:py:mod:`eelib.core.control.hems.schedule_helper`
=================================================

.. py:module:: eelib.core.control.hems.schedule_helper

.. autoapi-nested-parse::

   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
~~~~~~~~~

.. autoapisummary::

   eelib.core.control.hems.schedule_helper.calc_forecast_residual
   eelib.core.control.hems.schedule_helper.calc_forecast_thermal_residual
   eelib.core.control.hems.schedule_helper.bss_calc_schedule
   eelib.core.control.hems.schedule_helper.pv_calc_schedule
   eelib.core.control.hems.schedule_helper.hp_calc_schedule
   eelib.core.control.hems.schedule_helper.cs_calc_schedule_uncontrolled
   eelib.core.control.hems.schedule_helper.calc_schedule_opt



Attributes
~~~~~~~~~~

.. autoapisummary::

   eelib.core.control.hems.schedule_helper._logger


.. py:data:: _logger

   

.. py:function:: calc_forecast_residual(forecast: dict, forecast_horizon: int) -> numpy.ndarray

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

   :param forecast: input forecasts to calculate schedule
   :type forecast: dict
   :param forecast_horizon: timesteps in which forecast is created
   :type forecast_horizon: int

   :returns: residual electrical power values from forecasts for attribute "p" for devices
   :rtype: np.ndarray


.. py:function:: calc_forecast_thermal_residual(forecast: dict, forecast_horizon: int) -> numpy.ndarray

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

   :param forecast: input forecasts to calculate schedule
   :type forecast: dict
   :param forecast_horizon: timesteps in which forecast is created
   :type forecast_horizon: int

   :returns: residual thermal demand values from forecast
   :rtype: np.ndarray


.. py:function:: 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.

   :param step_size: simulation step_size
   :type step_size: int
   :param schedule_p_res: Input schedule of residual load.
   :type schedule_p_res: list
   :param bss_data: battery information at start of forecast with structure {id: BSSData}
   :type bss_data: dict

   :returns: Schedule with ``p_set`` values of the bss in the forecast horizon.
   :rtype: dict


.. py:function:: pv_calc_schedule(forecast: dict, pv_data: dict) -> dict

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

   :param forecast: collected forecast from EMS
   :type forecast: dict
   :param pv_data: contains all information of pv systems with structure {id: PVData}
   :type pv_data: dict

   :returns: Schedule with power set values of the pv systems in the forecast horizon.
   :rtype: dict


.. py:function:: 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.

   :param step_size: simulation step_size
   :type step_size: int
   :param th_demand_profile: input profile of the thermal power demand.
   :type th_demand_profile: list
   :param energy_demand_th: current thermic energy demand
   :type energy_demand_th: float
   :param hp_data: contains all information of heatpumps with structure {id: HPData}
   :type hp_data: dict

   :returns: Schedule with thermal and electrical set values of the hps in the forecast horizon.
   :rtype: dict


.. py:function:: 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.

   :param step_size: simulation step size in seconds
   :type step_size: int
   :param forecast: collected forecast from EMS
   :type forecast: dict
   :param forecast_horizon: length of the forecast period
   :type forecast_horizon: int
   :param cs_data: contains all information of charging stations with structure {id: CSData}
   :type cs_data: dict

   :returns: Schedule with power set values of the charging stations in the forecast horizon.
   :rtype: dict


.. py:function:: 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.

   :param step_size: simulation step_size
   :type step_size: int
   :param forecast: collected forecasts from EMS
   :type forecast: dict
   :param forecast_horizon: timesteps for which forecast should be calculated
   :type forecast_horizon: int
   :param opt: information about optimization setup (whats used and what not)
   :type opt: OptimOptions
   :param tariff: electricity price signal. Defaults to TariffSignal().
   :type tariff: TariffSignal
   :param cs_data: info of charging stations, like {id: CSData}. Defaults to {}.
   :type cs_data: dict
   :param bss_data: info of battery storage systems, like {id: BSSData}. Defaults to {}.
   :type bss_data: dict
   :param pv_data: info of pv systems, like {id: PVData}. Defaults to {}.
   :type pv_data: dict
   :param hp_data: info of heatpumps, like {id: HPData}. Defaults to {}.
   :type hp_data: dict
   :param control_signals: Control signals sent to EMS with lists for power limits.
   :type control_signals: ControlSignalEMS

   :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.
   :rtype: dict