pymgrid.envs.DiscreteMicrogridEnv#

class pymgrid.envs.DiscreteMicrogridEnv(modules, add_unbalanced_module=True, loss_load_cost=10, overgeneration_cost=2, reward_shaping_func=None, trajectory_func=None, flat_spaces=True, observation_keys=None, remove_redundant_gensets=True, step_callback=None, reset_callback=None)[source]#

A discrete env that implements priority lists as actions on a microgrid.

The environment deploys fixed controllable modules to the extent necessary to zero out the net load (load minus renewable generation).

Methods

`close`()	Override close in your subclass to perform any necessary cleanup.
`compute_net_load`([normalized])	Compute the net load at the current step.
`convert_action`(action)	Convert a reinforcement learning action to a microgrid control.
`deserialize`(mapping)
`dump`([stream])	Save a microgrid to a YAML buffer.
`flatten_obs`(observation_space, obs)
`from_microgrid`(microgrid, **kwargs)	Construct an RL environment from a microgrid.
`from_nonmodular`(nonmodular, **kwargs)	Convert to Microgrid from old-style NonModularMicrogrid.
`from_normalized`(data_dict[, act, obs])	De-normalize an action or observation.
`from_scenario`([microgrid_number])	Load one of the pymgrid25 benchmark microgrids.
`get_cost_info`()
`get_empty_action`([sample_flex_modules])	Get an action for the microgrid with no values set.
`get_forecast_horizon`()	Get the forecast horizon of timeseries modules contained in the microgrid.
`get_log`([as_frame, drop_singleton_key, ...])	Collect a log of controls and responses of the microgrid.
`get_priority_lists`(remove_redundant_gensets)	Get all of the priority lists for the microgrid.
`load`(stream)	Load a microgrid from a yaml buffer.
`potential_observation_keys`()
`remove_action`(action_number)	Remove an action from the action space.
`reset`()	Reset the microgrid and flush the log.
`run`(control[, normalized])
`sample_action`([strict_bound, ...])	Get a random action within the microgrid's action space.
`set_forecaster`(forecaster[, ...])	Set the forecaster for timeseries modules in the microgrid.
`set_module_attrs`([attr_dict])	Set the value of an attribute in all modules containing that attribute.
`state_dict`([normalized, as_run_output, _initial])	State of the microgrid as a dict.
`state_series`([normalized])	State of the microgrid as a pandas Series.
`step`(action)	Run one timestep of the environment's dynamics.
`to_nonmodular`()	Convert Microgrid to old-style NonModularMicrogrid.
`to_normalized`(data_dict[, act, obs])	Normalize an action or observation.
`verbose_eq`(other[, indent])

Attributes

`action_space`	Space object corresponding to valid actions.
`controllable`	Container of all controllable modules in the microgrid.
`current_step`	Current step of underlying modules.
`final_step`	Final step of underlying timeseries data.
`fixed`	Container of all fixed modules in the microgrid.
`flat_spaces`	Whether the environment's spaces are flat.
`flex`	Container of all flex modules in the microgrid.
`initial_step`	Initial step at which to start underlying timeseries data.
`log`	Microgrid's log as a DataFrame.
`module_list`	List of all modules in the microgrid.
`modules`	View of the module container.
`n_modules`	Number of modules in the microgrid.
`np_random`	Returns the environment's internal `_np_random` that if not set will initialise with a random seed.
`observation_space`	Space object corresponding to valid observations.
`yaml_tag`	Tag used for yaml serialization.
`actions_list`	List of priority lists.