013b705ee4
* Cluster first: build renewable profiles and add all assets after clustering * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * correction: pass landfall_lengths through functions * assign landfall_lenghts correctly * remove parameter add_land_use_constraint * fix network_dict * calculate distance to shoreline, remove underwater_fraction * adjust simplification parameter to exclude Crete from offshore wind connections * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * remove unused geth2015 hydro capacities * removing remaining traces of {simpl} wildcard * add release notes and update workflow graphics * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: lisazeyen <lisa.zeyen@web.de>
280 lines
8.0 KiB
Python
280 lines
8.0 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 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 map of optimised hydrogen network, storage and selected other
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infrastructure.
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"""
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import logging
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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 _helpers import configure_logging, set_scenario_config
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from plot_power_network import assign_location, load_projection
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from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
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logger = logging.getLogger(__name__)
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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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df = df.copy()
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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_orig"] = df.index
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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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return df.groupby(level=0).agg(
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{"p_nom_opt": "sum", "bus0": "first", "bus1": "first", "index_orig": "first"}
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)
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def plot_h2_map(n, regions):
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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"] = (
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h2_storage.rename(index=h2_storage.bus.map(n.buses.location))
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.e_nom_opt.groupby(level=0)
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.sum()
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.div(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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if snakemake.params.foresight != "myopic":
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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.apply(
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lambda x: x.split("-2")[0]
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)
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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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# group links by summing up p_nom values and taking the first value of the rest of the columns
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other_cols = dict.fromkeys(n.links.columns.drop(["p_nom_opt", "p_nom"]), "first")
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n.links = n.links.groupby(level=0).agg(
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{"p_nom_opt": "sum", "p_nom": "sum", **other_cols}
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)
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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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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(snakemake.output.map, bbox_inches="tight")
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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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snakemake = mock_snakemake(
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"plot_hydrogen_network",
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opts="",
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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-dist1",
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)
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configure_logging(snakemake)
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set_scenario_config(snakemake)
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n = pypsa.Network(snakemake.input.network)
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regions = gpd.read_file(snakemake.input.regions).set_index("name")
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map_opts = snakemake.params.plotting["map"]
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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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proj = load_projection(snakemake.params.plotting)
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plot_h2_map(n, regions)
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