def get_action_to_set(self):
line_status = self.get_line_status()
line_status = 2 * line_status - 1
line_status = line_status.astype(dt_int)
topo_vect = self.get_topo_vect()
self.runpf()
prod_p, _, prod_v = self.generators_info()
load_p, load_q, _ = self.loads_info()
# prod_p, prod_q, prod_v = self.init_pp_backend._gens_info()
# load_p, load_q, load_v = self.init_pp_backend._loads_info()
complete_action_class = CompleteAction.init_grid(self)
set_me = complete_action_class()
set_me.update({
"set_line_status": 1 * line_status,
"set_bus": 1 * topo_vect
})
injs = {
"prod_p": prod_p,
"prod_v": prod_v,
"load_p": load_p,
"load_q": load_q
}
set_me.update({"injection": injs})
return set_me
def get_action_to_set(self):
line_status = self.get_line_status()
line_status = 2 * line_status - 1
line_status = line_status.astype(dt_int)
topo_vect = self.get_topo_vect()
prod_p, _, prod_v = self.generators_info()
load_p, load_q, _ = self.loads_info()
complete_action_class = CompleteAction.init_grid(self)
set_me = complete_action_class(self)
set_me.update({"set_line_status": line_status, "set_bus": topo_vect})
return set_me
def _get_action_to_set_deprecated(self):
warnings.warn(
"DEPRECATION: grid2op <=1.4 is not well supported with lightsim2grid. Lots of bugs have been"
"fixed since then. Please upgrade to grid2op >= 1.5",
DeprecationWarning)
line_status = self.get_line_status()
line_status = 2 * line_status - 1
line_status = line_status.astype(dt_int)
topo_vect = self.get_topo_vect()
prod_p, _, prod_v = self.generators_info()
load_p, load_q, _ = self.loads_info()
complete_action_class = CompleteAction.init_grid(self)
set_me = complete_action_class()
set_me.update({"set_line_status": line_status, "set_bus": topo_vect})
return set_me
def __init__(
self,
path_grid_json, # complete path where the grid is represented as a json file
name="dc_approx",
is_dc=True,
attr_x=("prod_p", "prod_v", "load_p", "load_q",
"topo_vect"), # input that will be given to the proxy
attr_y=("a_or", "a_ex", "p_or", "p_ex", "q_or", "q_ex", "prod_q",
"load_v", "v_or",
"v_ex"), # output that we want the proxy to predict
):
BaseProxy.__init__(self,
name=name,
max_row_training_set=1,
eval_batch_size=1,
attr_x=attr_x,
attr_y=attr_y)
# datasets
self._supported_output = {
"a_or", "a_ex", "p_or", "p_ex", "q_or", "q_ex", "prod_q", "load_v",
"v_or", "v_ex"
}
self.is_dc = is_dc
for el in ("prod_p", "prod_v", "load_p", "load_q", "topo_vect"):
if not el in self.attr_x:
raise RuntimeError(
f"The DC approximation need the variable \"{el}\" to be computed."
)
for el in self.attr_y:
if not el in self._supported_output:
raise RuntimeError(
f"This solver cannot output the variable \"{el}\" at the moment. "
f"Only possible outputs are \"{self._supported_output}\".")
# specific part to dc model
self.solver = PandaPowerBackend()
self.solver.set_env_name(self.name)
self.solver.load_grid(
path_grid_json) # the real powergrid of the environment
self.solver.assert_grid_correct()
self._bk_act_class = _BackendAction.init_grid(self.solver)
self._act_class = CompleteAction.init_grid(self.solver)
# internal variables (speed optimisation)
self._indx_var = {}
for el in ("prod_p", "prod_v", "load_p", "load_q", "topo_vect"):
self._indx_var[el] = self.attr_x.index(el)