# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# 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 http://mozilla.org/MPL/2.0/.
# 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)