eelib.core.control.EMS.schedule_helper

Helper functions for the creation of schedules in EMS.

Pyomo released under 3-clause BSD license

Author: elenia@TUBS
Copyright 2024 elenia
This file is part of eELib, which is free software under the terms of the GNU GPL Version 3.

Module Contents

Functions

calc_forecast_residual(→ numpy.ndarray)

Calculates the residual forecast for the "p"-values of all devices' forecasts.

calc_forecast_thermal_residual(→ numpy.ndarray)

Calculates the residual forecast for the thermal demand ("p_th"-values).

bss_calc_schedule(→ dict)

Calculates the schedule for the p_set values of the bss.

pv_calc_schedule(→ dict)

Calculates the schedule for all pv systems using the forecast (maximum power).

hp_calc_schedule(→ dict)

Calculates the schedule for the thermal power set values of the heatpump.

cs_calc_schedule_uncontrolled(step_size, forecast, ...)

Calculates the schedule for all connected charging stations and its connected cars.

calc_schedule_opt(, tariff, cs_data, bss_data, ...)

Calculates an optimal schedule for all connected devices.

Attributes

_logger

_logger
calc_forecast_residual(forecast: dict, forecast_horizon: int) → numpy.ndarray

Calculates the residual forecast for the “p”-values of all devices’ forecasts.

Parameters:

  • forecast (dict) – input forecasts to calculate schedule

  • forecast_horizon (int) – time steps in which forecast is created

Returns:

residual electrical power values from forecasts for attribute “p” for devices

Return type:

np.ndarray

calc_forecast_thermal_residual(forecast: dict, forecast_horizon: int) → numpy.ndarray

Calculates the residual forecast for the thermal demand (“p_th”-values).

Parameters:

  • forecast (dict) – input forecasts to calculate schedule

  • forecast_horizon (int) – time steps in which forecast is created

Returns:

residual thermal demand values from forecast

Return type:

np.ndarray

bss_calc_schedule(step_size: int, schedule_p_res: list, bss_data: dict) → dict

Calculates the schedule for the p_set values of the bss.

Parameters:

  • step_size (int) – simulation step_size

  • schedule_p_res (list) – Input schedule of residual load.

  • bss_data (dict) – battery information at start of forecast with structure {id: BSSData}

Returns:

Schedule with p_set values of the bss in the forecast horizon.

Return type:

dict

pv_calc_schedule(forecast: dict, pv_data: dict) → dict

Calculates the schedule for all pv systems using the forecast (maximum power).

Parameters:

  • forecast (dict) – collected forecast from EMS

  • pv_data (dict) – contains all information of pv systems with structure {id: PVData}

Returns:

Schedule with power set values of the pv systems in the forecast horizon.

Return type:

dict

hp_calc_schedule(step_size: int, th_demand_profile: list, energy_demand_th: float, hp_data: dict) → dict

Calculates the schedule for the thermal power set values of the heatpump.

Parameters:

  • step_size (int) – simulation step_size

  • th_demand_profile (list) – input profile of the thermal power demand.

  • energy_demand_th (float) – current thermic energy demand

  • hp_data (dict) – contains all information of heatpumps with structure {id: HPData}

Returns:

Schedule with thermal and electrical set values of the hps in the forecast horizon.

Return type:

dict

cs_calc_schedule_uncontrolled(step_size: int, forecast: dict, forecast_horizon: int, cs_data: dict)

Calculates the schedule for all connected charging stations and its connected cars. Uses the forecast of the appereance and the cars consumption. NOTE: For this schedule the car is always charged with max. power.

Parameters:

  • step_size (int) – simulation step size in seconds

  • forecast (dict) – collected forecast from EMS

  • forecast_horizon (int) – length of the forecast period

  • cs_data (dict) – contains all information of charging stations with structure {id: CSData}

Returns:

Schedule with power set values of the charging stations in the forecast horizon.

Return type:

dict

calc_schedule_opt(step_size: int, forecast: dict, forecast_horizon: int, opt: eelib.data.OptimOptions = OptimOptions(), tariff: eelib.data.TariffSignal = TariffSignal(), cs_data: dict = {}, bss_data: dict = {}, pv_data: dict = {}, hp_data: dict = {}, control_signals: eelib.data.ControlSignalEMS = ControlSignalEMS()) → dict

Calculates an optimal schedule for all connected devices.

Parameters:

  • step_size (int) – simulation step_size

  • forecast (dict) – collected forecasts from EMS

  • forecast_horizon (int) – timesteps for which forecast should be calculated

  • opt (OptimOptions) – information about optimization setup (whats used and what not)

  • tariff (TariffSignal) – electricity price signal. Defaults to TariffSignal().

  • cs_data (dict) – info of charging stations, like {id: CSData}. Defaults to {}.

  • bss_data (dict) – info of battery storage systems, like {id: BSSData}. Defaults to {}.

  • pv_data (dict) – info of pv systems, like {id: PVData}. Defaults to {}.

  • hp_data (dict) – info of heatpumps, like {id: HPData}. Defaults to {}.

  • control_signals (ControlSignalEMS) – Control signals sent to EMS with lists for power limits.

Raises:

ValueError – constructed optimization problem could not be solved by solver

Returns:

Schedule with power set values of the connected devices in the forecast horizon.

Return type:

dict