# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Creates plots for optimised network topologies, including electricity, gas and
hydrogen networks, and regional generation, storage and conversion capacities
built.
This rule plots a map of the network with technology capacities at the
nodes.
"""
import logging
logger = logging.getLogger(__name__)
import cartopy.crs as ccrs
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
from make_summary import assign_carriers
from plot_summary import preferred_order, rename_techs
from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
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", "H2 liquefaction"]:
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:
# return "solar"
elif tech in ["Fischer-Tropsch", "methanolisation"]:
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 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,
with_legend=True,
):
tech_colors = snakemake.params.plotting["tech_colors"]
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)
if df_c.empty:
continue
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(0.0)
)
costs = pd.concat([costs, costs_c], axis=1)
logger.debug(f"{comp}, {costs}")
costs = costs.groupby(costs.columns, axis=1).sum()
costs.drop(list(costs.columns[(costs == 0.0).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 tech_colors:
logger.warning(f"{item} not in config/plotting/tech_colors")
costs = costs.stack() # .sort_index()
# hack because impossible to drop buses...
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"]
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:
logger.info(f"Dropping non-buses {to_drop.tolist()}")
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)
threshold = 100e6 # 100 mEUR/a
carriers = costs.groupby(level=1).sum()
carriers = carriers.where(carriers > threshold).dropna()
carriers = list(carriers.index)
# PDF has minimum width, so set these to zero
line_lower_threshold = 500.0
line_upper_threshold = 1e4
linewidth_factor = 4e3
ac_color = "rosybrown"
dc_color = "darkseagreen"
title = "added grid"
if snakemake.wildcards["ll"] == "v1.0":
# 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
if transmission:
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
linewidth_factor = 2e3
line_lower_threshold = 0.0
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
if transmission:
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
title = "total grid"
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)
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=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,
)
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",
bbox_to_anchor=(0.01, 1.06),
labelspacing=0.8,
frameon=False,
handletextpad=0,
title="system cost",
)
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
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",
bbox_to_anchor=(0.27, 1.06),
frameon=False,
labelspacing=0.8,
handletextpad=1,
title=title,
)
add_legend_lines(
ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
)
legend_kw = dict(
bbox_to_anchor=(1.52, 1.04),
frameon=False,
)
if with_legend:
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, 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,
)
return df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
def plot_h2_map(network, regions):
n = network.copy()
if "H2 pipeline" not in n.links.carrier.unique():
return
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 = 7e3
# MW below which not drawn
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)
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
)
# 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[n.links.carrier == "H2 pipeline"]
h2_retro = n.links[n.links.carrier == "H2 pipeline retrofitted"]
if snakemake.params.foresight == "myopic":
# sum capacitiy for pipelines from different investment periods
h2_new = group_pipes(h2_new)
if not h2_retro.empty:
h2_retro = (
group_pipes(h2_retro, drop_direction=True)
.reindex(h2_new.index)
.fillna(0)
)
if not h2_retro.empty:
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["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,
)
retro_w_new_i = h2_retro.index.intersection(h2_new.index)
h2_retro_w_new = h2_retro.loc[retro_w_new_i]
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.p_nom_opt
link_widths_total = h2_total / linewidth_factor
n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
n.links = n.links.groupby(level=0).first()
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.0)
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.0
retro = n.links.p_nom_opt.where(
n.links.carrier == "H2 pipeline retrofitted", other=0.0
)
link_widths_retro = retro / linewidth_factor
link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.0
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": proj})
color_h2_pipe = "#b3f3f4"
color_retrofit = "#499a9c"
bus_colors = {"H2 Electrolysis": "#ff29d9", "H2 Fuel Cell": "#805394"}
n.plot(
geomap=True,
bus_sizes=bus_sizes,
bus_colors=bus_colors,
link_colors=color_h2_pipe,
link_widths=link_widths_total,
branch_components=["Link"],
ax=ax,
**map_opts,
)
n.plot(
geomap=True,
bus_sizes=0,
link_colors=color_retrofit,
link_widths=link_widths_retro,
branch_components=["Link"],
ax=ax,
**map_opts,
)
regions.plot(
ax=ax,
column="H2",
cmap="Blues",
linewidths=0,
legend=True,
vmax=6,
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",
bbox_to_anchor=(0, 1),
labelspacing=0.8,
handletextpad=0,
frameon=False,
)
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
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",
bbox_to_anchor=(0.23, 1),
frameon=False,
labelspacing=0.8,
handletextpad=1,
)
add_legend_lines(
ax,
sizes,
labels,
patch_kw=dict(color="lightgrey"),
legend_kw=legend_kw,
)
colors = [bus_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)
ax.set_facecolor("white")
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 = 1e3
# 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.query("carrier == '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[f"p{str(i)}"]
.loc[:, c.df.index[c.df[f"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,
)
def plot_map_perfect(
network,
components=["Link", "Store", "StorageUnit", "Generator"],
bus_size_factor=1.7e10,
):
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)
# investment periods
investments = n.snapshots.levels[0]
costs = {}
for comp in components:
df_c = n.df(comp)
if df_c.empty:
continue
df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
attr = "e_nom_opt" if comp == "Store" else "p_nom_opt"
active = pd.concat(
[n.get_active_assets(comp, inv_p).rename(inv_p) for inv_p in investments],
axis=1,
).astype(int)
capital_cost = n.df(comp)[attr] * n.df(comp).capital_cost
capital_cost_t = (
(active.mul(capital_cost, axis=0))
.groupby([n.df(comp).location, n.df(comp).nice_group])
.sum()
)
capital_cost_t.drop("load", level=1, inplace=True, errors="ignore")
costs[comp] = capital_cost_t
costs = pd.concat(costs).groupby(level=[1, 2]).sum()
costs.drop(costs[costs.sum(axis=1) == 0].index, inplace=True)
new_columns = preferred_order.intersection(costs.index.levels[1]).append(
costs.index.levels[1].difference(preferred_order)
)
costs = costs.reindex(new_columns, level=1)
for item in new_columns:
if item not in snakemake.config["plotting"]["tech_colors"]:
print(
"Warning!",
item,
"not in config/plotting/tech_colors, assign random color",
)
snakemake.config["plotting"]["tech_colors"] = "pink"
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 = 500.0
line_upper_threshold = 1e4
linewidth_factor = 2e3
ac_color = "gray"
dc_color = "m"
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
linewidth_factor = 2e3
line_lower_threshold = 0.0
title = "Today's transmission"
line_widths[line_widths < line_lower_threshold] = 0.0
link_widths[link_widths < line_lower_threshold] = 0.0
line_widths[line_widths > line_upper_threshold] = line_upper_threshold
link_widths[link_widths > line_upper_threshold] = line_upper_threshold
for year in costs.columns:
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
fig.set_size_inches(7, 6)
fig.suptitle(year)
n.plot(
bus_sizes=costs[year] / 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,
)
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",
bbox_to_anchor=(0.01, 1.06),
labelspacing=0.8,
frameon=False,
handletextpad=0,
title="system cost",
)
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
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",
bbox_to_anchor=(0.27, 1.06),
frameon=False,
labelspacing=0.8,
handletextpad=1,
title=title,
)
add_legend_lines(
ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
)
legend_kw = dict(
bbox_to_anchor=(1.52, 1.04),
frameon=False,
)
fig.savefig(
snakemake.output[f"map_{year}"], transparent=True, bbox_inches="tight"
)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake(
"plot_network",
simpl="",
opts="",
clusters="37",
ll="v1.0",
sector_opts="4380H-T-H-B-I-A-solar+p3-dist1",
)
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]
if snakemake.params["foresight"] == "perfect":
plot_map_perfect(
n,
components=["Link", "Store", "StorageUnit", "Generator"],
bus_size_factor=2e10,
)
else:
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)