48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
# -*- 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 coefficient of performance (COP) time series for air- or ground-sourced
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heat pumps.
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The COP is a function of the temperature difference between
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source and sink.
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The quadratic regression used is based on Staffell et al. (2012)
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https://doi.org/10.1039/C2EE22653G.
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"""
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import xarray as xr
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def coefficient_of_performance(delta_T, source="air"):
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if source == "air":
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return 6.81 - 0.121 * delta_T + 0.000630 * delta_T**2
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elif source == "soil":
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return 8.77 - 0.150 * delta_T + 0.000734 * delta_T**2
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else:
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raise NotImplementedError("'source' must be one of ['air', 'soil']")
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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_cop_profiles",
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simpl="",
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clusters=48,
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)
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for area in ["total", "urban", "rural"]:
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for source in ["air", "soil"]:
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source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_{area}"])
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delta_T = snakemake.config["sector"]["heat_pump_sink_T"] - source_T
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cop = coefficient_of_performance(delta_T, source)
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cop.to_netcdf(snakemake.output[f"cop_{source}_{area}"])
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