Implementing an EMS strategy

Energy Management Strategies are handled by Energy Management Systems (EMS). To implement a new strategy, additions in multiple places are necessary.

1. Adapt the EMS_model.py file and implement the operating strategy

Create a new class that is inheriting from the general HEMS class. Don’t forget to add the strategy in the valid parameter of the HEMS ABC class.

eelib/core/control/EMS/EMS_model.py (01/24)

class HEMS_default(HEMS):
    """Default strategy for Energy Management System.
    Should be copied and adapted for the use of a specific EMS concept.
    """

    @classmethod
    def get_valid_parameters(cls):
        """Returns dictionary containing valid parameter types and values.

        Returns:
            dict: valid parameters for this model
        """

        # use parent's parameter list and modify them for this class
        result = HEMS.get_valid_parameters().copy()
        result.update({})
        return result

    def __init__(self, ename: str, step_size: int = 900, **kwargs):
        """Initializes the eELib HEMS default model.

        Args:
            ename (str): name of the entity to create
            step_size (int): length of a simulation step in seconds
            **kwargs: initial values for the HEMS entity

        Raises:
            ValueError: Error if selected strategy does not comply with model type.
        """

        # check given strategy
        if "strategy" in kwargs.keys() and kwargs["strategy"] != "HEMS_default":
            raise ValueError("Created a HEMS_default entity with strategy not 'HEMS_default'!")
        else:
            kwargs["strategy"] = "HEMS_default"  # set strategy if not already given

        # call init function of super HEMS class
        super().__init__(ename=ename, step_size=step_size, **kwargs)

Add a step() function that first calls the HEMS step() and afterwards implement the functionalities of your operating strategy

eelib/core/control/EMS/EMS_model.py (01/24)

def step(self, time):
    """Calculates power set values for each connected component according to the strategy.

    Args:
        time (int): Current simulation time
    """

    # execute general processes (aggregation of power values etc.)
    super().step(time)

    # from here on: execute strategy-specific processes
    ...

---

2. Add the strategy and its input to the model_data of the scenarios

examples/data/model_data_scenario/model_data_building.json (01/24)

{
    "ems": [
        {
            "strategy": "HEMS_default",
            "cs_strategy": "balanced"
        }
    ],
    ...

---

3. Add your EMS class to the model_connections/model_connect_config.json file

Add the data that is sent out…

eelib/model_connections/model_connect_config.json (01/24)

"HEMS_default": {
    "PVLib": [["p_set_pv", "p_set"]],
    "BSS": [
        [
            "p_set_storage",
            "p_set"
        ]
    ],
    "ChargingStation": [
        [
            "p_set_charging_station",
            "p_set"
        ]
    ],
    "Heatpump": [
        [
            "p_th_set_heatpump",
            "p_th_set"
        ]
    ],
    "grid_load": [["p_balance", "p_w"], ["q_balance", "q_var"]]
},

… but also to every model, which data is sent to your HEMS!

e.g. but not only the BSS

"BSS": {
    "HEMS_default": [
        [
            "p_discharge_max",
            "p_min"
        ],
        [
            "p_charge_max",
            "p_max"
        ],
        [
            "p",
            "p"
        ]
    ],
    ...
},

---

1. Add the model with its name and (input/output) attributes to the META of the EMS_simulator

eelib/core/control/EMS/EMS_simulator.py (01/24)

META = {
    "type": "hybrid",
    "models": {

        "HEMS_default": {
            "public": True,
            "params": ["init_vals"],
            "attrs": [
                "q",
                "p",
                "p_max",
                "p_min",
                "p_set_storage",
                "p_set_charging_station",
                "p_set_pv",
                "p_balance",
                "q_balance",
                "bss_data",
                "pv_data",
                "cs_data",
                "p_th",
            ],