Implementing an Operating Strategy

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

Add your strategy to the hems_model package

The convention is to use one file and one class per strategy, named in the following way. hems_<strategy name>.py contains the class HEMS_<strategy name>. E.g. the Operating Strategy for user-based multi-use is abbreviated as “mu_user” from which follows that the file hems_mu_user.py contains only the class HEMS_mu_user(HEMS).

You also have to list your strategy in the package to expose it to the simulator, by adding

from .hems_<strategy name> import HEMS_<strategy name>

to the hems_model/__init__.py.

Inherit existing methods and add your behaviour

Let your class inherit from the base class HEMS. You can also inherit from subclasses of the HEMS class like HEMS_default to gain access to their methods and properties.

eelib/core/control/hems/hems_model/hems_default.py (12/24)

class HEMS_default(HEMS):
    """Default strategy for Energy Management System.
    Should be copied and adapted for the use of a specific HEMS 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, eid: str, step_size: int = 900, **kwargs):
        """Initializes the eELib HEMS default model.

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

        # call init function of super HEMS class
        super().__init__(eid=eid, 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/hems/hems_model/hems_default.py (12/24)

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

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

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

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

---

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

If you want to use your strategy in an (example) scenario, add a HEMS in the following way.

examples/data_input/model_data_scenario/model_data_building.json (12/24)

{
    "HEMS_default": {
        "HEMS_default_0": {
            "cs_strategy": "max_p",
            "bss_strategy": "surplus"
        }
    },
    "HouseholdCSV": {
        "HouseholdCSV_0": {
    ...

---

Add your connections to the eelib/model_connections/model_connect_config.json

Add the data that is sent out…

eelib/model_connections/model_connect_config.json (12/24)

    "HEMS_default": {
        "PVLib": [
            [
                "p_set_pv",
                "p_set"
            ]
        ],
        "PVLibExact": [
            [
                "p_set_pv",
                "p_set"
            ]
        ],
        "BSS": [
            [
                "p_set_storage",
                "p_set"
            ]
        ],
        ...

… 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"
        ]
    ],
    ...
},

Hint

Check all entries of "HEMS_default":. You likely need all these connections for your strategy as well, extended by your specific additions.

---

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

eelib/core/control/hems/hems_simulator.py (12/24)

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

         "HEMS_default": {
             "public": True,
             "params": ["init_vals"],
             "attrs": [
                 "q",
                 "p",
                 "p_max",
                 "p_min",
                 "p_set_storage",
                 ...