131 lines
4.5 KiB
Python
131 lines
4.5 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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Builds table of existing heat generation capacities for initial planning
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horizon.
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"""
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import country_converter as coco
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import numpy as np
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import pandas as pd
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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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# Add existing heating capacities, data comes from the study
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# "Mapping and analyses of the current and future (2020 - 2030)
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# heating/cooling fuel deployment (fossil/renewables) "
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# https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en
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# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
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# data is for buildings only (i.e. NOT district heating) and represents the year 2012
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# TODO start from original file
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existing_heating = pd.read_csv(
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snakemake.input.existing_heating, index_col=0, header=0
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)
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# data for Albania, Montenegro and Macedonia not included in database
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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"] = (
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existing_heating["oil boiler"] + existing_heating["coal boiler"]
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)
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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, 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, 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(
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snakemake.input.clustered_pop_energy_layout, index_col=0
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)
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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[
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[f"total {sector} {use}" for use in uses]
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].sum(axis=1)
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nodal_sectoral_fraction = nodal_sectoral_totals.div(
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nodal_sectoral_totals.sum(axis=1), axis=0
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)
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nodal_heat_name_fraction = pd.DataFrame(index=district_heat_info.index, dtype=float)
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nodal_heat_name_fraction["urban central"] = 0.0
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for sector in sectors:
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nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[
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sector
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] * (1 - urban_fraction)
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nodal_heat_name_fraction[f"{sector} urban decentral"] = (
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nodal_sectoral_fraction[sector] * urban_fraction
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)
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nodal_heat_name_tech = pd.concat(
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{
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name: nodal_heating.multiply(nodal_heat_name_fraction[name], axis=0)
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for name in nodal_heat_name_fraction.columns
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},
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axis=1,
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names=["heat name", "technology"],
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)
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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")] += (
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nodal_heat_name_tech[("urban central", "ground heat pump")]
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* nodal_sectoral_fraction[sector]
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+ nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")]
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)
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nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] = 0.0
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nodal_heat_name_tech[
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(f"{sector} urban decentral", "air heat pump")
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] += nodal_heat_name_tech[(f"{sector} rural", "air heat pump")]
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nodal_heat_name_tech[(f"{sector} rural", "air heat pump")] = 0.0
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nodal_heat_name_tech[("urban central", "ground heat pump")] = 0.0
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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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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_existing_heating_distribution",
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simpl="",
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clusters=48,
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planning_horizons=2050,
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)
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build_existing_heating()
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