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>
63 lines
1.8 KiB
Python
63 lines
1.8 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 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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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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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.electricity_prices,
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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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optimized = n.buses_t.marginal_price.groupby(n.buses.country, axis=1).mean()
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data = pd.concat([historic, optimized], keys=["Historic", "Optimized"], axis=1)
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data.columns.names = ["Kind", "Country"]
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fig, ax = plt.subplots(figsize=(6, 6))
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df = data.mean().unstack().T
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df.plot.barh(ax=ax, xlabel="Electricity Price [€/MWh]", ylabel="")
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ax.grid(axis="y")
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fig.savefig(snakemake.output.price_bar, bbox_inches="tight")
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fig, ax = plt.subplots()
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df = data.groupby(level="Kind", axis=1).mean()
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df.plot(ax=ax, xlabel="", ylabel="Electricity Price [€/MWh]", alpha=0.8)
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ax.grid(axis="x")
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fig.savefig(snakemake.output.price_line, bbox_inches="tight")
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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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