def __init__(self, detailed_infos_for_cascading_failures=False):
"""
Initialize an instance of Backend. This does nothing per se. Only the call to :func:`Backend.load_grid`
should guarantee the backend is properly configured.
:param detailed_infos_for_cascading_failures: Whether to be detailed (but slow) when computing cascading failures
:type detailed_infos_for_cascading_failures: :class:`bool`
"""
# lazy loading
from grid2op.Action import CompleteAction
from grid2op.Action._BackendAction import _BackendAction
GridObjects.__init__(self)
# the following parameter is used to control the amount of verbosity when computing a cascading failure
# if it's set to true, it returns all intermediate _grid states. This can slow down the computation!
self.detailed_infos_for_cascading_failures = detailed_infos_for_cascading_failures
# the power _grid manipulated. One powergrid per backend.
self._grid = None
# thermal limit setting, in ampere, at the same "side" of the powerline than self.get_line_overflow
self.thermal_limit_a = None
# action to set me
self.my_bk_act_class = _BackendAction
self._complete_action_class = CompleteAction
# for the shunt (only if supported)
self._sh_vnkv = None # for each shunt gives the nominal value at the bus at which it is connected
def __init__(self):
GridObjects.__init__(self)
# last connected registered
self.last_topo_registered = ValueStore(self.dim_topo, dtype=dt_int)
# topo at time t
self.current_topo = ValueStore(self.dim_topo, dtype=dt_int)
# injection at time t
self.prod_p = ValueStore(self.n_gen, dtype=dt_float)
self.prod_v = ValueStore(self.n_gen, dtype=dt_float)
self.load_p = ValueStore(self.n_load, dtype=dt_float)
self.load_q = ValueStore(self.n_load, dtype=dt_float)
self.activated_bus = np.full((self.n_sub, 2),
dtype=dt_bool,
fill_value=False)
self.big_topo_to_subid = np.repeat(list(range(self.n_sub)),
repeats=self.sub_info)
# shunts
if self.shunts_data_available:
self.shunt_p = ValueStore(self.n_shunt, dtype=dt_float)
self.shunt_q = ValueStore(self.n_shunt, dtype=dt_float)
self.shunt_bus = ValueStore(self.n_shunt, dtype=dt_int)
def __init__(self, envs):
GridObjects.__init__(self)
self.envs = envs
for env in envs:
if not isinstance(env, Environment):
raise MultiEnvException(
"You provided environment of type \"{}\" which is not supported."
"Please only provide a grid2op.Environment.Environment class."
"".format(type(env)))
self.nb_env = len(envs)
max_int = np.iinfo(dt_int).max
self._remotes, self._work_remotes = zip(
*[Pipe() for _ in range(self.nb_env)])
env_params = [
envs[e].get_kwargs(with_backend=False) for e in range(self.nb_env)
]
self._ps = [
RemoteEnv(env_params=env_,
remote=work_remote,
parent_remote=remote,
name="{}_subprocess_{}".format(envs[i].name, i),
seed=envs[i].space_prng.randint(max_int))
for i, (work_remote, remote, env_) in enumerate(
zip(self._work_remotes, self._remotes, env_params))
]
for p in self._ps:
p.daemon = True # if the main process crashes, we should not cause things to hang
p.start()
for remote in self._work_remotes:
remote.close()
self._waiting = True
def __init__(self, nb_env, env):
GridObjects.__init__(self)
self.imported_env = env
self.nb_env = nb_env
self._remotes, self._work_remotes = zip(
*[Pipe() for _ in range(self.nb_env)])
env_params = [env.get_kwargs() for _ in range(self.nb_env)]
for el in env_params:
el["backendClass"] = type(env.backend)
self._ps = [
RemoteEnv(env_params=env_,
remote=work_remote,
parent_remote=remote,
name="env: {}".format(i),
seed=np.random.randint(np.iinfo(dt_int).max))
for i, (work_remote, remote, env_) in enumerate(
zip(self._work_remotes, self._remotes, env_params))
]
for p in self._ps:
p.daemon = True # if the main process crashes, we should not cause things to hang
p.start()
for remote in self._work_remotes:
remote.close()
self._waiting = True
def __init__(
self,
envs_dir,
_add_to_name="", # internal, for test only, do not use !
**kwargs):
GridObjects.__init__(self)
RandomObject.__init__(self)
self.current_env = None
self.env_index = None
self.mix_envs = []
# Special case handling for backend
backendClass = None
if "backend" in kwargs:
backendClass = type(kwargs["backend"])
del kwargs["backend"]
# Inline import to prevent cyclical import
from grid2op.MakeEnv.Make import make
try:
for env_dir in sorted(os.listdir(envs_dir)):
env_path = os.path.join(envs_dir, env_dir)
if not os.path.isdir(env_path):
continue
# Special case for backend
if backendClass is not None:
env = make(env_path,
backend=backendClass(),
_add_to_name=_add_to_name,
**kwargs)
else:
env = make(env_path, **kwargs)
self.mix_envs.append(env)
except Exception as e:
err_msg = "MultiMix environment creation failed: {}".format(e)
raise EnvError(err_msg)
if len(self.mix_envs) == 0:
err_msg = "MultiMix envs_dir did not contain any valid env"
raise EnvError(err_msg)
self.env_index = 0
self.current_env = self.mix_envs[self.env_index]
# Make sure GridObject class attributes are set from first env
# Should be fine since the grid is the same for all envs
multi_env_name = os.path.basename(
os.path.abspath(envs_dir)) + _add_to_name
save_env_name = self.current_env.env_name
self.current_env.env_name = multi_env_name
self.__class__ = self.init_grid(self.current_env)
self.current_env.env_name = save_env_name
def __init__(self, detailed_infos_for_cascading_failures=False):
"""
Initialize an instance of Backend. This does nothing per se. Only the call to :func:`Backend.load_grid`
should guarantee the backend is properly configured.
:param detailed_infos_for_cascading_failures: Whether to be detailed (but slow) when computing cascading failures
:type detailed_infos_for_cascading_failures: :class:`bool`
"""
GridObjects.__init__(self)
# the following parameter is used to control the amount of verbosity when computing a cascading failure
# if it's set to true, it returns all intermediate _grid states. This can slow down the computation!
self.detailed_infos_for_cascading_failures = detailed_infos_for_cascading_failures
# the power _grid manipulated. One powergrid per backend.
self._grid = None
# thermal limit setting, in ampere, at the same "side" of the powerline than self.get_line_overflow
self.thermal_limit_a = None
def __init__(self, obs_env=None, action_helper=None, seed=None):
GridObjects.__init__(self)
self.action_helper = action_helper
# time stamp information
self.year = 1970
self.month = 0
self.day = 0
self.hour_of_day = 0
self.minute_of_hour = 0
self.day_of_week = 0
# for non deterministic observation that would not use default np.random module
self.seed = None
# handles the forecasts here
self._forecasted_grid_act = {}
self._forecasted_inj = []
self.timestep_overflow = np.zeros(shape=(self.n_line, ), dtype=dt_int)
# 0. (line is disconnected) / 1. (line is connected)
self.line_status = np.ones(shape=self.n_line, dtype=dt_bool)
# topological vector
self.topo_vect = np.full(shape=self.dim_topo,
dtype=dt_int,
fill_value=0)
# generators information
self.prod_p = np.full(shape=self.n_gen,
dtype=dt_float,
fill_value=np.NaN)
self.prod_q = np.full(shape=self.n_gen,
dtype=dt_float,
fill_value=np.NaN)
self.prod_v = np.full(shape=self.n_gen,
dtype=dt_float,
fill_value=np.NaN)
# loads information
self.load_p = np.full(shape=self.n_load,
dtype=dt_float,
fill_value=np.NaN)
self.load_q = np.full(shape=self.n_load,
dtype=dt_float,
fill_value=np.NaN)
self.load_v = np.full(shape=self.n_load,
dtype=dt_float,
fill_value=np.NaN)
# lines origin information
self.p_or = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
self.q_or = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
self.v_or = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
self.a_or = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
# lines extremity information
self.p_ex = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
self.q_ex = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
self.v_ex = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
self.a_ex = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
# lines relative flows
self.rho = np.full(shape=self.n_line,
dtype=dt_float,
fill_value=np.NaN)
# cool down and reconnection time after hard overflow, soft overflow or cascading failure
self.time_before_cooldown_line = np.full(shape=self.n_line,
dtype=dt_int,
fill_value=-1)
self.time_before_cooldown_sub = np.full(shape=self.n_sub,
dtype=dt_int,
fill_value=-1)
self.time_next_maintenance = np.full(shape=self.n_line,
dtype=dt_int,
fill_value=-1)
self.duration_next_maintenance = np.full(shape=self.n_line,
dtype=dt_int,
fill_value=-1)
# calendar data
self.year = dt_int(1970)
self.month = dt_int(0)
self.day = dt_int(0)
self.hour_of_day = dt_int(0)
self.minute_of_hour = dt_int(0)
self.day_of_week = dt_int(0)
# forecasts
self._forecasted_inj = []
self._forecasted_grid = []
self._obs_env = obs_env
# redispatching
self.target_dispatch = np.full(shape=self.n_gen,
dtype=dt_float,
fill_value=np.NaN)
self.actual_dispatch = np.full(shape=self.n_gen,
dtype=dt_float,
fill_value=np.NaN)
# value to assess if two observations are equal
self._tol_equal = 5e-1
self.attr_list_vect = None
def __init__(self,
parameters,
thermal_limit_a=None,
epsilon_poly=1e-2,
tol_poly=1e-6,
other_rewards={}):
GridObjects.__init__(self)
# specific to power system
if not isinstance(parameters, Parameters):
raise Grid2OpException(
"Parameter \"parameters\" used to build the Environment should derived form the "
"grid2op.Parameters class, type provided is \"{}\"".format(
type(parameters)))
self.parameters = parameters
# some timers
self._time_apply_act = 0
self._time_powerflow = 0
self._time_extract_obs = 0
self._time_opponent = 0
# data relative to interpolation
self._epsilon_poly = epsilon_poly
self._tol_poly = tol_poly
# define logger
self.logger = None
# and calendar data
self.time_stamp = None
self.nb_time_step = 0
# observation
self.current_obs = None
# type of power flow to play
# if True, then it will not disconnect lines above their thermal limits
self.no_overflow_disconnection = self.parameters.NO_OVERFLOW_DISCONNECTION
self.timestep_overflow = None
self.nb_timestep_overflow_allowed = None
# store actions "cooldown"
self.times_before_line_status_actionable = None
self.max_timestep_line_status_deactivated = self.parameters.NB_TIMESTEP_LINE_STATUS_REMODIF
self.times_before_topology_actionable = None
self.max_timestep_topology_deactivated = self.parameters.NB_TIMESTEP_TOPOLOGY_REMODIF
# for maintenance operation
self.time_next_maintenance = None
self.duration_next_maintenance = None
# hazard (not used outside of this class, information is given in `time_remaining_before_line_reconnection`
self._hazard_duration = None
# hard overflow part
self.hard_overflow_threshold = self.parameters.HARD_OVERFLOW_THRESHOLD
self.time_remaining_before_line_reconnection = None
self.env_dc = self.parameters.ENV_DC
# redispatching data
self.target_dispatch = None
self.actual_dispatch = None
self.gen_uptime = None
self.gen_downtime = None
self.gen_activeprod_t = None
self._thermal_limit_a = thermal_limit_a
# maintenance / hazards
self.time_next_maintenance = None
self.duration_next_maintenance = None
self.time_remaining_before_reconnection = None
# store environment modifications
self._injection = None
self._maintenance = None
self._hazards = None
self.env_modification = None
# to use the data
self.done = False
self.current_reward = None
self.helper_action_env = None
self.chronics_handler = None
self.game_rules = None
self.helper_action_player = None
self.rewardClass = None
self.actionClass = None
self.observationClass = None
self.legalActClass = None
self.helper_observation = None
self.names_chronics_to_backend = None
self.reward_helper = None
self.reward_range = None, None
# other rewards
self.other_rewards = {}
for k, v in other_rewards.items():
if not issubclass(v, BaseReward):
raise Grid2OpException(
"All keys of \"rewards\" key word argument should be classes that inherit from "
"\"grid2op.BaseReward\"")
self.other_rewards[k] = RewardHelper(v)
# opponent
self.opponent_action_class = DontAct # class of the action of the opponent
self.opponent_class = BaseOpponent # class of the opponent
self.opponent_init_budget = 0
## below initialized by _create_env, above: need to be called
self.opponent_action_space = None # ActionSpace(gridobj=)
self.compute_opp_budg = None # UnlimitedBudget(self.opponent_act_space)
self.opponent = None # OpponentSpace()
self.oppSpace = None
# voltage
self.voltage_controler = None
# backend
self.init_grid_path = None
# specific to Basic Env, do not change
self.backend = None
self.__is_init = False
def __init__(self,
observation_space,
substation_layout=None,
radius_sub=20.,
load_prod_dist=70.,
bus_radius=6.):
if substation_layout is None:
if observation_space.grid_layout is None:
# if no layout is provided, and observation_space has no layout, then it fails
raise PlotError(
"Impossible to use plotting abilities without specifying a layout (coordinates) "
"of the substations.")
# if no layout is provided, use the one in the observation_space
substation_layout = []
for el in observation_space.name_sub:
substation_layout.append(observation_space.grid_layout[el])
if len(substation_layout) != observation_space.n_sub:
raise PlotError(
"You provided a layout with {} elements while there are {} substations on the powergrid. "
"Your layout is invalid".format(len(substation_layout),
observation_space.n_sub))
GridObjects.__init__(self)
self.init_grid(observation_space)
self.observation_space = observation_space
self._layout = {}
self._layout["substations"] = self._get_sub_layout(substation_layout)
self.radius_sub = radius_sub
self.load_prod_dist = load_prod_dist # distance between load and generator to the center of the substation
self.bus_radius = bus_radius
self.subs_elements = [None for _ in self.observation_space.sub_info]
# get the element in each substation
for sub_id in range(self.observation_space.sub_info.shape[0]):
this_sub = {}
objs = self.observation_space.get_obj_connect_to(
substation_id=sub_id)
for c_id in objs["loads_id"]:
c_nm = self._get_load_name(sub_id, c_id)
this_load = {}
this_load["type"] = "load"
this_load["sub_pos"] = self.observation_space.load_to_sub_pos[
c_id]
this_sub[c_nm] = this_load
for g_id in objs["generators_id"]:
g_nm = self._get_gen_name(sub_id, g_id)
this_gen = {}
this_gen["type"] = "gen"
this_gen["sub_pos"] = self.observation_space.gen_to_sub_pos[
g_id]
this_sub[g_nm] = this_gen
for lor_id in objs["lines_or_id"]:
ext_id = self.observation_space.line_ex_to_subid[lor_id]
l_nm = self._get_line_name(sub_id, ext_id, lor_id)
this_line = {}
this_line["type"] = "line"
this_line[
"sub_pos"] = self.observation_space.line_or_to_sub_pos[
lor_id]
this_sub[l_nm] = this_line
for lex_id in objs["lines_ex_id"]:
or_id = self.observation_space.line_or_to_subid[lex_id]
l_nm = self._get_line_name(or_id, sub_id, lex_id)
this_line = {}
this_line["type"] = "line"
this_line[
"sub_pos"] = self.observation_space.line_ex_to_sub_pos[
lex_id]
this_sub[l_nm] = this_line
self.subs_elements[sub_id] = this_sub
self._compute_layout()
def __init__(self,
obs_env=None,
action_helper=None,
seed=None):
GridObjects.__init__(self)
self.action_helper = action_helper
# time stamp information
self.year = 1970
self.month = 0
self.day = 0
self.hour_of_day = 0
self.minute_of_hour = 0
self.day_of_week = 0
# for non deterministic observation that would not use default np.random module
self.seed = None
# handles the forecasts here
self._forecasted_grid_act = {}
self._forecasted_inj = []
self._obs_env = obs_env
self.timestep_overflow = np.zeros(shape=(self.n_line,), dtype=dt_int)
# 0. (line is disconnected) / 1. (line is connected)
self.line_status = np.ones(shape=self.n_line, dtype=dt_bool)
# topological vector
self.topo_vect = np.zeros(shape=self.dim_topo, dtype=dt_int)
# generators information
self.prod_p = np.full(shape=self.n_gen, dtype=dt_float, fill_value=np.NaN)
self.prod_q = 1.0 * self.prod_p
self.prod_v = 1.0 * self.prod_p
# loads information
self.load_p = np.full(shape=self.n_load, dtype=dt_float, fill_value=np.NaN)
self.load_q = 1.0 * self.load_p
self.load_v = 1.0 * self.load_p
# lines origin information
self.p_or = np.full(shape=self.n_line, dtype=dt_float, fill_value=np.NaN)
self.q_or = 1.0 * self.p_or
self.v_or = 1.0 * self.p_or
self.a_or = 1.0 * self.p_or
# lines extremity information
self.p_ex = 1.0 * self.p_or
self.q_ex = 1.0 * self.p_or
self.v_ex = 1.0 * self.p_or
self.a_ex = 1.0 * self.p_or
# lines relative flows
self.rho = 1.0 * self.p_or
# cool down and reconnection time after hard overflow, soft overflow or cascading failure
self.time_before_cooldown_line = np.full(shape=self.n_line, dtype=dt_int, fill_value=-1)
self.time_before_cooldown_sub = np.full(shape=self.n_sub, dtype=dt_int, fill_value=-1)
self.time_next_maintenance = 1 * self.time_before_cooldown_line
self.duration_next_maintenance = 1 * self.time_before_cooldown_line
# calendar data
self.year = dt_int(1970)
self.month = dt_int(0)
self.day = dt_int(0)
self.hour_of_day = dt_int(0)
self.minute_of_hour = dt_int(0)
self.day_of_week = dt_int(0)
# redispatching
self.target_dispatch = 1.0 * self.prod_p
self.actual_dispatch = 1.0 * self.prod_p
# to save some computation time
self._connectivity_matrix_ = None
self._bus_connectivity_matrix_ = None
self._dictionnarized = None
# for shunt (these are not stored!)
if self.shunts_data_available:
self._shunt_p = np.full(shape=self.n_shunt, dtype=dt_float, fill_value=np.NaN)
self._shunt_q = 1.0 * self._shunt_p
self._shunt_v = 1.0 * self._shunt_p
self._shunt_bus = np.full(shape=self.n_shunt, dtype=dt_int, fill_value=1)
def __init__(self, gridobj, obs_env=None, action_helper=None, seed=None):
GridObjects.__init__(self)
self.init_grid(gridobj)
self.action_helper = action_helper
# time stamp information
self.year = 1970
self.month = 0
self.day = 0
self.hour_of_day = 0
self.minute_of_hour = 0
self.day_of_week = 0
# for non deterministic observation that would not use default np.random module
self.seed = None
# handles the forecasts here
self._forecasted_grid = []
self._forecasted_inj = []
self._obs_env = obs_env
self.timestep_overflow = None
# 0. (line is disconnected) / 1. (line is connected)
self.line_status = None
# topological vector
self.topo_vect = None
# generators information
self.prod_p = None
self.prod_q = None
self.prod_v = None
# loads information
self.load_p = None
self.load_q = None
self.load_v = None
# lines origin information
self.p_or = None
self.q_or = None
self.v_or = None
self.a_or = None
# lines extremity information
self.p_ex = None
self.q_ex = None
self.v_ex = None
self.a_ex = None
# lines relative flows
self.rho = None
# cool down and reconnection time after hard overflow, soft overflow or cascading failure
self.time_before_cooldown_line = None
self.time_before_cooldown_sub = None
self.time_before_line_reconnectable = None
self.time_next_maintenance = None
self.duration_next_maintenance = None
# matrices
self.connectivity_matrix_ = None
self.bus_connectivity_matrix_ = None
self.vectorized = None
# redispatching
self.target_dispatch = None
self.actual_dispatch = None
# value to assess if two observations are equal
self._tol_equal = 5e-1
self.attr_list_vect = None
self.reset()
def __init__(
self,
envs_dir,
experimental_read_from_local_dir=False,
_add_to_name="", # internal, for test only, do not use !
_compat_glop_version=None, # internal, for test only, do not use !
_test=False,
**kwargs):
GridObjects.__init__(self)
RandomObject.__init__(self)
self.current_env = None
self.env_index = None
self.mix_envs = []
self._env_dir = os.path.abspath(envs_dir)
# Special case handling for backend
# TODO: with backend.copy() instead !
backendClass = None
if "backend" in kwargs:
backendClass = type(kwargs["backend"])
del kwargs["backend"]
# Inline import to prevent cyclical import
from grid2op.MakeEnv.Make import make
# TODO reuse same observation_space and action_space in all the envs maybe ?
try:
for env_dir in sorted(os.listdir(envs_dir)):
env_path = os.path.join(envs_dir, env_dir)
if not os.path.isdir(env_path):
continue
# Special case for backend
if backendClass is not None:
env = make(env_path,
backend=backendClass(),
_add_to_name=_add_to_name,
_compat_glop_version=_compat_glop_version,
test=_test,
experimental_read_from_local_dir=
experimental_read_from_local_dir,
**kwargs)
else:
env = make(env_path,
_add_to_name=_add_to_name,
_compat_glop_version=_compat_glop_version,
test=_test,
experimental_read_from_local_dir=
experimental_read_from_local_dir,
**kwargs)
self.mix_envs.append(env)
except Exception as exc_:
err_msg = "MultiMix environment creation failed: {}".format(exc_)
raise EnvError(err_msg)
if len(self.mix_envs) == 0:
err_msg = "MultiMix envs_dir did not contain any valid env"
raise EnvError(err_msg)
self.env_index = 0
self.current_env = self.mix_envs[self.env_index]
# Make sure GridObject class attributes are set from first env
# Should be fine since the grid is the same for all envs
multi_env_name = os.path.basename(
os.path.abspath(envs_dir)) + _add_to_name
save_env_name = self.current_env.env_name
self.current_env.env_name = multi_env_name
self.__class__ = self.init_grid(self.current_env)
self.current_env.env_name = save_env_name