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test_forecast_from_arrays.py
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test_forecast_from_arrays.py
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# Copyright (c) 2023, 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 grid2op
import unittest
import warnings
import numpy as np
import pdb
class TestForecastFromArrays(unittest.TestCase):
def setUp(self) -> None:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
self.env = grid2op.make("l2rpn_case14_sandbox", test=True, _add_to_name=type(self).__name__)
return super().setUp()
def tearDown(self) -> None:
self.env.close()
return super().tearDown()
def test_basic_behaviour(self):
obs = self.env.reset()
# test the max step
for nb_ts in [1, 3, 10, 30]:
load_p_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
load_q_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
gen_p_forecasted = np.tile(obs.gen_p, nb_ts).reshape(nb_ts, -1)
gen_v_forecasted = np.tile(obs.gen_v, nb_ts).reshape(nb_ts, -1)
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted)
sim_obs = forcast_env.reset()
assert sim_obs.max_step == nb_ts + 1
# test with some actions
sim_obs1, reward, done, info = forcast_env.step(self.env.action_space())
assert sim_obs1.max_step == nb_ts + 1
sim_obs2, reward, done, info = forcast_env.step(self.env.action_space())
assert sim_obs2.max_step == nb_ts + 1
sim_obs3, reward, done, info = forcast_env.step(self.env.action_space())
assert sim_obs3.max_step == nb_ts + 1
def test_missing_gen_v(self):
obs = self.env.reset()
nb_ts = 5
# test the max step
load_p_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
load_q_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
gen_p_forecasted = np.tile(obs.gen_p, nb_ts).reshape(nb_ts, -1)
gen_v_forecasted = None
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted)
sim_obs = forcast_env.reset()
assert (sim_obs.gen_v == obs.gen_v).all()
sim_obs1, reward, done, info = forcast_env.step(self.env.action_space())
def test_missing_gen_p(self):
obs = self.env.reset()
nb_ts = 5
# test the max step
load_p_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
load_q_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
gen_p_forecasted = None
gen_v_forecasted = np.tile(obs.gen_v, nb_ts).reshape(nb_ts, -1)
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted)
sim_obs = forcast_env.reset()
assert (sim_obs.gen_p == obs.gen_p).all()
sim_obs1, reward, done, info = forcast_env.step(self.env.action_space())
def test_missing_load_p(self):
obs = self.env.reset()
nb_ts = 5
# test the max step
load_p_forecasted = None
load_q_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
gen_p_forecasted = np.tile(obs.gen_p, nb_ts).reshape(nb_ts, -1)
gen_v_forecasted = np.tile(obs.gen_v, nb_ts).reshape(nb_ts, -1)
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted)
sim_obs = forcast_env.reset()
assert (sim_obs.load_p == obs.load_p).all()
sim_obs1, reward, done, info = forcast_env.step(self.env.action_space())
def test_missing_load_q(self):
obs = self.env.reset()
nb_ts = 5
# test the max step
load_p_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
load_q_forecasted = None
gen_p_forecasted = np.tile(obs.gen_p, nb_ts).reshape(nb_ts, -1)
gen_v_forecasted = np.tile(obs.gen_v, nb_ts).reshape(nb_ts, -1)
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted)
sim_obs = forcast_env.reset()
assert (sim_obs.load_q == obs.load_q).all()
sim_obs1, reward, done, info = forcast_env.step(self.env.action_space())
def test_all_missing(self):
obs = self.env.reset()
# test the max step
load_p_forecasted = None
load_q_forecasted = None
gen_p_forecasted = None
gen_v_forecasted = None
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted)
sim_obs = forcast_env.reset()
assert sim_obs.max_step == 1
def test_all_without_maintenance(self):
obs = self.env.reset()
nb_ts = 5
# test the max step
load_p_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
load_q_forecasted = np.tile(obs.load_p, nb_ts).reshape(nb_ts, -1)
gen_p_forecasted = np.tile(obs.gen_p, nb_ts).reshape(nb_ts, -1)
gen_v_forecasted = np.tile(obs.gen_v, nb_ts).reshape(nb_ts, -1)
forcast_env = obs.get_env_from_external_forecasts(load_p_forecasted,
load_q_forecasted,
gen_p_forecasted,
gen_v_forecasted,
with_maintenance=False)
sim_obs = forcast_env.reset()
assert sim_obs.max_step == nb_ts + 1
if __name__ == "__main__":
unittest.main()