pypsa-eur/scripts/build_existing_heating_distribution.py

# -*- 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.
"""
import pandas as pd
import numpy as np
import country_converter as coco

cc = coco.CountryConverter()


def build_existing_heating():
    # retrieve existing heating capacities

    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

    existing_heating.fillna(0.0, inplace=True)

    # convert GW to MW
    existing_heating *= 1e3

    existing_heating.index = cc.convert(existing_heating.index, to="iso2")

    # 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)

    nodal_heating = existing_heating.loc[pop_layout.ct]
    nodal_heating.index = pop_layout.index
    nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)

    district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
                                     index_col=0)
    dist_fraction = district_heat_info["district fraction of node"]
    urban_fraction = district_heat_info["urban fraction"]

    energy_layout = pd.read_csv(snakemake.input.clustered_pop_energy_layout,
                                index_col=0)

    uses = ["space", "water"]
    sectors = ["residential", "services"]

    nodal_sectoral_totals = pd.DataFrame(dtype=float)

    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)


    nodal_heat_name_fraction = pd.DataFrame(dtype=float)

    nodal_heat_name_fraction["urban central"] = dist_fraction

    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 - dist_fraction)


    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"])


    #move all ground HPs to rural, all air to urban

    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.

        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.

    nodal_heat_name_tech[("urban central","ground heat pump")] = 0.

    nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)


if __name__ == "__main__":
    if "snakemake" not in globals():
        from _helpers import mock_snakemake

        snakemake = mock_snakemake(
            "build_existing_heating_distribution",
            simpl="",
            clusters=48,
            planning_horizons=2050,
        )

    build_existing_heating()