pvcompare.heat_pump_and_chiller.add_sector_coupling

pvcompare.heat_pump_and_chiller.add_sector_coupling(weather, lat, lon, user_inputs_pvcompare_directory=None, user_inputs_mvs_directory=None, overwrite_hp_parameters=None)[source]

Add heat sector if heat pump or chiller are in energyConversion.csv.

COPs or EERS are calculated automatically as long as the parameters inflow_direction and outflow_direction give a hint that the respective asset is a heat pump (inflow_direction: “Electricity”, outflow_direction: “Heat”) or chiller (Not implemented, yet.).

Parameters

  • weather (pandas.DataFrame) – DataFrame with time series for temperature in column ‘temp_air’ in °C.

  • lat (float) – Latitude of ambient temperature location in weather.

  • lon (float) – Longitude of ambient temperature location in weather.

  • user_inputs_pvcompare_directory (str or None) – Directory of the user inputs. If None, constants.DEFAULT_USER_INPUTS_PVCOMPARE_DIRECTORY is used as user_inputs_pvcompare_directory. Default: None.

  • user_inputs_mvs_directory (str or None) – Directory of the multi-vector simulation inputs; where ‘csv_elements/’ is located. If None, constants.DEFAULT_USER_INPUTS_MVS_DIRECTORY is used as user_inputs_mvs_directory. Default: None.

  • overwrite_hp_parameters (bool) – Default: True. If true, existing COP time series of the heat pump will be overwritten with calculated time series of COP.

Notes

Chillers were not tested, yet, and no automatic calculation of EERs is implemented. Attention: the above mentioned characteristics (inflow_direction: “Electricity”, outflow_direction: “Heat”) could also account for other heating elements. This function could be enhanced.

Returns

Depending on the case, updates energyConversion.csv and saves calculated cops to ‘data/mvs_inputs/time_series’.

Return type

None