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>
42 lines
1.3 KiB
Python
42 lines
1.3 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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Distribute country-level energy demands by population.
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"""
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import pandas as pd
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from _helpers import set_scenario_config
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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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"build_population_weighted_energy_totals",
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kind="heat",
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clusters=60,
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)
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set_scenario_config(snakemake)
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config = snakemake.config["energy"]
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if snakemake.wildcards.kind == "heat":
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years = pd.date_range(freq="h", **snakemake.params.snapshots).year.unique()
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assert len(years) == 1, "Currently only works for single year."
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data_year = years[0]
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else:
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data_year = int(config["energy_totals_year"])
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
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totals = pd.read_csv(snakemake.input.energy_totals, index_col=[0, 1])
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totals = totals.xs(data_year, level="year")
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nodal_totals = totals.loc[pop_layout.ct].fillna(0.0)
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nodal_totals.index = pop_layout.index
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nodal_totals = nodal_totals.multiply(pop_layout.fraction, axis=0)
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nodal_totals.to_csv(snakemake.output[0])
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