/
test_diff_backend.py
207 lines (174 loc) · 8.78 KB
/
test_diff_backend.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
# Copyright (c) 2019-2023, 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 copy
import grid2op
from grid2op.Agent import BaseAgent
from grid2op.Backend import PandaPowerBackend
from grid2op.Runner import Runner
import unittest
import warnings
import numpy as np
import pdb
class ModifPPBackend(PandaPowerBackend):
pass
class RememberRX(BaseAgent):
def act(self, observation, reward, done=False):
self._x = 1.0 * observation._obs_env.backend._grid.line["x_ohm_per_km"]
self._r = 1.0 * observation._obs_env.backend._grid.line["r_ohm_per_km"]
self._detailed_infos_for_cascading_failures = observation._obs_env.backend.detailed_infos_for_cascading_failures
self._lightsim2grid = observation._obs_env.backend._lightsim2grid
self._max_iter = observation._obs_env.backend._max_iter
self._bk = observation._obs_env.backend
return self.action_space()
def __copy__(self):
# prevent copy
raise copy.Error
class Case14DiffGridTester(unittest.TestCase):
def setUp(self) -> None:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# this needs to be tested with pandapower backend
self.env = grid2op.make("l2rpn_case14_sandbox_diff_grid", test=True)
self.env.seed(0)
self.env.set_id(0)
def test_backend_different(self):
assert self.env.backend._grid is not self.env.observation_space._backend_obs
x_orig = self.env.backend._grid.line["x_ohm_per_km"]
x_modif = self.env.observation_space._backend_obs._grid.line["x_ohm_per_km"]
assert np.all(x_orig != x_modif)
r_orig = self.env.backend._grid.line["r_ohm_per_km"]
r_modif = self.env.observation_space._backend_obs._grid.line["r_ohm_per_km"]
assert np.all(r_orig != r_modif)
def test_simulate(self):
obs_init = self.env.reset()
loadp_next = [22.1, 89. , 45.9, 6.9, 12.3, 28.4, 8.8, 3.4, 5.5, 12.9, 15.2]
loadq_next = [15.5, 62.1, 31.2, 4.9, 8.4, 19.8, 6.1, 2.4, 3.8, 8.9, 10.6]
genp_next = [73.3 , 72.6 , 36.6 , 0. , 0. , 71.5158]
act = self.env.action_space()
# set the observation to the right values (for the forecast)
obs_init._forecasted_inj[1][1]["injection"]["load_p"] = np.array(loadp_next).astype(np.float32)
obs_init._forecasted_inj[1][1]["injection"]["load_q"] = np.array(loadq_next).astype(np.float32)
obs_init._forecasted_inj[1][1]["injection"]["prod_p"] = np.array(genp_next).astype(np.float32)
if 1 in obs_init._forecasted_grid_act:
del obs_init._forecasted_grid_act[1]
sim_obs, sim_r, simd, sim_i = obs_init.simulate(act)
obs, reward, done, info = self.env.step(act)
# inputs are the same
assert np.all(sim_obs.load_p == obs.load_p)
assert np.all(sim_obs.load_q == obs.load_q)
assert np.all(sim_obs.gen_p[:-1] == obs.gen_p[:-1]) # all equals except the slack
# and now check the outputs of the backend are different
assert sim_obs.gen_p[-1] != obs.gen_p[-1] # slack different
assert np.all(sim_obs.p_or != obs.p_or)
assert np.all(sim_obs.q_or != obs.q_or)
assert np.all(sim_obs.a_or != obs.a_or)
def test_simulator(self):
obs = self.env.reset()
sim = obs.get_simulator()
assert self.env.backend._grid is not sim.backend
x_orig = self.env.backend._grid.line["x_ohm_per_km"]
x_modif = sim.backend._grid.line["x_ohm_per_km"]
assert np.all(x_orig != x_modif)
r_orig = self.env.backend._grid.line["r_ohm_per_km"]
r_modif = sim.backend._grid.line["r_ohm_per_km"]
assert np.all(r_orig != r_modif)
def test_forecasted_env(self):
obs = self.env.reset()
act = self.env.action_space()
for_env = obs.get_forecast_env()
assert self.env.backend._grid is not for_env.backend
x_orig = self.env.backend._grid.line["x_ohm_per_km"]
x_modif = for_env.backend._grid.line["x_ohm_per_km"]
assert np.all(x_orig != x_modif)
r_orig = self.env.backend._grid.line["r_ohm_per_km"]
r_modif = for_env.backend._grid.line["r_ohm_per_km"]
assert np.all(r_orig != r_modif)
sim_obs, sim_r, simd, sim_i = obs.simulate(act)
for_obs, for_r, for_d, for_i = for_env.step(act)
assert np.all(sim_obs.a_or == for_obs.a_or)
def test_thermal_limit(self):
obs = self.env.reset()
sim = obs.get_simulator()
for_env = obs.get_forecast_env()
assert np.all(sim.backend.get_thermal_limit() == self.env.get_thermal_limit())
assert np.all(for_env.get_thermal_limit() == self.env.get_thermal_limit())
assert np.all(obs._obs_env.get_thermal_limit() == self.env.get_thermal_limit())
new_th_lim = 2.0 * self.env.get_thermal_limit()
self.env.set_thermal_limit(new_th_lim)
obs = self.env.reset()
sim = obs.get_simulator()
for_env = obs.get_forecast_env()
assert np.all(sim.backend.get_thermal_limit() == new_th_lim)
assert np.all(for_env.get_thermal_limit() == new_th_lim)
assert np.all(obs._obs_env.get_thermal_limit() == new_th_lim)
def test_runner(self):
agent = RememberRX(self.env.action_space)
runner = Runner(**self.env.get_params_for_runner(), agentInstance=agent, agentClass=None)
_ = runner.run(nb_episode=1, max_iter=1)
obs = self.env.reset()
assert hasattr(agent, "_r")
assert np.all(agent._r == obs._obs_env.backend._grid.line["r_ohm_per_km"])
assert np.all(agent._r != self.env.backend._grid.line["r_ohm_per_km"])
assert hasattr(agent, "_x")
assert np.all(agent._x == obs._obs_env.backend._grid.line["x_ohm_per_km"])
assert np.all(agent._x != self.env.backend._grid.line["x_ohm_per_km"])
class Case14DiffGridCopyTester(Case14DiffGridTester):
def setUp(self) -> None:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# this needs to be tested with pandapower backend
self.aux_env = grid2op.make("l2rpn_case14_sandbox_diff_grid", test=True)
self.env = self.aux_env.copy()
self.env.seed(0)
self.env.set_id(0)
class DiffGridMakeTester(unittest.TestCase):
def _aux_check_bk_kwargs(self, bk):
assert bk._lightsim2grid
assert bk._max_iter == 15
def _aux_check_different_stuff(self, env, fun_bk):
obs = env.reset()
fun_bk(obs._obs_env.backend)
# copy
env_cpy = env.copy()
obs_cpy = env_cpy.reset()
fun_bk(obs_cpy._obs_env.backend)
# runner
agent = RememberRX(env.action_space)
runner = Runner(**env.get_params_for_runner(), agentInstance=agent, agentClass=None)
_ = runner.run(nb_episode=1, max_iter=1)
fun_bk(agent)
# forecasted_env
for_env = obs.get_forecast_env()
fun_bk(for_env.backend)
# simulator
sim = obs.get_simulator()
fun_bk(sim.backend)
def test_bk_kwargs(self) -> None:
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# this needs to be tested with pandapower backend
env = grid2op.make("l2rpn_case14_sandbox_diff_grid", test=True,
observation_backend_kwargs={"max_iter": 15,
"lightsim2grid": True})
self._aux_check_different_stuff(env, self._aux_check_bk_kwargs)
def _aux_bk_class(self, bk):
if isinstance(bk, PandaPowerBackend):
# subtlety: it can be called with an agent for the test of runner...
assert isinstance(bk, ModifPPBackend)
else:
# in this case "bk" is in fact an agent...
assert isinstance(bk._bk, ModifPPBackend)
def test_bk_class(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# this needs to be tested with pandapower backend
env = grid2op.make("l2rpn_case14_sandbox_diff_grid", test=True,
observation_backend_class=ModifPPBackend)
self._aux_check_different_stuff(env, self._aux_bk_class)
if __name__ == "__main__":
unittest.main()