Source code for grid2op.Agent.deltaRedispatchRandomAgent

# Copyright (c) 2019-2020, RTE (
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.

import numpy as np
from grid2op.Agent.baseAgent import BaseAgent

[docs]class DeltaRedispatchRandomAgent(BaseAgent): """ INTERNAL .. warning:: /!\\\\ Internal, do not use unless you know what you are doing /!\\\\ Used for test. Prefer using a random agent by selecting only the redispatching action that you want. This agent will perform some redispatch of a given amount among randomly selected dispatchable generators. Parameters ---------- action_space: :class:`grid2op.Action.ActionSpace` the Grid2Op action space n_gens_to_redispatch: `int` The maximum number of dispatchable generators to play with redispatching_delta: `float` The redispatching MW value used in both directions """ def __init__(self, action_space, n_gens_to_redispatch=2, redispatching_delta=1.0): super().__init__(action_space) self.desired_actions = [] # Get all generators IDs gens_ids = np.arange(self.action_space.n_gen, dtype=int) # Filter out non resipatchable IDs gens_redisp = gens_ids[self.action_space.gen_redispatchable] # Cut if needed if len(gens_redisp) > n_gens_to_redispatch: gens_redisp = gens_redisp[0:n_gens_to_redispatch] # Register do_nothing action self.desired_actions.append(self.action_space({})) # Register 2 actions per generator # (increase or decrease by the delta) for gen_id in gens_redisp: # Create action redispatch by opposite delta act1 = self.action_space( {"redispatch": [(gen_id, -float(redispatching_delta))]} ) # Create action redispatch by delta act2 = self.action_space( {"redispatch": [(gen_id, float(redispatching_delta))]} ) # Register this generator actions self.desired_actions.append(act1) self.desired_actions.append(act2)
[docs] def act(self, observation, reward, done=False): act = self.space_prng.choice(self.desired_actions) return act