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>
235 lines
6.8 KiB
Python
235 lines
6.8 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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import country_converter as coco
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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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import seaborn as sns
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from _helpers import configure_logging, set_scenario_config
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sns.set_theme("paper", style="whitegrid")
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cc = coco.CountryConverter()
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color_country = {
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"AL": "#440154",
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"AT": "#482677",
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"BA": "#43398e",
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"BE": "#3953a4",
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"BG": "#2c728e",
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"CH": "#228b8d",
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"CZ": "#1f9d8a",
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"DE": "#29af7f",
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"DK": "#3fbc73",
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"EE": "#5ec962",
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"ES": "#84d44b",
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"FI": "#addc30",
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"FR": "#d8e219",
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"GB": "#fde725",
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"GR": "#f0f921",
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"HR": "#f1c25e",
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"HU": "#f4a784",
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"IE": "#f78f98",
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"IT": "#f87ea0",
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"LT": "#f87a9a",
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"LU": "#f57694",
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"LV": "#f3758d",
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"ME": "#f37685",
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"MK": "#f37b7c",
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"NL": "#FF6666",
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"NO": "#FF3333",
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"PL": "#eb0000",
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"PT": "#d70000",
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"RO": "#c00000",
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"RS": "#a50000",
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"SE": "#8a0000",
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"SI": "#6f0000",
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"SK": "#550000",
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}
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def sort_one_country(country, df):
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indices = [link for link in df.columns if country in link]
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df_country = df[indices].copy()
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for link in df_country.columns:
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if country in link[5:]:
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df_country[link] = -df_country[link]
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link_reverse = str(link[5:] + " - " + link[:2])
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df_country = df_country.rename(columns={link: link_reverse})
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return df_country.reindex(sorted(df_country.columns), axis=1)
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def cross_border_time_series(countries, data):
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fig, ax = plt.subplots(2 * len(countries), 1, figsize=(15, 10 * len(countries)))
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axis = 0
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for country in countries:
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ymin = 0
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ymax = 0
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for df in data:
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df_country = sort_one_country(country, df)
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df_neg, df_pos = df_country.clip(upper=0), df_country.clip(lower=0)
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color = [color_country[link[5:]] for link in df_country.columns]
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df_pos.plot.area(
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ax=ax[axis], stacked=True, linewidth=0.0, color=color, ylim=[-1, 1]
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)
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df_neg.plot.area(
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ax=ax[axis], stacked=True, linewidth=0.0, color=color, ylim=[-1, 1]
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)
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title = "Historic" if (axis % 2) == 0 else "Optimized"
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ax[axis].set_title(
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f"{title} Import / Export for " + cc.convert(country, to="name_short")
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)
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# Custom legend elements
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legend_elements = []
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for link in df_country.columns:
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legend_elements = legend_elements + [
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plt.fill_between(
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[],
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[],
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color=color_country[link[5:]],
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label=cc.convert(link[5:], to="name_short"),
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)
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]
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# Create the legend
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ax[axis].legend(handles=legend_elements, loc="upper right")
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# rescale the y axis
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neg_min = df_neg.sum(axis=1).min() * 1.2
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if neg_min < ymin:
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ymin = neg_min
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pos_max = df_pos.sum(axis=1).max() * 1.2
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if pos_max < ymax:
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ymax = pos_max
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axis = axis + 1
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for x in range(axis - 2, axis):
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ax[x].set_ylim([neg_min, pos_max])
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fig.savefig(snakemake.output.trade_time_series, bbox_inches="tight")
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def cross_border_bar(countries, data):
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df_positive = pd.DataFrame()
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df_negative = pd.DataFrame()
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color = []
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for country in countries:
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order = 0
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for df in data:
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df_country = sort_one_country(country, df)
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df_neg, df_pos = df_country.clip(upper=0), df_country.clip(lower=0)
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title = "Historic" if (order % 2) == 0 else "Optimized"
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df_positive_new = pd.DataFrame(data=df_pos.sum()).T.rename(
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{0: f"{title} " + cc.convert(country, to="name_short")}
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)
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df_negative_new = pd.DataFrame(data=df_neg.sum()).T.rename(
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{0: f"{title} " + cc.convert(country, to="name_short")}
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)
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df_positive = pd.concat([df_positive_new, df_positive])
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df_negative = pd.concat([df_negative_new, df_negative])
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order = order + 1
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color = [color_country[link[5:]] for link in df_positive.columns]
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fig, ax = plt.subplots(figsize=(15, 60))
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df_positive.plot.barh(ax=ax, stacked=True, color=color, zorder=2)
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df_negative.plot.barh(ax=ax, stacked=True, color=color, zorder=2)
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plt.grid(axis="x", zorder=0)
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plt.grid(axis="y", zorder=0)
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# Custom legend elements
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legend_elements = []
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for country in list(color_country.keys()):
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legend_elements = legend_elements + [
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plt.fill_between(
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[],
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[],
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color=color_country[country],
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label=cc.convert(country, to="name_short"),
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)
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]
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# Create the legend
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plt.legend(handles=legend_elements, loc="upper right")
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fig.savefig(snakemake.output.cross_border_bar, 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_electricity_prices",
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opts="Ept-12h",
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clusters="37",
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ll="v1.0",
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)
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configure_logging(snakemake)
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set_scenario_config(snakemake)
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countries = snakemake.params.countries
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n = pypsa.Network(snakemake.input.network)
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n.loads.carrier = "load"
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historic = pd.read_csv(
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snakemake.input.cross_border_flows,
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index_col=0,
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header=0,
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parse_dates=True,
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)
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if len(historic.index) > len(n.snapshots):
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historic = historic.resample(n.snapshots.inferred_freq).mean().loc[n.snapshots]
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# Preparing network data to be shaped similar to ENTSOE datastructure
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optimized_links = n.links_t.p0.rename(
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columns=dict(n.links.bus0.str[:2] + " - " + n.links.bus1.str[:2])
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)
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optimized_lines = n.lines_t.p0.rename(
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columns=dict(n.lines.bus0.str[:2] + " - " + n.lines.bus1.str[:2])
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)
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optimized = pd.concat([optimized_links, optimized_lines], axis=1)
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# Drop internal country connection
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optimized.drop(
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[c for c in optimized.columns if c[:2] == c[5:]], axis=1, inplace=True
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)
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# align columns name
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for c1 in optimized.columns:
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for c2 in optimized.columns:
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if c1[:2] == c2[5:] and c2[:2] == c1[5:]:
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optimized = optimized.rename(columns={c1: c2})
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optimized = optimized.groupby(lambda x: x, axis=1).sum()
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cross_border_bar(countries, [historic, optimized])
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cross_border_time_series(countries, [historic, optimized])
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# touch file
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with open(snakemake.output.plots_touch, "a"):
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pass
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