Source code for grid2op.Reward.economicReward

# 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.Exceptions import Grid2OpException
from grid2op.Reward.baseReward import BaseReward
from grid2op.dtypes import dt_float

[docs]class EconomicReward(BaseReward): """ This reward computes the marginal cost of the powergrid. As RL is about maximising a reward, while we want to minimize the cost, this class also ensures that: - the reward is positive if there is no game over, no error etc. - the reward is inversely proportional to the cost of the grid (the higher the reward, the lower the economic cost). Examples --------- You can use this reward in any environment with: .. code-block:: python import grid2op from grid2op.Reward import EconomicReward # then you create your environment with it: NAME_OF_THE_ENVIRONMENT = "l2rpn_case14_sandbox" env = grid2op.make(NAME_OF_THE_ENVIRONMENT,reward_class=EconomicReward) # and do a step with a "do nothing" action obs = env.reset() obs, reward, done, info = env.step(env.action_space()) # the reward is computed with the EconomicReward class """
[docs] def __init__(self, logger=None): BaseReward.__init__(self, logger=logger) self.reward_min = dt_float(0.0) self.reward_max = dt_float(1.0) self.worst_cost = None
[docs] def initialize(self, env): if not env.redispatching_unit_commitment_availble: raise Grid2OpException( "Impossible to use the EconomicReward reward with an environment without generators" "cost. Please make sure env.redispatching_unit_commitment_availble is available." ) self.worst_cost = dt_float((env.gen_cost_per_MW * env.gen_pmax).sum() * env.delta_time_seconds / 3600.0)
[docs] def __call__(self, action, env, has_error, is_done, is_illegal, is_ambiguous): if has_error or is_illegal or is_ambiguous: res = self.reward_min else: # compute the cost of the grid res = dt_float((env.get_obs(_do_copy=False).prod_p * env.gen_cost_per_MW).sum() * env.delta_time_seconds / 3600.0) # we want to minimize the cost by maximizing the reward so let's take the opposite res *= dt_float(-1.0) # to be sure it's positive, add the highest possible cost res += self.worst_cost res = np.interp( res, [dt_float(0.0), self.worst_cost], [self.reward_min, self.reward_max] ) return dt_float(res)