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test_RewardNewRenewableSourcesUsageScore.py
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test_RewardNewRenewableSourcesUsageScore.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 warnings
import numpy as np
import unittest
import grid2op
from grid2op.Reward import _NewRenewableSourcesUsageScore
from grid2op.Agent import DoNothingAgent, BaseAgent
class CurtailTrackerAgent(BaseAgent):
def __init__(self, action_space, gen_renewable, gen_pmax, curtail_level=1.):
super().__init__(action_space)
self.gen_renewable = gen_renewable
self.gen_pmax = gen_pmax[gen_renewable]
self.curtail_level = curtail_level
def act(self, obs, reward, done):
curtail_target = self.curtail_level * obs.gen_p_before_curtail[self.gen_renewable] / self.gen_pmax
act = self.action_space(
{"curtail": [(el, ratio) for el, ratio in zip(np.arange(len(self.gen_renewable))[self.gen_renewable], curtail_target)]}
)
return act
class DoNothingSimulatorAgent(DoNothingAgent):
def __init__(self, action_space, nres_id, gen_pmax):
super().__init__(action_space)
self.nres_id = nres_id
self.gen_pmax = gen_pmax
def act(self, obs, reward, done):
curtail_target = 0.5 * obs.gen_p_before_curtail[self.nres_id] / self.gen_pmax[self.nres_id]
act = self.action_space(
{"curtail": [(el, ratio) for el, ratio in zip(self.nres_id, curtail_target)]}
)
sim_obs_1, *_ = obs.simulate(act, time_step=1)
return super().act(obs, reward, done)
class TestJustGameOver(unittest.TestCase):
def setUp(self) -> None:
env_name = "l2rpn_case14_sandbox"
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
self.env = grid2op.make(env_name,
reward_class=_NewRenewableSourcesUsageScore,
test=True,
_add_to_name=type(self).__name__
)
self.env.set_max_iter(20)
self.env.parameters.NO_OVERFLOW_DISCONNECTION = True
self.nres_id = np.arange(self.env.n_gen)[self.env.gen_renewable]
def tearDown(self) -> None:
self.env.close()
return super().tearDown()
def test_when_no_step(self):
obs = self.env.reset()
with warnings.catch_warnings():
warnings.filterwarnings("error")
obs, reward, done, info = self.env.step(self.env.action_space({"set_bus": {"loads_id": [(0, -1)]}}))
assert done
assert reward == 1., f"{reward:.2f} vs 1."
class TestNewRenewableSourcesUsageScore(unittest.TestCase):
def setUp(self) -> None:
env_name = "l2rpn_case14_sandbox"
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
self.env = grid2op.make(env_name,
reward_class = _NewRenewableSourcesUsageScore,
test=True,
_add_to_name=type(self).__name__
)
self.env.set_max_iter(20)
self.env.parameters.NO_OVERFLOW_DISCONNECTION = True
self.nres_id = np.arange(self.env.n_gen)[self.env.gen_renewable]
def tearDown(self) -> None:
self.env.close()
return super().tearDown()
def test_surlinear_function(self):
#for recalls, use nres_ratio percentages between 50 and 100
delta_x = 0.5
x = np.arange(start=50, stop=100, step=delta_x)
f_x = _NewRenewableSourcesUsageScore._surlinear_func_curtailment(x)
gradient_f = (f_x[1:] - f_x[:-1]) / delta_x
assert all(gradient_f > 1 / 50)
assert all(np.equal(np.argsort(gradient_f),np.arange(len(gradient_f), dtype=int)))
def test_capitalization_score(self):
my_agent = DoNothingAgent(self.env.action_space)
done = False
reward = self.env.reward_range[0]
gen_res_p_curtailed_array = np.zeros(self.env.chronics_handler.max_timestep())
gen_res_p_before_curtail_array = np.zeros(self.env.chronics_handler.max_timestep())
obs = self.env.reset()
while True:
gen_res_p_curtailed_array[self.env.nb_time_step] = np.sum(obs.gen_p[self.env.gen_renewable])
gen_res_p_before_curtail_array[self.env.nb_time_step] = np.sum(obs.gen_p_before_curtail[self.env.gen_renewable])
action = my_agent.act(obs, reward, done)
obs, reward, done, _ = self.env.step(action)
if done:
break
return all(
[
np.array_equal(self.env._reward_helper.template_reward.gen_res_p_curtailed_list, gen_res_p_curtailed_array),
np.array_equal(self.env._reward_helper.template_reward.gen_res_p_before_curtail_list, gen_res_p_before_curtail_array),
reward == _NewRenewableSourcesUsageScore._surlinear_func_curtailment(100 * np.sum(gen_res_p_curtailed_array[1:]) / np.sum(gen_res_p_before_curtail_array[1:]))
]
)
def test_reward_after_blackout(self):
for blackout_time_step in [1,3,10]:
my_agent = DoNothingAgent(self.env.action_space)
done = False
reward = self.env.reward_range[0]
obs = self.env.reset()
while True:
if self.env.nb_time_step + 1 > blackout_time_step:
blackout_act = {"set_bus": {"generators_id": (0,-1)}}
action = self.env.action_space(blackout_act)
else:
action = my_agent.act(obs, reward, done)
obs, reward, done, _ = self.env.step(action)
if done:
break
assert reward == 1.
def test_reward_value(self):
for curtail_target, ratio_curtail_expected in [
(0.5, 50.84402431116107),
(0.65, 66.09722918973647),
(0.8, 81.35044050770632),
(0.9, 91.51924150630187),
(1., 99.96623954270511)
]:
my_agent = CurtailTrackerAgent(self.env.action_space,
gen_renewable = self.env.gen_renewable,
gen_pmax=self.env.gen_pmax,
curtail_level = curtail_target)
self.env.seed(0)
self.env.set_id(0)
obs = self.env.reset()
done = False
reward = self.env.reward_range[0]
while True:
action = my_agent.act(obs, reward, done)
obs, reward, done, _ = self.env.step(action)
if done:
break
assert reward == _NewRenewableSourcesUsageScore._surlinear_func_curtailment(ratio_curtail_expected)
def test_simulate_ignored(self):
my_agent = DoNothingSimulatorAgent(self.env.action_space,
nres_id = np.arange(self.env.n_gen)[self.env.gen_renewable],
gen_pmax=self.env.gen_pmax,)
done = False
reward = self.env.reward_range[0]
obs = self.env.reset()
while True:
action = my_agent.act(obs, reward, done)
obs, reward, done, _ = self.env.step(action)
if done:
break
return reward == 1.
def test_simulate_blackout_ignored(self):
obs = self.env.reset()
obs, reward, done, _ = self.env.step(self.env.action_space())
go_act = self.env.action_space({"set_bus": {"generators_id": [(0, -1)]}})
simO, simr, simd, simi = obs.simulate(go_act)
assert simr == 0., f"{simr} vs 0."
assert simd
def test_simulated_env(self):
obs = self.env.reset()
f_env = obs.get_forecast_env()
forD = False
while not forD:
forO, forR, forD, forI = f_env.step(self.env.action_space())
assert forR == 0.
if __name__ == "__main__":
unittest.main()