add network plot for perfect
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lisazeyen
parent
f2b01fbbe0
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be23b5c56a
@ -44,16 +44,18 @@ if config["foresight"] == "perfect":
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+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
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regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
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output:
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map=RESULTS
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+ "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{year}.pdf",
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today=RESULTS
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+ "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{year}-today.pdf",
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**{
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f"map_{year}": RESULTS
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+ "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_"
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+ f"{year}.pdf"
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for year in config["scenario"]["planning_horizons"]
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},
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threads: 2
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resources:
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mem_mb=10000,
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benchmark:
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BENCHMARKS
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+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{year}_brownfield_all_years_benchmark.txt",
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+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years_benchmark",
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conda:
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"../envs/environment.yaml"
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script:
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@ -24,7 +24,7 @@ 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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plt.style.use(["ggplot"])
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def rename_techs_tyndp(tech):
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@ -913,6 +913,153 @@ def plot_series(network, carrier="AC", name="test"):
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)
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def plot_map_perfect(network, components=["Link", "Store", "StorageUnit", "Generator"],
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bus_size_factor=1.7e10):
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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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# investment periods
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investments = n.snapshots.levels[0]
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costs = {}
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for comp in components:
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df_c = n.df(comp)
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if df_c.empty: 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 == "Store" else "p_nom_opt"
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active = pd.concat(
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[
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n.get_active_assets(comp, inv_p).rename(inv_p)
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for inv_p in investments
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],
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axis=1,
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).astype(int)
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capital_cost = n.df(comp)[attr] * n.df(comp).capital_cost
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capital_cost_t = ((active.mul(capital_cost, axis=0))
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.groupby([n.df(comp).location,
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n.df(comp).nice_group]).sum())
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capital_cost_t.drop("load", level=1, inplace=True, errors="ignore")
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costs[comp] = capital_cost_t
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costs = pd.concat(costs).groupby(level=[1,2]).sum()
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costs.drop(costs[costs.sum(axis=1)==0].index, inplace=True)
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new_columns = (preferred_order.intersection(costs.index.levels[1])
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.append(costs.index.levels[1].difference(preferred_order)))
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costs = costs.reindex(new_columns, level=1)
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for item in new_columns:
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if item not in snakemake.config['plotting']['tech_colors']:
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print("Warning!",item,"not in config/plotting/tech_colors, assign random color")
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snakemake.config['plotting']['tech_colors'] = "pink"
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n.links.drop(n.links.index[(n.links.carrier != "DC") & (
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n.links.carrier != "B2B")], inplace=True)
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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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print("dropping non-buses", to_drop)
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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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# PDF has minimum width, so set these to zero
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line_lower_threshold = 500.
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line_upper_threshold = 1e4
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linewidth_factor = 2e3
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ac_color = "gray"
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dc_color = "m"
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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.
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title = "Today's transmission"
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line_widths[line_widths < line_lower_threshold] = 0.
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link_widths[link_widths < line_lower_threshold] = 0.
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line_widths[line_widths > line_upper_threshold] = line_upper_threshold
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link_widths[link_widths > line_upper_threshold] = line_upper_threshold
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for year in costs.columns:
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fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
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fig.set_size_inches(7, 6)
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fig.suptitle(year)
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n.plot(
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bus_sizes=costs[year] / bus_size_factor,
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bus_colors=snakemake.config['plotting']['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, **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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fig.savefig(
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snakemake.output[f"map_{year}"],
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transparent=True,
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bbox_inches="tight"
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)
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#%%
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake
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@ -921,10 +1068,9 @@ if __name__ == "__main__":
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"plot_network",
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simpl="",
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opts="",
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clusters="5",
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ll="v1.5",
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sector_opts="CO2L0-1H-T-H-B-I-A-solar+p3-dist1",
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planning_horizons="2030",
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clusters="37",
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ll="v1.0",
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sector_opts="4380H-T-H-B-I-A-solar+p3-dist1",
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)
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logging.basicConfig(level=snakemake.config["logging"]["level"])
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@ -938,6 +1084,11 @@ if __name__ == "__main__":
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if map_opts["boundaries"] is None:
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map_opts["boundaries"] = regions.total_bounds[[0, 2, 1, 3]] + [-1, 1, -1, 1]
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if snakemake.params["foresight"] == "perfect":
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plot_map_perfect(n,
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components=["Link", "Store", "StorageUnit", "Generator"],
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bus_size_factor=2e10)
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else:
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plot_map(
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n,
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components=["generators", "links", "stores", "storage_units"],
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