/
utils_benchmark.py
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/
utils_benchmark.py
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# Copyright (c) 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 LightSim2grid, LightSim2grid a implements a c++ backend targeting the Grid2Op platform.
import time
import numpy as np
from tqdm import tqdm
import argparse
from grid2op.Agent import AgentWithConverter
from grid2op.Converter import IdToAct
class ProfileAgent(AgentWithConverter):
def __init__(self,
action_space,
env_name,
action_space_converter=IdToAct,
**kwargs_converter
):
AgentWithConverter.__init__(self, action_space, action_space_converter=action_space_converter, **kwargs_converter)
self.action_space.all_actions = []
# do nothing
all_actions_tmp = [action_space()]
# powerline switch: disconnection
for i in range(action_space.n_line):
if env_name == "rte_case14_realistic":
if i == 18:
continue
elif env_name == "rte_case5_example":
pass
elif env_name == "rte_case118_example" or env_name.startswith("l2rpn_neurips_2020_track2"):
if i == 6:
continue
if i == 26:
continue
if i == 72:
continue
if i == 73:
continue
if i == 80:
continue
if i == 129:
continue
if i == 140:
continue
if i == 176:
continue
if i == 177:
continue
elif env_name == "l2rpn_wcci_2020":
if i == 2:
continue
all_actions_tmp.append(action_space.disconnect_powerline(line_id=i))
# other type of actions
all_actions_tmp += action_space.get_all_unitary_topologies_set(action_space)
# self.action_space.all_actions += action_space.get_all_unitary_redispatch(action_space)
if env_name == "rte_case14_realistic":
# remove action that makes the powerflow diverge
breaking_acts = [action_space({"set_bus": {"lines_or_id": [(7,2), (8,1), (9,1)],
"lines_ex_id": [(17,2)],
"generators_id": [(2,2)],
"loads_id": [(4,1)]}}),
action_space({"set_bus": {"lines_or_id": [(10, 2), (11, 1), (19,2)],
"lines_ex_id": [(16, 2)],
"loads_id": [(5, 1)]}}),
action_space({"set_bus": {"lines_or_id": [(5, 1)],
"lines_ex_id": [(2, 2)],
"generators_id": [(1, 2)],
"loads_id": [(1, 1)]}}),
action_space({"set_bus": {"lines_or_id": [(6, 2), (15, 2), (16, 1)],
"lines_ex_id": [(3, 2), (5, 2)],
"loads_id": [(2, 1)]}}),
action_space({"set_bus": {"lines_or_id": [(18, 1)],
"lines_ex_id": [(15, 2), (19, 2)],
}})
]
elif env_name == "rte_case118_example" or env_name.startswith("l2rpn_neurips_2020_track2"):
breaking_acts = [action_space({"set_bus": {"lines_or_id": [(100, 2), (129, 1), (173, 2)],
# "lines_ex_id": [(17,2)],
"generators_id": [(2, 2)],
"loads_id": [(6, 1)]
}}),
action_space({"set_bus": {"lines_or_id": [(100, 2), (129, 1), (173, 2)],
# "lines_ex_id": [(17,2)],
"generators_id": [(2, 2)],
"loads_id": [(6, 2)]
}}),
action_space({"set_bus": {"lines_or_id": [(100, 2), (129, 1), (173, 2)],
# "lines_ex_id": [(17,2)],
"generators_id": [(2, 1)],
"loads_id": [(6, 1)]
}}),
action_space({"set_bus": {"lines_or_id": [(140, 1)],
"lines_ex_id": [(129, 2)],
# "generators_id": [(2, 1)],
# "loads_id": [(6, 1)]
}}),
action_space({"set_bus": {"lines_or_id": [(57, 2), (80, 1), (83, 2)],
"lines_ex_id": [(2, 2), (13, 2), (24, 2), (35, 2)],
"generators_id": [(6, 2)],
"loads_id": [(8, 2)]
}}),
action_space({"set_bus": {"lines_or_id": [(57, 2), (80, 1), (83, 2)],
"lines_ex_id": [(2, 2), (13, 2), (24, 2), (35, 2)],
"generators_id": [(6, 2)],
"loads_id": [(8, 1)]
}}),
action_space({"set_bus": {"lines_or_id": [(57, 2), (80, 1), (83, 2)],
"lines_ex_id": [(2, 2), (13, 2), (24, 2), (35, 2)],
"generators_id": [(6, 1)],
"loads_id": [(8, 2)]
}}),
action_space({"set_bus": {"lines_or_id": [(57, 2), (80, 1), (83, 2)],
"lines_ex_id": [(2, 2), (13, 2), (24, 2), (35, 2)],
"generators_id": [(6, 1)],
"loads_id": [(8, 1)]
}}),
action_space({"set_bus": {"lines_or_id": [(100, 2), (129, 1), (173, 2)],
# "lines_ex_id": [(2, 2), (13, 2), (24, 2), (35, 2)],
"generators_id": [(2, 1)],
"loads_id": [(6, 2)]
}}),
]
elif env_name == "l2rpn_wcci_2020":
breaking_acts = [action_space({"set_bus": {"lines_or_id": [(5, 2), (6, 2)],
"lines_ex_id": [(1, 2), (2, 1), (4, 2), (55, 2)],
# "generators_id": [(2, 2)],
# "loads_id": [(6, 1)]
}}),
]
else:
breaking_acts = [action_space({"set_bus": {"lines_or_id": [(0,2), (1,2), (2,2), (3,1)],
"generators_id": [(0,1)],
"loads_id": [(0,1)]}}),
]
# filter out actions that break everything
all_actions = []
for el in all_actions_tmp:
if not el in breaking_acts:
all_actions.append(el)
# set the action to the action space
self.action_space.all_actions = all_actions
# add the action "reset everything to bus 1"
self.action_space.all_actions.append(action_space({"set_bus": np.ones(action_space.dim_topo, dtype=np.int),
"set_line_status": np.ones(action_space.n_line,
dtype=np.int)}))
def print_res(env_klu, env_pp,
nb_ts_klu, nb_ts_pp,
time_klu, time_pp,
aor_klu, aor_pp,
gen_p_klu, gen_p_pp,
gen_q_klu, gen_q_pp):
print("Overall speed-up of KLU vs pandapower (for grid2opbackend) {:.2f}\n".format(time_pp / time_klu))
print("PyKLU Backend {} time steps in {}s ({:.2f} it/s)".format(nb_ts_klu, time_klu, nb_ts_klu/time_klu))
print("\tTime apply act: {:.2f}ms".format(1000. * env_klu._time_apply_act / nb_ts_klu))
print("\tTime powerflow: {:.2f}ms".format(1000. * env_klu._time_powerflow / nb_ts_klu))
print("\tTime extract observation: {:.2f}ms".format(1000. * env_klu._time_extract_obs / nb_ts_klu))
print("Pandapower Backend {} time steps in {}s ({:.2f} it/s)".format(nb_ts_pp, time_pp, nb_ts_pp/time_pp))
print("\tTime apply act: {:.2f}ms".format(1000. * env_pp._time_apply_act / nb_ts_pp))
print("\tTime powerflow: {:.2f}ms".format(1000. * env_pp._time_powerflow / nb_ts_pp))
print("\tTime extract observation: {:.2f}ms".format(1000. * env_pp._time_extract_obs / nb_ts_pp))
print("Absolute value of the difference for aor: {}".format(np.max(np.abs(aor_klu - aor_pp))))
print("Absolute value of the difference for gen_p: {}".format(np.max(np.abs(gen_p_klu - gen_p_pp))))
print("Absolute value of the difference for gen_q: {}".format(np.max(np.abs(gen_q_klu - gen_q_pp))))
def run_env(env, max_ts, agent):
nb_rows = min(env.chronics_handler.max_timestep(), max_ts)
aor = np.zeros((nb_rows, env.n_line))
gen_p = np.zeros((nb_rows, env.n_gen))
gen_q = np.zeros((nb_rows, env.n_gen))
obs = env.get_obs()
done = False
reward = env.reward_range[0]
nb_ts = 0
prev_act = None
beg_ = time.time()
with tqdm(total=nb_rows) as pbar:
while not done:
act = agent.act(obs, reward, done)
obs, reward, done, info = env.step(act)
aor[nb_ts, :] = obs.a_or
gen_p[nb_ts, :] = obs.prod_p
gen_q[nb_ts, :] = obs.prod_q
nb_ts += 1
pbar.update(1)
if nb_ts >= max_ts:
break
# if np.sum(obs.line_status) < obs.n_line - 1 * (nb_ts % 2 == 1):
# print("There is a bug following action; {}".format(act))
prev_act = act
# if done:
# print(act)
end_ = time.time()
total_time = end_ - beg_
return nb_ts, total_time, aor, gen_p, gen_q
def run_env_with_reset(env, max_ts, agent, seed=None):
nb_rows = min(env.chronics_handler.max_timestep(), max_ts)
aor = np.zeros((nb_rows, env.n_line))
gen_p = np.zeros((nb_rows, env.n_gen))
gen_q = np.zeros((nb_rows, env.n_gen))
if seed is not None:
env.seed(seed)
obs = env.reset()
done = False
reward = env.reward_range[0]
nb_ts = 0
beg_ = time.time()
reset_count = 0
with tqdm(total=nb_rows) as pbar:
while not done:
act = agent.act(obs, reward, done)
obs, reward, done, info = env.step(act)
aor[nb_ts, :] = obs.a_or
gen_p[nb_ts, :] = obs.prod_p
gen_q[nb_ts, :] = obs.prod_q
nb_ts += 1
pbar.update(1)
if nb_ts >= max_ts:
break
if done:
# I reset
reward = env.reward_range[0]
obs = env.reset()
reset_count += 1
done = False
end_ = time.time()
total_time = end_ - beg_
return nb_ts, total_time, aor, gen_p, gen_q, reset_count
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')