955 lines
27 KiB
Python
955 lines
27 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Creates plots for optimised network topologies, including electricity, gas and
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hydrogen networks, and regional generation, storage and conversion capacities
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built.
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This rule plots a map of the network with technology capacities at the
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nodes.
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"""
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import logging
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logger = logging.getLogger(__name__)
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import cartopy.crs as ccrs
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import geopandas as gpd
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import matplotlib.pyplot as plt
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import pandas as pd
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import pypsa
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from make_summary import assign_carriers
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from plot_summary import preferred_order, rename_techs
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from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
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plt.style.use(["ggplot", "matplotlibrc"])
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def rename_techs_tyndp(tech):
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tech = rename_techs(tech)
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if "heat pump" in tech or "resistive heater" in tech:
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return "power-to-heat"
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elif tech in ["H2 Electrolysis", "methanation", "helmeth", "H2 liquefaction"]:
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return "power-to-gas"
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elif tech == "H2":
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return "H2 storage"
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elif tech in ["NH3", "Haber-Bosch", "ammonia cracker", "ammonia store"]:
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return "ammonia"
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elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
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return "gas-to-power/heat"
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# elif "solar" in tech:
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# return "solar"
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elif tech in ["Fischer-Tropsch", "methanolisation"]:
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return "power-to-liquid"
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elif "offshore wind" in tech:
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return "offshore wind"
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elif "CC" in tech or "sequestration" in tech:
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return "CCS"
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else:
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return tech
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def assign_location(n):
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for c in n.iterate_components(n.one_port_components | n.branch_components):
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ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
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for i in ifind.value_counts().index:
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# these have already been assigned defaults
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if i == -1:
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continue
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names = ifind.index[ifind == i]
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c.df.loc[names, "location"] = names.str[:i]
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def plot_map(
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network,
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components=["links", "stores", "storage_units", "generators"],
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bus_size_factor=1.7e10,
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transmission=False,
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with_legend=True,
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):
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tech_colors = snakemake.params.plotting["tech_colors"]
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n = network.copy()
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assign_location(n)
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# Drop non-electric buses so they don't clutter the plot
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n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
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costs = pd.DataFrame(index=n.buses.index)
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for comp in components:
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df_c = getattr(n, comp)
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if df_c.empty:
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continue
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df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
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attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
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costs_c = (
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(df_c.capital_cost * df_c[attr])
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.groupby([df_c.location, df_c.nice_group])
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.sum()
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.unstack()
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.fillna(0.0)
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)
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costs = pd.concat([costs, costs_c], axis=1)
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logger.debug(f"{comp}, {costs}")
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costs = costs.groupby(costs.columns, axis=1).sum()
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costs.drop(list(costs.columns[(costs == 0.0).all()]), axis=1, inplace=True)
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new_columns = preferred_order.intersection(costs.columns).append(
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costs.columns.difference(preferred_order)
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)
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costs = costs[new_columns]
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for item in new_columns:
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if item not in tech_colors:
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logger.warning(f"{item} not in config/plotting/tech_colors")
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costs = costs.stack() # .sort_index()
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# hack because impossible to drop buses...
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eu_location = snakemake.params.plotting.get("eu_node_location", dict(x=-5.5, y=46))
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n.buses.loc["EU gas", "x"] = eu_location["x"]
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n.buses.loc["EU gas", "y"] = eu_location["y"]
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n.links.drop(
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n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")],
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inplace=True,
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)
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# drop non-bus
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to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
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if len(to_drop) != 0:
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logger.info(f"Dropping non-buses {to_drop.tolist()}")
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costs.drop(to_drop, level=0, inplace=True, axis=0, errors="ignore")
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# make sure they are removed from index
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costs.index = pd.MultiIndex.from_tuples(costs.index.values)
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threshold = 100e6 # 100 mEUR/a
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carriers = costs.groupby(level=1).sum()
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carriers = carriers.where(carriers > threshold).dropna()
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carriers = list(carriers.index)
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# PDF has minimum width, so set these to zero
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line_lower_threshold = 500.0
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line_upper_threshold = 1e4
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linewidth_factor = 4e3
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ac_color = "rosybrown"
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dc_color = "darkseagreen"
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if snakemake.wildcards["ll"] == "v1.0":
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# should be zero
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line_widths = n.lines.s_nom_opt - n.lines.s_nom
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link_widths = n.links.p_nom_opt - n.links.p_nom
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title = "added grid"
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if transmission:
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line_widths = n.lines.s_nom_opt
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link_widths = n.links.p_nom_opt
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linewidth_factor = 2e3
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line_lower_threshold = 0.0
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title = "current grid"
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else:
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line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
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link_widths = n.links.p_nom_opt - n.links.p_nom_min
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title = "added grid"
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if transmission:
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line_widths = n.lines.s_nom_opt
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link_widths = n.links.p_nom_opt
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title = "total grid"
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line_widths = line_widths.clip(line_lower_threshold, line_upper_threshold)
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link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)
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line_widths = line_widths.replace(line_lower_threshold, 0)
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link_widths = link_widths.replace(line_lower_threshold, 0)
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fig, ax = plt.subplots(subplot_kw={"projection": ccrs.EqualEarth()})
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fig.set_size_inches(7, 6)
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n.plot(
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bus_sizes=costs / bus_size_factor,
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bus_colors=tech_colors,
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line_colors=ac_color,
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link_colors=dc_color,
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line_widths=line_widths / linewidth_factor,
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link_widths=link_widths / linewidth_factor,
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ax=ax,
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**map_opts,
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)
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sizes = [20, 10, 5]
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labels = [f"{s} bEUR/a" for s in sizes]
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sizes = [s / bus_size_factor * 1e9 for s in sizes]
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legend_kw = dict(
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loc="upper left",
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bbox_to_anchor=(0.01, 1.06),
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labelspacing=0.8,
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frameon=False,
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handletextpad=0,
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title="system cost",
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)
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add_legend_circles(
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ax,
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sizes,
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labels,
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srid=n.srid,
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patch_kw=dict(facecolor="lightgrey"),
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legend_kw=legend_kw,
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)
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sizes = [10, 5]
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labels = [f"{s} GW" for s in sizes]
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scale = 1e3 / linewidth_factor
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sizes = [s * scale for s in sizes]
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legend_kw = dict(
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loc="upper left",
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bbox_to_anchor=(0.27, 1.06),
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frameon=False,
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labelspacing=0.8,
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handletextpad=1,
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title=title,
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)
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add_legend_lines(
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ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
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)
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legend_kw = dict(
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bbox_to_anchor=(1.52, 1.04),
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frameon=False,
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)
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if with_legend:
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colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
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labels = carriers + ["HVAC line", "HVDC link"]
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add_legend_patches(
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ax,
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colors,
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labels,
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legend_kw=legend_kw,
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)
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fig.savefig(snakemake.output.map, transparent=True, bbox_inches="tight")
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def group_pipes(df, drop_direction=False):
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"""
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Group pipes which connect same buses and return overall capacity.
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"""
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if drop_direction:
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positive_order = df.bus0 < df.bus1
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df_p = df[positive_order]
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swap_buses = {"bus0": "bus1", "bus1": "bus0"}
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df_n = df[~positive_order].rename(columns=swap_buses)
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df = pd.concat([df_p, df_n])
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# there are pipes for each investment period rename to AC buses name for plotting
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df.index = df.apply(
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lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
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axis=1,
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)
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# group pipe lines connecting the same buses and rename them for plotting
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pipe_capacity = df.groupby(level=0).agg(
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{"p_nom_opt": sum, "bus0": "first", "bus1": "first"}
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)
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return pipe_capacity
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def plot_h2_map(network, regions):
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n = network.copy()
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if "H2 pipeline" not in n.links.carrier.unique():
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return
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assign_location(n)
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h2_storage = n.stores.query("carrier == 'H2'")
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regions["H2"] = h2_storage.rename(
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index=h2_storage.bus.map(n.buses.location)
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).e_nom_opt.div(
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1e6
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) # TWh
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regions["H2"] = regions["H2"].where(regions["H2"] > 0.1)
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bus_size_factor = 1e5
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linewidth_factor = 7e3
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# MW below which not drawn
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line_lower_threshold = 750
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# Drop non-electric buses so they don't clutter the plot
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n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
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carriers = ["H2 Electrolysis", "H2 Fuel Cell"]
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elec = n.links[n.links.carrier.isin(carriers)].index
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bus_sizes = (
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n.links.loc[elec, "p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum()
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/ bus_size_factor
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)
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# make a fake MultiIndex so that area is correct for legend
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bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
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# drop all links which are not H2 pipelines
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n.links.drop(
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n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True
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)
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h2_new = n.links[n.links.carrier == "H2 pipeline"]
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h2_retro = n.links[n.links.carrier == "H2 pipeline retrofitted"]
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if snakemake.params.foresight == "myopic":
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# sum capacitiy for pipelines from different investment periods
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h2_new = group_pipes(h2_new)
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if not h2_retro.empty:
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h2_retro = (
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group_pipes(h2_retro, drop_direction=True)
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.reindex(h2_new.index)
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.fillna(0)
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)
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if not h2_retro.empty:
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positive_order = h2_retro.bus0 < h2_retro.bus1
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h2_retro_p = h2_retro[positive_order]
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swap_buses = {"bus0": "bus1", "bus1": "bus0"}
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h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
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h2_retro = pd.concat([h2_retro_p, h2_retro_n])
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h2_retro["index_orig"] = h2_retro.index
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h2_retro.index = h2_retro.apply(
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lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
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axis=1,
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)
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retro_w_new_i = h2_retro.index.intersection(h2_new.index)
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h2_retro_w_new = h2_retro.loc[retro_w_new_i]
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retro_wo_new_i = h2_retro.index.difference(h2_new.index)
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h2_retro_wo_new = h2_retro.loc[retro_wo_new_i]
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h2_retro_wo_new.index = h2_retro_wo_new.index_orig
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to_concat = [h2_new, h2_retro_w_new, h2_retro_wo_new]
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h2_total = pd.concat(to_concat).p_nom_opt.groupby(level=0).sum()
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else:
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h2_total = h2_new.p_nom_opt
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link_widths_total = h2_total / linewidth_factor
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n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
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n.links = n.links.groupby(level=0).first()
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link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.0)
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link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.0
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retro = n.links.p_nom_opt.where(
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n.links.carrier == "H2 pipeline retrofitted", other=0.0
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)
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link_widths_retro = retro / linewidth_factor
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link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.0
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n.links.bus0 = n.links.bus0.str.replace(" H2", "")
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n.links.bus1 = n.links.bus1.str.replace(" H2", "")
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proj = ccrs.EqualEarth()
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regions = regions.to_crs(proj.proj4_init)
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fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": proj})
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color_h2_pipe = "#b3f3f4"
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color_retrofit = "#499a9c"
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bus_colors = {"H2 Electrolysis": "#ff29d9", "H2 Fuel Cell": "#805394"}
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n.plot(
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geomap=True,
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bus_sizes=bus_sizes,
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bus_colors=bus_colors,
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link_colors=color_h2_pipe,
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link_widths=link_widths_total,
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branch_components=["Link"],
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ax=ax,
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**map_opts,
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)
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n.plot(
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geomap=True,
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bus_sizes=0,
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link_colors=color_retrofit,
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link_widths=link_widths_retro,
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branch_components=["Link"],
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ax=ax,
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**map_opts,
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)
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regions.plot(
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ax=ax,
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column="H2",
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cmap="Blues",
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linewidths=0,
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legend=True,
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vmax=6,
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vmin=0,
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legend_kwds={
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"label": "Hydrogen Storage [TWh]",
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"shrink": 0.7,
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"extend": "max",
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},
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)
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sizes = [50, 10]
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labels = [f"{s} GW" for s in sizes]
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sizes = [s / bus_size_factor * 1e3 for s in sizes]
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legend_kw = dict(
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loc="upper left",
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bbox_to_anchor=(0, 1),
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labelspacing=0.8,
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handletextpad=0,
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frameon=False,
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)
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add_legend_circles(
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ax,
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sizes,
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labels,
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srid=n.srid,
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patch_kw=dict(facecolor="lightgrey"),
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legend_kw=legend_kw,
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)
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sizes = [30, 10]
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labels = [f"{s} GW" for s in sizes]
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scale = 1e3 / linewidth_factor
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sizes = [s * scale for s in sizes]
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legend_kw = dict(
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loc="upper left",
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bbox_to_anchor=(0.23, 1),
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frameon=False,
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labelspacing=0.8,
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handletextpad=1,
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)
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add_legend_lines(
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ax,
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sizes,
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labels,
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patch_kw=dict(color="lightgrey"),
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legend_kw=legend_kw,
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)
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colors = [bus_colors[c] for c in carriers] + [color_h2_pipe, color_retrofit]
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labels = carriers + ["H2 pipeline (total)", "H2 pipeline (repurposed)"]
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legend_kw = dict(
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loc="upper left",
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bbox_to_anchor=(0, 1.13),
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ncol=2,
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frameon=False,
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)
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add_legend_patches(ax, colors, labels, legend_kw=legend_kw)
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ax.set_facecolor("white")
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fig.savefig(
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snakemake.output.map.replace("-costs-all", "-h2_network"), bbox_inches="tight"
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)
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def plot_ch4_map(network):
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n = network.copy()
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if "gas pipeline" not in n.links.carrier.unique():
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return
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assign_location(n)
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bus_size_factor = 8e7
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linewidth_factor = 1e4
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# MW below which not drawn
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line_lower_threshold = 1e3
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# Drop non-electric buses so they don't clutter the plot
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n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
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fossil_gas_i = n.generators[n.generators.carrier == "gas"].index
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fossil_gas = (
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n.generators_t.p.loc[:, fossil_gas_i]
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.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] = 0.0
|
|
|
|
link_widths_orig = n.links.p_nom / linewidth_factor
|
|
link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.0
|
|
|
|
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] = 0.0
|
|
|
|
tech_colors = snakemake.params.plotting["tech_colors"]
|
|
|
|
pipe_colors = {
|
|
"gas pipeline": "#f08080",
|
|
"gas pipeline new": "#c46868",
|
|
"gas pipeline (in 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", "")
|
|
|
|
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.EqualEarth()})
|
|
|
|
n.plot(
|
|
bus_sizes=bus_sizes,
|
|
bus_colors=bus_colors,
|
|
link_colors=pipe_colors["gas pipeline (in 2020)"],
|
|
link_widths=link_widths_orig,
|
|
branch_components=["Link"],
|
|
ax=ax,
|
|
**map_opts,
|
|
)
|
|
|
|
n.plot(
|
|
ax=ax,
|
|
bus_sizes=0.0,
|
|
link_colors=pipe_colors["gas pipeline (available)"],
|
|
link_widths=link_widths_rem,
|
|
branch_components=["Link"],
|
|
color_geomap=False,
|
|
boundaries=map_opts["boundaries"],
|
|
)
|
|
|
|
n.plot(
|
|
ax=ax,
|
|
bus_sizes=0.0,
|
|
link_colors=link_color_used,
|
|
link_widths=link_widths_used,
|
|
branch_components=["Link"],
|
|
color_geomap=False,
|
|
boundaries=map_opts["boundaries"],
|
|
)
|
|
|
|
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",
|
|
bbox_to_anchor=(0, 1.03),
|
|
labelspacing=0.8,
|
|
frameon=False,
|
|
handletextpad=1,
|
|
title="gas sources",
|
|
)
|
|
|
|
add_legend_circles(
|
|
ax,
|
|
sizes,
|
|
labels,
|
|
srid=n.srid,
|
|
patch_kw=dict(facecolor="lightgrey"),
|
|
legend_kw=legend_kw,
|
|
)
|
|
|
|
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",
|
|
bbox_to_anchor=(0.25, 1.03),
|
|
frameon=False,
|
|
labelspacing=0.8,
|
|
handletextpad=1,
|
|
title="gas pipeline",
|
|
)
|
|
|
|
add_legend_lines(
|
|
ax,
|
|
sizes,
|
|
labels,
|
|
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"), 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.EqualEarth()})
|
|
|
|
# PDF has minimum width, so set these to zero
|
|
line_lower_threshold = 200.0
|
|
line_upper_threshold = 1e4
|
|
linewidth_factor = 3e3
|
|
ac_color = "rosybrown"
|
|
dc_color = "darkseagreen"
|
|
|
|
# hack because impossible to drop buses...
|
|
if "EU gas" in n.buses.index:
|
|
eu_location = snakemake.params.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["ll"] == "v1.0":
|
|
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.clip(line_lower_threshold, line_upper_threshold)
|
|
link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)
|
|
|
|
line_widths = line_widths.replace(line_lower_threshold, 0)
|
|
link_widths = link_widths.replace(line_lower_threshold, 0)
|
|
|
|
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(f"{s} GW")
|
|
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 < 0.0).any() & (supply > 0.0).any()]
|
|
|
|
positive_supply = supply[both]
|
|
negative_supply = supply[both]
|
|
|
|
positive_supply[positive_supply < 0.0] = 0.0
|
|
negative_supply[negative_supply > 0.0] = 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:
|
|
logger.info(f"Dropping {to_drop.tolist()} from supply")
|
|
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=0.0,
|
|
color=[
|
|
snakemake.params.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(
|
|
"{}/{RDIR}maps/series-{}-{}-{}-{}-{}.pdf".format(
|
|
"results",
|
|
snakemake.params.RDIR,
|
|
snakemake.wildcards["ll"],
|
|
carrier,
|
|
start,
|
|
stop,
|
|
name,
|
|
),
|
|
transparent=True,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
if "snakemake" not in globals():
|
|
from _helpers import mock_snakemake
|
|
|
|
snakemake = mock_snakemake(
|
|
"plot_network",
|
|
weather_year="",
|
|
simpl="",
|
|
opts="",
|
|
clusters="5",
|
|
ll="v1.5",
|
|
sector_opts="CO2L0-1H-T-H-B-I-A-solar+p3-dist1",
|
|
planning_horizons="2030",
|
|
)
|
|
|
|
logging.basicConfig(level=snakemake.config["logging"]["level"])
|
|
|
|
n = pypsa.Network(snakemake.input.network)
|
|
|
|
regions = gpd.read_file(snakemake.input.regions).set_index("name")
|
|
|
|
map_opts = snakemake.params.plotting["map"]
|
|
|
|
if map_opts["boundaries"] is None:
|
|
map_opts["boundaries"] = regions.total_bounds[[0, 2, 1, 3]] + [-1, 1, -1, 1]
|
|
|
|
plot_map(
|
|
n,
|
|
components=["generators", "links", "stores", "storage_units"],
|
|
bus_size_factor=2e10,
|
|
transmission=False,
|
|
)
|
|
|
|
plot_h2_map(n, regions)
|
|
plot_ch4_map(n)
|
|
plot_map_without(n)
|
|
|
|
# plot_series(n, carrier="AC", name=suffix)
|
|
# plot_series(n, carrier="heat", name=suffix)
|