88 lines
2.9 KiB
Python
88 lines
2.9 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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from typing import Union
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import numpy as np
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import xarray as xr
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from BaseCopApproximator import BaseCopApproximator
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class DecentralHeatingCopApproximator(BaseCopApproximator):
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"""
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Approximate the coefficient of performance (COP) for a heat pump in a
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decentral heating system (individual/household heating).
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Uses a quadratic regression on the temperature difference between the source and sink based on empirical data proposed by Staffell et al. 2012 .
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References
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----------
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[1] Staffell et al., Energy & Environmental Science 11 (2012): A review of domestic heat pumps, https://doi.org/10.1039/C2EE22653G.
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"""
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def __init__(
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self,
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forward_temperature_celsius: Union[xr.DataArray, np.array],
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source_inlet_temperature_celsius: Union[xr.DataArray, np.array],
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source_type: str,
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):
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"""
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Initialize the COPProfileBuilder object.
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Parameters:
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----------
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forward_temperature_celsius : Union[xr.DataArray, np.array]
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The forward temperature in Celsius.
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return_temperature_celsius : Union[xr.DataArray, np.array]
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The return temperature in Celsius.
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source: str
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The source of the heat pump. Must be either 'air' or 'ground'
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"""
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self.delta_t = forward_temperature_celsius - source_inlet_temperature_celsius
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if source_type not in ["air", "ground"]:
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raise ValueError("'source' must be one of ['air', 'ground']")
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else:
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self.source_type = source_type
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def approximate_cop(self) -> Union[xr.DataArray, np.array]:
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"""
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Compute output of quadratic regression for air-/ground-source heat
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pumps.
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Calls the appropriate method depending on `source`.
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"""
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if self.source_type == "air":
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return self._approximate_cop_air_source()
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elif self.source_type == "ground":
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return self._approximate_cop_ground_source()
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def _approximate_cop_air_source(self) -> Union[xr.DataArray, np.array]:
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"""
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Evaluate quadratic regression for an air-sourced heat pump.
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COP = 6.81 - 0.121 * delta_T + 0.000630 * delta_T^2
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Returns
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-------
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Union[xr.DataArray, np.array]
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The calculated COP values.
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"""
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return 6.81 - 0.121 * self.delta_t + 0.000630 * self.delta_t**2
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def _approximate_cop_ground_source(self) -> Union[xr.DataArray, np.array]:
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"""
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Evaluate quadratic regression for a ground-sourced heat pump.
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COP = 8.77 - 0.150 * delta_T + 0.000734 * delta_T^2
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Returns
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-------
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Union[xr.DataArray, np.array]
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The calculated COP values.
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"""
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return 8.77 - 0.150 * self.delta_t + 0.000734 * self.delta_t**2
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