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test_back_to_orig.py
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test_back_to_orig.py
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# 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.
# do some generic tests that can be implemented directly to test if a backend implementation can work out of the box
# with grid2op.
# see an example of test_Pandapower for how to use this suit.
import unittest
import numpy as np
import warnings
import grid2op
from grid2op.Parameters import Parameters
from grid2op.Action import BaseAction
import pdb
class Test_BackToOrig(unittest.TestCase):
def setUp(self) -> None:
self.env_name = "educ_case14_storage"
param = Parameters()
param.NO_OVERFLOW_DISCONNECTION = True
param.NB_TIMESTEP_COOLDOWN_LINE = 0
param.NB_TIMESTEP_COOLDOWN_SUB = 0
param.ACTIVATE_STORAGE_LOSS = False
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
self.env = grid2op.make(
self.env_name, test=True, action_class=BaseAction, param=param
)
def tearDown(self) -> None:
self.env.close()
def test_substation(self):
obs, reward, done, info = self.env.step(
self.env.action_space({"set_bus": {"substations_id": [(2, (1, 2, 2, 1))]}})
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{"set_bus": {"substations_id": [(5, (1, 2, 2, 1, 2, 1, 1, 2))]}}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "substation" in res
assert len(res["substation"]) == 2
for act in res["substation"]:
lines_impacted, subs_impacted = act.get_topological_impact()
assert subs_impacted[2] ^ subs_impacted[5] # xor
assert np.sum(lines_impacted) == 0
for act in res["substation"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
def test_line(self):
obs = self.env.reset()
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 0
obs, reward, done, info = self.env.step(
self.env.action_space({"set_line_status": [(12, -1)]})
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space({"set_line_status": [(15, -1)]})
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "powerline" in res
assert len(res["powerline"]) == 2
for act in res["powerline"]:
lines_impacted, subs_impacted = act.get_topological_impact()
assert lines_impacted[12] ^ lines_impacted[15] # xor
assert np.sum(subs_impacted) == 0
for act in res["powerline"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
def test_redisp(self):
obs = self.env.reset()
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 0
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"redispatch": [
(0, self.env.gen_max_ramp_up[0]),
(1, -self.env.gen_max_ramp_down[1]),
]
}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "redispatching" in res
assert len(res["redispatching"]) == 1 # one action is enough
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"redispatch": [
(0, self.env.gen_max_ramp_up[0]),
(1, -self.env.gen_max_ramp_down[1]),
]
}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "redispatching" in res
assert len(res["redispatching"]) == 2 # one action is NOT enough
for act in res["redispatching"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
np.max(np.abs(obs.target_dispatch)) <= 1e-6
) # I am in the original topology
assert len(self.env.action_space.get_back_to_ref_state(obs)) == 0
# now try with "non integer" stuff
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"redispatch": [
(0, self.env.gen_max_ramp_up[0]),
(1, -self.env.gen_max_ramp_down[1]),
]
}
)
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"redispatch": [
(0, 0.5 * self.env.gen_max_ramp_up[0]),
(1, -0.5 * self.env.gen_max_ramp_down[1]),
]
}
)
)
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "redispatching" in res
assert len(res["redispatching"]) == 2 # one action is NOT enough
for act in res["redispatching"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
np.max(np.abs(obs.target_dispatch)) <= 1e-6
) # I am in the original topology
# try with non integer, non symmetric stuff
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"redispatch": [
(0, self.env.gen_max_ramp_up[0]),
(1, -self.env.gen_max_ramp_down[1]),
]
}
)
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{"redispatch": [(0, 0.5 * self.env.gen_max_ramp_up[0])]}
)
)
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "redispatching" in res
assert len(res["redispatching"]) == 2 # one action is NOT enough
for act in res["redispatching"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
np.max(np.abs(obs.target_dispatch)) <= 1e-6
) # I am in the original topology
assert len(self.env.action_space.get_back_to_ref_state(obs)) == 0
def test_storage_no_loss(self):
obs = self.env.reset()
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 0
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 1 # one action is enough (no losses)
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 2 # one action is NOT enough
for act in res["storage"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
# now try with "non integer" stuff
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, 0.5 * self.env.storage_max_p_absorb[0]),
(1, -0.5 * self.env.storage_max_p_prod[1]),
]
}
)
)
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 2 # one action is NOT enough
for act in res["storage"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert np.max(np.abs(obs.target_dispatch)) <= 1e-6
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
# try with non integer, non symmetric stuff
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{"set_storage": [(0, 0.5 * self.env.storage_max_p_absorb[0])]}
)
)
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 2 # one action is NOT enough
for act in res["storage"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
def test_storage_with_loss(self):
param = self.env.parameters
param.ACTIVATE_STORAGE_LOSS = True
self.env.change_parameters(param)
obs = self.env.reset()
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 0
# check i get the right power if i do nothing
obs, reward, done, info = self.env.step(self.env.action_space())
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 1 # one action is enough to compensate the losses
assert np.all(
np.abs(res["storage"][0].storage_p - self.env.storage_loss) <= 1e-5
)
# now do some action
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 2 # one action is NOT enough (no losses)
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 3 # two actions are NOT enough (losses)
for act in res["storage"]:
obs, reward, done, info = self.env.step(act)
assert not done
dict_ = self.env.action_space.get_back_to_ref_state(obs)
assert len(dict_) == 1
assert "storage" in dict_
assert np.all(
np.abs(dict_["storage"][0].storage_p - 3.0 * self.env.storage_loss) <= 1e-5
) # I am in the original topology (up to the storage losses)
# now try with "non integer" stuff
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, 0.5 * self.env.storage_max_p_absorb[0]),
(1, -0.5 * self.env.storage_max_p_prod[1]),
]
}
)
)
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 2 # one action is NOT enough
for act in res["storage"]:
obs, reward, done, info = self.env.step(act)
assert not done
dict_ = self.env.action_space.get_back_to_ref_state(obs)
assert len(dict_) == 1
assert "storage" in dict_
assert np.all(
np.abs(dict_["storage"][0].storage_p - 2.0 * self.env.storage_loss) <= 1e-5
) # I am in the original topology (up to the storage losses)
# try with non integer, non symmetric stuff
obs, reward, done, info = self.env.step(
self.env.action_space(
{
"set_storage": [
(0, self.env.storage_max_p_absorb[0]),
(1, -self.env.storage_max_p_prod[1]),
]
}
)
)
assert not done
obs, reward, done, info = self.env.step(
self.env.action_space(
{"set_storage": [(0, 0.5 * self.env.storage_max_p_absorb[0])]}
)
)
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "storage" in res
assert len(res["storage"]) == 2 # one action is NOT enough
for act in res["storage"]:
obs, reward, done, info = self.env.step(act)
assert not done
dict_ = self.env.action_space.get_back_to_ref_state(obs)
assert len(dict_) == 1
assert "storage" in dict_
assert np.all(
np.abs(dict_["storage"][0].storage_p - 2.0 * self.env.storage_loss) <= 1e-5
) # I am in the original topology (up to the storage losses)
def test_curtailment(self):
obs, reward, done, info = self.env.step(
self.env.action_space({"curtail": [(3, 0.05)]})
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "curtailment" in res
assert len(res["curtailment"]) == 1
for act in res["curtailment"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert np.all(obs.curtailment_limit == 1.0)
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
obs, reward, done, info = self.env.step(
self.env.action_space({"curtail": [(3, 0.05)]})
)
obs, reward, done, info = self.env.step(
self.env.action_space({"curtail": [(4, 0.5)]})
)
assert not done
res = self.env.action_space.get_back_to_ref_state(obs)
assert len(res) == 1
assert "curtailment" in res
assert len(res["curtailment"]) == 1
for act in res["curtailment"]:
obs, reward, done, info = self.env.step(act)
assert not done
assert np.all(obs.curtailment_limit == 1.0)
assert (
len(self.env.action_space.get_back_to_ref_state(obs)) == 0
) # I am in the original topology
# TODO test when not all action types are enable (typically the change / set part)
if __name__ == "__main__":
unittest.main()