
+  land_transport_ice_share:
+    2020: 1
+    2030: 0.7
+    2040: 0.3
+    2050: 0
   transport_fuel_cell_efficiency: 0.5
   transport_internal_combustion_efficiency: 0.3
   agriculture_machinery_electric_share: 0
+  agriculture_machinery_oil_share: 1
   agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
   agriculture_machinery_electric_efficiency: 0.3 # electricity per use
   MWh_MeOH_per_MWh_H2: 0.8787 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
@@ -202,6 +208,8 @@ sector:
   shipping_oil_share: 0
   shipping_methanol_efficiency: 0.46 # 10-15% higher https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf, https://users.ugent.be/~lsileghe/documents/extended_abstract.pdf
   shipping_oil_efficiency: 0.40 #For conversion of fuel oil to propulsion in 2011
+  aviation_demand_factor: 1. # relative aviation demand compared to today
+  HVC_demand_factor: 1. # relative HVC demand compared to today
   time_dep_hp_cop: true #time dependent heat pump coefficient of performance
   heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
    # conservatively high to cover hot water and space heating in poorly-insulated buildings
diff --git a/doc/release_notes.rst b/doc/release_notes.rst
index ef92357a..fd9a3549 100644
--- a/doc/release_notes.rst
+++ b/doc/release_notes.rst
@@ -88,6 +88,11 @@ incorporates retrofitting options to hydrogen.
 
 * Improved network plots including better legends, hydrogen retrofitting network display, and change to EqualEarth projection.
 
+* New config options for changing energy demands in aviation
+  (``aviation_demand_factor``) and HVC industry (``HVC_demand_factor``), as well
+  as explicit ICE shares for land transport (``land_transport_ice_share``) and
+  agriculture machinery (``agriculture_machinery_oil_share``).
+
 **Bugfixes**
 
 * The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)
diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index b40c2084..491c44dc 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -1273,13 +1273,15 @@ def add_land_transport(n, costs):
 
     fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
     electric_share = get(options["land_transport_electric_share"], investment_year)
-    ice_share = 1 - fuel_cell_share - electric_share
+    ice_share = get(options["land_transport_ice_share"], investment_year)
+    
+    total_share = fuel_cell_share + electric_share + ice_share
+    if total_share != 1:
+        logger.warning(f"Total land transport shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
 
-    print("FCEV share", fuel_cell_share)
-    print("EV share", electric_share)
-    print("ICEV share", ice_share)
-
-    assert ice_share >= 0, "Error, more FCEV and EV share than 1."
+    logger.info(f"FCEV share: {fuel_cell_share*100}%")
+    logger.info(f"EV share: {electric_share*100}%")
+    logger.info(f"ICEV share: {ice_share*100}%")
 
     nodes = pop_layout.index
 
@@ -2321,15 +2323,23 @@ def add_industry(n, costs):
         lifetime=costs.at['Fischer-Tropsch', 'lifetime']
     )
 
+    demand_factor = options.get("HVC_demand_factor", 1)
+    p_set = demand_factor * industrial_demand.loc[nodes, "naphtha"].sum() / 8760
+    if demand_factor != 1:
+        logger.warning(f"Changing HVC demand by {demand_factor*100-100:+.2f}%.")
+
     n.madd("Load",
         ["naphtha for industry"],
         bus=spatial.oil.nodes,
         carrier="naphtha for industry",
-        p_set=industrial_demand.loc[nodes, "naphtha"].sum() / 8760
+        p_set=p_set
     )
 
+    demand_factor = options.get("aviation_demand_factor", 1)
     all_aviation = ["total international aviation", "total domestic aviation"]
-    p_set = pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
+    p_set = demand_factor * pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
+    if demand_factor != 1:
+        logger.warning(f"Changing aviation demand by {demand_factor*100-100:+.2f}%.")
 
     n.madd("Load",
         ["kerosene for aviation"],
@@ -2480,8 +2490,11 @@ def add_agriculture(n, costs):
     # machinery
 
     electric_share = get(options["agriculture_machinery_electric_share"], investment_year)
-    assert electric_share <= 1.
-    ice_share = 1 - electric_share
+    oil_share = get(options["agriculture_machinery_oil_share"], investment_year)
+
+    total_share = electric_share + oil_share
+    if total_share != 1:
+        logger.warning(f"Total agriculture machinery shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
 
     machinery_nodal_energy = pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"]
 
@@ -2497,16 +2510,16 @@ def add_agriculture(n, costs):
             p_set=electric_share / efficiency_gain * machinery_nodal_energy * 1e6 / 8760,
         )
 
-    if ice_share > 0:
+    if oil_share > 0:
 
         n.madd("Load",
             ["agriculture machinery oil"],
             bus=spatial.oil.nodes,
             carrier="agriculture machinery oil",
-            p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
+            p_set=oil_share * machinery_nodal_energy.sum() * 1e6 / 8760
         )
 
-        co2 = ice_share * machinery_nodal_energy.sum() * 1e6 / 8760 * costs.at["oil", 'CO2 intensity']
+        co2 = oil_share * machinery_nodal_energy.sum() * 1e6 / 8760 * costs.at["oil", 'CO2 intensity']
 
         n.add("Load",
             "agriculture machinery oil emissions",

From 383aef7e1c541e1240ebb0d85636224e6240e226 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Tue, 3 Jan 2023 08:38:10 +0100
Subject: [PATCH 239/243] rename shipping_average_efficiency to
 shipping_oil_efficiency

---
 scripts/prepare_sector_network.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index 491c44dc..e46b2471 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2157,7 +2157,8 @@ def add_industry(n, costs):
 
     if shipping_hydrogen_share:
 
-        efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
+        oil_efficiency = options.get('shipping_oil_efficiency', options.get('shipping_average_efficiency', 0.4))
+        efficiency = oil_efficiency / costs.at["fuel cell", "efficiency"]
         shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
 
         if options["shipping_hydrogen_liquefaction"]:

From ddcaff0b5b7d352392fac34507de3b822c3786b5 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Tue, 3 Jan 2023 13:14:16 +0100
Subject: [PATCH 240/243] plot_network: do not plot very small pipelines

---
 scripts/plot_network.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 386a45b7..5472ee69 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -260,7 +260,7 @@ def plot_h2_map(network, regions):
     bus_size_factor = 1e5
     linewidth_factor = 1e4
     # MW below which not drawn
-    line_lower_threshold = 1e2
+    line_lower_threshold = 1e3
 
     # Drop non-electric buses so they don't clutter the plot
     n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)

From 64969c2deebc888261e838682a0bd38cceb549d2 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Thu, 5 Jan 2023 13:42:01 +0100
Subject: [PATCH 241/243] add methanol store

---
 scripts/prepare_sector_network.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
index e46b2471..b6c052be 100644
--- a/scripts/prepare_sector_network.py
+++ b/scripts/prepare_sector_network.py
@@ -2206,6 +2206,15 @@ def add_industry(n, costs):
             unit="MWh_LHV"
         )
 
+        n.madd("Store",
+            spatial.methanol.nodes,
+            suffix=" Store",
+            bus=spatial.methanol.nodes,
+            e_nom_extendable=True,
+            e_cyclic=True,
+            carrier="methanol",
+        )
+
         n.madd("Link",
             spatial.h2.locations + " methanolisation",
             bus0=spatial.h2.nodes,

From 433514b1687b1f4e0acdb34de95922c93a54781e Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sat, 7 Jan 2023 13:23:12 +0100
Subject: [PATCH 242/243] add a collection rule plot_all_networks

---
 Snakefile | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/Snakefile b/Snakefile
index 77f5fdd6..90fcfb56 100644
--- a/Snakefile
+++ b/Snakefile
@@ -44,6 +44,13 @@ rule prepare_sector_networks:
         expand(RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
                **config['scenario'])
 
+
+rule plot_all_networks:
+    input:
+        expand(RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
+               **config['scenario'])
+
+
 datafiles = [
     "data/eea/UNFCCC_v23.csv",
     "data/switzerland-sfoe/switzerland-new_format.csv",
@@ -245,9 +252,9 @@ rule build_biomass_potentials:
         enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
         nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
         regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
-        nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
-        swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
-        swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
+        nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
+        swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
+        swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
         country_shapes=pypsaeur('resources/country_shapes.geojson')
     output:
         biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',
@@ -602,7 +609,7 @@ if config["foresight"] == "overnight":
             network=RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
             costs=CDIR + "costs_{}.csv".format(config['costs']['year']),
             config=SDIR + '/configs/config.yaml',
-            env=SDIR + '/configs/environment.yaml',
+            #env=SDIR + '/configs/environment.yaml',
         output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
         shadow: "shallow"
         log:

From 8bafaf25f762bde0b16a35d8ff53b462030d9836 Mon Sep 17 00:00:00 2001
From: Fabian Neumann <fabian.neumann@outlook.de>
Date: Sat, 7 Jan 2023 13:23:33 +0100
Subject: [PATCH 243/243] plot_network: adjust limits and thresholds

---
 scripts/plot_network.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/scripts/plot_network.py b/scripts/plot_network.py
index 5472ee69..53c52187 100644
--- a/scripts/plot_network.py
+++ b/scripts/plot_network.py
@@ -258,9 +258,9 @@ def plot_h2_map(network, regions):
     regions["H2"] = regions["H2"].where(regions["H2"] > 0.1)
 
     bus_size_factor = 1e5
-    linewidth_factor = 1e4
+    linewidth_factor = 7e3
     # MW below which not drawn
-    line_lower_threshold = 1e3
+    line_lower_threshold = 750
 
     # Drop non-electric buses so they don't clutter the plot
     n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
@@ -370,7 +370,7 @@ def plot_h2_map(network, regions):
         cmap='Blues',
         linewidths=0,
         legend=True,
-        vmax=10,
+        vmax=6,
         vmin=0,
         legend_kwds={
             "label": "Hydrogen Storage [TWh]",
@@ -455,7 +455,7 @@ def plot_ch4_map(network):
     bus_size_factor = 8e7
     linewidth_factor = 1e4
     # MW below which not drawn
-    line_lower_threshold = 500
+    line_lower_threshold = 1e3
 
     # Drop non-electric buses so they don't clutter the plot
     n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)