109 lines
4.1 KiB
Python
109 lines
4.1 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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Builds table of existing heat generation capacities for initial planning
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horizon.
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"""
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import pandas as pd
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import sys
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from pypsa.descriptors import Dict
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import numpy as np
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import country_converter as coco
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cc = coco.CountryConverter()
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def build_existing_heating():
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# retrieve existing heating capacities
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techs = [
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"gas boiler",
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"oil boiler",
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"resistive heater",
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"air heat pump",
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"ground heat pump",
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]
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existing_heating = pd.read_csv(snakemake.input.existing_heating,
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index_col=0,
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header=0)
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# data for Albania, Montenegro and Macedonia not included in database existing_heating.loc["Albania"] = np.nan
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existing_heating.loc["Montenegro"] = np.nan
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existing_heating.loc["Macedonia"] = np.nan
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existing_heating.fillna(0.0, inplace=True)
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# convert GW to MW
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existing_heating *= 1e3
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existing_heating.index = cc.convert(existing_heating.index, to="iso2")
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# coal and oil boilers are assimilated to oil boilers
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existing_heating["oil boiler"] = existing_heating["oil boiler"] + existing_heating["coal boiler"]
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existing_heating.drop(["coal boiler"], axis=1, inplace=True)
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# distribute technologies to nodes by population
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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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nodal_heating = existing_heating.loc[pop_layout.ct]
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nodal_heating.index = pop_layout.index
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nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)
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district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
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index_col=0)
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dist_fraction = district_heat_info["district fraction of node"]
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urban_fraction = district_heat_info["urban fraction"]
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energy_layout = pd.read_csv(snakemake.input.clustered_pop_energy_layout,
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index_col=0)
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uses = ["space", "water"]
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sectors = ["residential", "services"]
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nodal_sectoral_totals = pd.DataFrame(dtype=float)
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for sector in sectors:
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nodal_sectoral_totals[sector] = energy_layout[[f"total {sector} {use}" for use in uses]].sum(axis=1)
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nodal_sectoral_fraction = nodal_sectoral_totals.div(nodal_sectoral_totals.sum(axis=1),
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axis=0)
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nodal_heat_name_fraction = pd.DataFrame(dtype=float)
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nodal_heat_name_fraction["urban central"] = dist_fraction
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for sector in sectors:
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nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[sector]*(1 - urban_fraction)
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nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[sector]*(urban_fraction - dist_fraction)
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nodal_heat_name_tech = pd.concat({name : nodal_heating .multiply(nodal_heat_name_fraction[name],
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axis=0) for name in nodal_heat_name_fraction.columns},
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axis=1,
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names=["heat name","technology"])
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#move all ground HPs to rural, all air to urban
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for sector in sectors:
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nodal_heat_name_tech[(f"{sector} rural","ground heat pump")] += (nodal_heat_name_tech[("urban central","ground heat pump")]*nodal_sectoral_fraction[sector]
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+ nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")])
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nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")] = 0.
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nodal_heat_name_tech[(f"{sector} urban decentral","air heat pump")] += nodal_heat_name_tech[(f"{sector} rural","air heat pump")]
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nodal_heat_name_tech[(f"{sector} rural","air heat pump")] = 0.
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nodal_heat_name_tech[("urban central","ground heat pump")] = 0.
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nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)
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if __name__ == "__main__":
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build_existing_heating()
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