78 lines
2.6 KiB
Python
78 lines
2.6 KiB
Python
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build district heat shares at each node, depending on investment year.
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"""
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import pandas as pd
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from prepare_sector_network import get
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import logging
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logger = logging.getLogger(__name__)
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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_heat_demands",
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simpl="",
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clusters=48,
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)
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investment_year = int(snakemake.wildcards.planning_horizons[-4:])
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
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index_col=0)
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district_heat_share = pd.read_csv(snakemake.input.district_heat_share,
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index_col=0).squeeze()
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# make ct-based share nodal
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district_heat_share = district_heat_share.loc[pop_layout.ct]
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district_heat_share.index = pop_layout.index
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# total urban population per country
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ct_urban = pop_layout.urban.groupby(pop_layout.ct).sum()
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# distribution of urban population within a country
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pop_layout["urban_ct_fraction"] = pop_layout.urban / pop_layout.ct.map(ct_urban.get)
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# fraction of node that is urban
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urban_fraction = pop_layout.urban / pop_layout[["rural", "urban"]].sum(axis=1)
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# maximum potential of urban demand covered by district heating
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central_fraction = snakemake.config["sector"]["district_heating"]["potential"]
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# district heating share at each node
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dist_fraction_node = (
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district_heat_share * pop_layout["urban_ct_fraction"] / pop_layout["fraction"]
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)
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# if district heating share larger than urban fraction -> set urban
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# fraction to district heating share
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urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1)
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# difference of max potential and today's share of district heating
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diff = (urban_fraction * central_fraction) - dist_fraction_node
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progress = get(snakemake.config["sector"]["district_heating"]["progress"], investment_year)
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dist_fraction_node += diff * progress
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logger.info(
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f"Increase district heating share by a progress factor of {progress:.2%} "
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f"resulting in new average share of {dist_fraction_node.mean():.2%}"
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)
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df = pd.DataFrame(dtype=float)
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df["original district heat share"] = district_heat_share
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df["district fraction of node"] = dist_fraction_node
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df["urban fraction"] = urban_fraction
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df.to_csv(snakemake.output.district_heat_share)
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