Class Microgrid

src/pymgrid/microgrid/microgrid.py:18–1092

Microgrid class, used to define and simulate an environment with a variety of modules. Parameters ---------- modules : List[Union[Tuple[str, BaseMicrogridModule], BaseMicrogridModule]] List of modules that define the microgrid. The list can contain either/both microgrid mod

class Microgrid(yaml.YAMLObject):
    """
    Microgrid class, used to define and simulate an environment with a variety of modules.

    Parameters
    ----------
    modules : List[Union[Tuple[str, BaseMicrogridModule], BaseMicrogridModule]]
        List of modules that define the microgrid. The list can contain either/both microgrid modules -- subclasses of
        ``BaseMicrogridModule`` -- and tuples of length two, which must contain a string defining the name of the module
        followed by the module.

        ``Microgrid`` groups modules into lists based on their names. If no name is given (e.g. an element in ``modules``
        is a subclass of ``BaseMicrogridModule`` and not a tuple, then the name is defined to be
        ``module.__class__.name[0]``. Modules are then exposed (within lists) by name as attributes to the microgrid.
        See below for an example.

        .. note::
        The constructor copies modules passed to it.

    add_unbalanced_module : bool, default True.
        Whether to add an unbalanced energy module to your microgrid. Such a module computes and attributes
        costs to any excess supply or demand.
        Set to True unless ``modules`` contains an ``UnbalancedEnergyModule``.

    loss_load_cost : float, default 10.0
        Cost per unit of unmet demand. Ignored if ``add_unbalanced_module=False``.

    overgeneration_cost : float, default 2.0
        Cost per unit of excess generation.  Ignored if ``add_unbalanced_module=False``.

    reward_shaping_func : callable or None, default None
        Function that allows for custom definition of the microgrid's reward/cost.
        If None, the cost of each step is simply the total cost of all modules.
        For example, you may define a function that defines the cost as only being the loss load, or only
        the pv curtailment.

    trajectory_func : callable or None, default None
        Callable that sets an initial and final step for an episode. ``trajectory_func`` must take two inputs:
        :attr:`.initial_step` and :attr:`.final_step`, and return two integers: the initial and final step for
        that particular episode, respectively. This function will be called every time :meth:`.reset` is called.

        If None, :attr:`.initial_step` and :attr:`.final_step` are used to define every episode.


    Examples
    --------
    >>> from pymgrid import Microgrid
    >>> from pymgrid.modules import LoadModule, RenewableModule, GridModule, BatteryModule
    >>> timesteps = 10
    >>> load = LoadModule(10*np.random.rand(timesteps), loss_load_cost=10.)
    >>> pv = RenewableModule(10*np.random.rand(timesteps))
    >>> grid = GridModule(max_import=100, max_export=10, time_series=np.random.rand(timesteps, 3))
    >>> battery_0 = BatteryModule(min_capacity=0, \
                                  max_capacity=100, \
                                  max_charge=1,\
                                  max_discharge=10, \
                                  efficiency=0.9, \
                                  init_soc=0.5)