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| # -*- coding: utf-8 -*- | ||
| # SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors | ||
| # | ||
| # SPDX-License-Identifier: MIT | ||
| """ | ||
| Builds table of existing heat generation capacities for initial planning | ||
| horizon. | ||
| Existing heat generation capacities are distributed to nodes based on population. | ||
| Within the nodes, the capacities are distributed to sectors (residential and services) based on sectoral consumption and urban/rural based population distribution. | ||
| Inputs: | ||
| ------- | ||
| - Existing heating generators: `data/existing_heating_raw.csv` per country | ||
| - Population layout: `resources/{run_name}/pop_layout_s<simpl>_<clusters>.csv`. Output of `scripts/build_clustered_population_layout.py` | ||
| - Population layout with energy demands: `resources/<run_name>/pop_weighted_energy_totals_s<simpl>_<clusters>.csv` | ||
| - District heating share: `resources/<run_name>/district_heat_share_elec_s<simpl>_<clusters>_<planning_horizons>.csv` | ||
| Outputs: | ||
| -------- | ||
| - Existing heat generation capacities distributed to nodes: `resources/{run_name}/existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv` | ||
| Relevant settings: | ||
| ------------------ | ||
| .. code:: yaml | ||
| scenario: | ||
| planning_horizons | ||
| sector: | ||
| existing_capacities: | ||
| Notes: | ||
| ------ | ||
| - Data for Albania, Montenegro and Macedonia is not included in input database and assumed 0. | ||
| - Coal and oil boilers are assimilated to oil boilers. | ||
| - All ground-source heat pumps are assumed in rural areas and all air-source heat pumps are assumed to be in urban areas. | ||
| References: | ||
| ----------- | ||
| - "Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)" (https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en) | ||
| """ | ||
| import logging | ||
| import os | ||
|
|
||
| import country_converter as coco | ||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| logger = logging.getLogger(__name__) | ||
|
|
||
| cc = coco.CountryConverter() | ||
|
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|
|
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| def build_existing_heating(): | ||
| # retrieve existing heating capacities | ||
|
|
||
| # Add existing heating capacities, data comes from the study | ||
| # "Mapping and analyses of the current and future (2020 - 2030) | ||
| # heating/cooling fuel deployment (fossil/renewables) " | ||
| # https://energy.ec.europa.eu/publications/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment-fossilrenewables-1_en | ||
| # file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv". | ||
| # data is for buildings only (i.e. NOT district heating) and represents the year 2012 | ||
| # TODO start from original file | ||
|
|
||
| existing_heating = pd.read_csv( | ||
| snakemake.input.existing_heating, index_col=0, header=0 | ||
| ) | ||
|
|
||
| # data for Albania, Montenegro and Macedonia not included in database | ||
| existing_heating.loc["Albania"] = np.nan | ||
| existing_heating.loc["Montenegro"] = np.nan | ||
| existing_heating.loc["Macedonia"] = np.nan | ||
|
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| existing_heating.fillna(0.0, inplace=True) | ||
|
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| fillvalue_missing = existing_heating.loc["DEFAULT"] | ||
|
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| # convert GW to MW | ||
| 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 | ||
| existing_heating["oil boiler"] = ( | ||
| existing_heating["oil boiler"] + existing_heating["coal boiler"] | ||
| ) | ||
| existing_heating.drop(["coal boiler"], axis=1, inplace=True) | ||
|
|
||
| # distribute technologies to nodes by population | ||
| pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0) | ||
|
|
||
| # fill missing rows | ||
| missing_countries = list(set(pop_layout.ct.unique()) - set(existing_heating.index)) | ||
| if len(missing_countries) > 0: | ||
| logger.warning( | ||
| f"Missing population data for countries: {missing_countries}. Filling with DEFAULT values." | ||
| ) | ||
| for country in missing_countries: | ||
| existing_heating.loc[country] = fillvalue_missing | ||
|
|
||
| nodal_heating = existing_heating.loc[pop_layout.ct] | ||
| nodal_heating.index = pop_layout.index | ||
| 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) | ||
| urban_fraction = pop_layout["fraction"] | ||
|
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| energy_layout = pd.read_csv( | ||
| snakemake.input.clustered_pop_energy_layout, index_col=0 | ||
| ) | ||
|
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| uses = ["space", "water"] | ||
| sectors = ["residential", "services"] | ||
|
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| nodal_sectoral_totals = pd.DataFrame(dtype=float) | ||
|
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| for sector in sectors: | ||
| nodal_sectoral_totals[sector] = energy_layout[ | ||
| [f"total {sector} {use}" for use in uses] | ||
| ].sum(axis=1) | ||
|
|
||
| nodal_sectoral_fraction = nodal_sectoral_totals.div( | ||
| nodal_sectoral_totals.sum(axis=1), axis=0 | ||
| ) | ||
|
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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: | ||
| nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[ | ||
| sector | ||
| ] * (1 - urban_fraction) | ||
| nodal_heat_name_fraction[f"{sector} urban decentral"] = ( | ||
| nodal_sectoral_fraction[sector] * urban_fraction | ||
| ) | ||
|
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||
| nodal_heat_name_tech = pd.concat( | ||
| { | ||
| name: nodal_heating.multiply(nodal_heat_name_fraction[name], axis=0) | ||
| for name in nodal_heat_name_fraction.columns | ||
| }, | ||
| axis=1, | ||
| 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: | ||
| nodal_heat_name_tech[(f"{sector} rural", "ground heat pump")] += ( | ||
| nodal_heat_name_tech[("urban central", "ground heat pump")] | ||
| * nodal_sectoral_fraction[sector] | ||
| + nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] | ||
| ) | ||
| nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] = 0.0 | ||
|
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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")] | ||
| 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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|
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| if __name__ == "__main__": | ||
| if "snakemake" not in globals(): | ||
| from helpers import mock_snakemake | ||
|
|
||
| os.chdir(os.path.dirname(os.path.abspath(__file__))) | ||
|
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| snakemake = mock_snakemake( | ||
| "build_existing_heating_distribution", | ||
| simpl="", | ||
| clusters=4, | ||
| planning_horizons=2030, | ||
| demand="DF", | ||
| ) | ||
|
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| build_existing_heating() |