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PandaPowerBackend.py
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PandaPowerBackend.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.
"""
This module presents an example of an implementation of a `grid2op.Backend` when using the powerflow
implementation "pandapower" available at `PandaPower <https://www.pandapower.org/>`_ for more details about
this backend. This file is provided as an example of a proper :class:`grid2op.Backend.Backend` implementation.
This backend currently does not work with 3 winding transformers and other exotic object.
"""
import os # load the python os default module
import sys # laod the python sys default module
import copy
import warnings
import numpy as np
import pandas as pd
import pandapower as pp
import scipy
from grid2op.dtypes import dt_int, dt_float, dt_bool
from grid2op.Backend.Backend import Backend
from grid2op.Action import BaseAction
from grid2op.Exceptions import *
try:
import numba
numba_ = True
except (ImportError, ModuleNotFoundError):
numba_ = False
warnings.warn("Numba cannot be loaded. You will gain possibly massive speed if installing it by "
"\n\t{} -m pip install numba\n".format(sys.executable))
class PandaPowerBackend(Backend):
"""
As explained in the `grid2op.Backend` module, every module must inherit the `grid2op.Backend` class.
This class have more attributes that are used internally for faster information retrieval.
Attributes
----------
prod_pu_to_kv: :class:`numpy.array`, dtype:float
The ratio that allow the conversion from pair-unit to kv for the generators
load_pu_to_kv: :class:`numpy.array`, dtype:float
The ratio that allow the conversion from pair-unit to kv for the loads
lines_or_pu_to_kv: :class:`numpy.array`, dtype:float
The ratio that allow the conversion from pair-unit to kv for the origin end of the powerlines
lines_ex_pu_to_kv: :class:`numpy.array`, dtype:float
The ratio that allow the conversion from pair-unit to kv for the extremity end of the powerlines
p_or: :class:`numpy.array`, dtype:float
The active power flowing at the origin end of each powerline
q_or: :class:`numpy.array`, dtype:float
The reactive power flowing at the origin end of each powerline
v_or: :class:`numpy.array`, dtype:float
The voltage magnitude at the origin bus of the powerline
a_or: :class:`numpy.array`, dtype:float
The current flowing at the origin end of each powerline
p_ex: :class:`numpy.array`, dtype:float
The active power flowing at the extremity end of each powerline
q_ex: :class:`numpy.array`, dtype:float
The reactive power flowing at the extremity end of each powerline
a_ex: :class:`numpy.array`, dtype:float
The current flowing at the extremity end of each powerline
v_ex: :class:`numpy.array`, dtype:float
The voltage magnitude at the extremity bus of the powerline
"""
def __init__(self, detailed_infos_for_cascading_failures=False):
Backend.__init__(self, detailed_infos_for_cascading_failures=detailed_infos_for_cascading_failures)
self.prod_pu_to_kv = None
self.load_pu_to_kv = None
self.lines_or_pu_to_kv = None
self.lines_ex_pu_to_kv = None
self.p_or = None
self.q_or = None
self.v_or = None
self.a_or = None
self.p_ex = None
self.q_ex = None
self.v_ex = None
self.a_ex = None
self.load_p = None
self.load_q = None
self.load_v = None
self.prod_p = None
self.prod_q = None
self.prod_v = None
self.line_status = None
self._pf_init = "flat"
self._pf_init = "results"
self._nb_bus_before = 0
self.thermal_limit_a = None
self._iref_slack = None
self._id_bus_added = None
self._fact_mult_gen = -1
self._what_object_where = None
self._number_true_line = -1
self._corresp_name_fun = {}
self._get_vector_inj = {}
self.dim_topo = -1
self._vars_action = BaseAction.attr_list_vect
self._vars_action_set = BaseAction.attr_list_vect
self.cst_1 = dt_float(1.0)
self._topo_vect = None
# self._time_topo_vect = 0.
def get_nb_active_bus(self):
"""
Compute the amount of buses "in service" eg with at least a powerline connected to it.
Returns
-------
res: :class:`int`
The total number of active buses.
"""
return np.sum(self._grid.bus["in_service"])
@staticmethod
def _load_grid_load_p_mw(grid):
return grid.load["p_mw"]
@staticmethod
def _load_grid_load_q_mvar(grid):
return grid.load["q_mvar"]
@staticmethod
def _load_grid_gen_p_mw(grid):
return grid.gen["p_mw"]
@staticmethod
def _load_grid_gen_vm_pu(grid):
return grid.gen["vm_pu"]
def reset(self, path=None, filename=None):
"""
Reload the grid.
For pandapower, it is a bit faster to store of a copy of itself at the end of load_grid
and deep_copy it to itself instead of calling load_grid again
"""
# Assign the content of itself as saved at the end of load_grid
# This overide all the attributes with the attributes from the copy in __pp_backend_initial_state
self.__dict__.update(copy.deepcopy(self.__pp_backend_initial_state).__dict__)
def load_grid(self, path=None, filename=None):
"""
Load the _grid, and initialize all the member of the class. Note that in order to perform topological
modification of the substation of the underlying powergrid, some buses are added to the test case loaded. They
are set as "out of service" unless a topological action acts on these specific substations.
"""
# TODO read the name from the file if they are set...
if path is None and filename is None:
raise RuntimeError("You must provide at least one of path or file to laod a powergrid.")
if path is None:
full_path = filename
elif filename is None:
full_path = path
else:
full_path = os.path.join(path, filename)
if not os.path.exists(full_path):
raise RuntimeError("There is no powergrid at \"{}\"".format(full_path))
with warnings.catch_warnings():
# remove deprecationg warnings for old version of pandapower
warnings.filterwarnings("ignore", category=DeprecationWarning)
self._grid = pp.from_json(full_path)
# add the slack bus that is often not modeled as a generator, but i need it for this backend to work
bus_gen_added = None
i_ref = None
self._iref_slack = None
self._id_bus_added = None
pp.runpp(self._grid, numba=numba_)
if np.all(~self._grid.gen["slack"]):
# there are not defined slack bus on the data, i need to hack it up a little bit
pd2ppc = self._grid._pd2ppc_lookups["bus"] # pd2ppc[pd_id] = ppc_id
ppc2pd = np.argsort(pd2ppc) # ppc2pd[ppc_id] = pd_id
for i, el in enumerate(self._grid._ppc['gen'][:, 0]):
if int(el) not in self._grid._pd2ppc_lookups["bus"][self._grid.gen["bus"].values]:
if bus_gen_added is not None:
raise RuntimeError("Impossible to recognize the powergrid")
bus_gen_added = ppc2pd[int(el)]
i_ref = i
break
self._iref_slack = i_ref
self._id_bus_added = self._grid.gen.shape[0]
# see https://matpower.org/docs/ref/matpower5.0/idx_gen.html for details on the comprehension of self._grid._ppc
pp.create_gen(self._grid, bus_gen_added,
p_mw=self._grid._ppc['gen'][i_ref, 1],
vm_pu=self._grid._ppc['gen'][i_ref, 5],
min_p_mw=self._grid._ppc['gen'][i_ref, 9],
max_p_mw=self._grid._ppc['gen'][i_ref, 8],
max_q_mvar=self._grid._ppc['gen'][i_ref, 3],
min_q_mvar=self._grid._ppc['gen'][i_ref, 4],
slack=True,
controllable=True)
pp.runpp(self._grid, numba=numba_)
self.__nb_bus_before = self._grid.bus.shape[0]
self.__nb_powerline = self._grid.line.shape[0]
self._init_bus_load = self.cst_1 * self._grid.load["bus"].values
self._init_bus_gen = self.cst_1 * self._grid.gen["bus"].values
self._init_bus_lor = self.cst_1 * self._grid.line["from_bus"].values
self._init_bus_lex = self.cst_1 * self._grid.line["to_bus"].values
t_for = self.cst_1 * self._grid.trafo["hv_bus"].values
t_fex = self.cst_1 * self._grid.trafo["lv_bus"].values
self._init_bus_lor = np.concatenate((self._init_bus_lor, t_for)).astype(np.int)
self._init_bus_lex = np.concatenate((self._init_bus_lex, t_fex)).astype(np.int)
self._grid["ext_grid"]["va_degree"] = 0.0
# this has the effect to divide by 2 the active power in the added generator, if this generator and the "slack bus"
# one are connected to the same bus.
# if not, it must not be done. So basically, i create a vector for which p and q for generator must be multiply
self._fact_mult_gen = np.ones(self._grid.gen.shape[0])
# self._fact_mult_gen[-1] += 1
# now extract the powergrid
self.n_line = copy.deepcopy(self._grid.line.shape[0]) + copy.deepcopy(self._grid.trafo.shape[0])
if "name" in self._grid.line.columns and not self._grid.line["name"].isnull().values.any():
self.name_line = [name for name in self._grid.line["name"]]
else:
self.name_line = ['{from_bus}_{to_bus}_{id_powerline_me}'.format(**row, id_powerline_me=i)
for i, (_, row) in enumerate(self._grid.line.iterrows())]
if "name" in self._grid.trafo.columns and not self._grid.trafo["name"].isnull().values.any():
self.name_line += [name_traf for name_traf in self._grid.trafo["name"]]
else:
transfo = [('{hv_bus}'.format(**row), '{lv_bus}'.format(**row))
for i, (_, row) in enumerate(self._grid.trafo.iterrows())]
transfo = [sorted(el) for el in transfo]
self.name_line += ['{}_{}_{}'.format(*el, i + self._grid.line.shape[0]) for i, el in enumerate(transfo)]
self.name_line = np.array(self.name_line)
self.n_gen = copy.deepcopy(self._grid.gen.shape[0])
if "name" in self._grid.gen.columns and not self._grid.gen["name"].isnull().values.any():
self.name_gen = [name_g for name_g in self._grid.gen["name"]]
else:
self.name_gen = ["gen_{bus}_{index_gen}".format(**row, index_gen=i)
for i, (_, row) in enumerate(self._grid.gen.iterrows())]
self.name_gen = np.array(self.name_gen)
self.n_load = copy.deepcopy(self._grid.load.shape[0])
if "name" in self._grid.load.columns and not self._grid.load["name"].isnull().values.any():
self.name_load = [nl for nl in self._grid.load["name"]]
else:
self.name_load = ["load_{bus}_{index_gen}".format(**row, index_gen=i)
for i, (_, row) in enumerate(self._grid.load.iterrows())]
self.name_load = np.array(self.name_load)
self.n_sub = copy.deepcopy(self._grid.bus.shape[0])
self.name_sub = ["sub_{}".format(i) for i, row in self._grid.bus.iterrows()]
self.name_sub = np.array(self.name_sub)
# number of elements per substation
self.sub_info = np.zeros(self.n_sub, dtype=dt_int)
self.load_to_subid = np.zeros(self.n_load, dtype=dt_int)
self.gen_to_subid = np.zeros(self.n_gen, dtype=dt_int)
self.line_or_to_subid = np.zeros(self.n_line, dtype=dt_int)
self.line_ex_to_subid = np.zeros(self.n_line, dtype=dt_int)
self.load_to_sub_pos = np.zeros(self.n_load, dtype=dt_int)
self.gen_to_sub_pos = np.zeros(self.n_gen, dtype=dt_int)
self.line_or_to_sub_pos = np.zeros(self.n_line, dtype=dt_int)
self.line_ex_to_sub_pos = np.zeros(self.n_line, dtype=dt_int)
pos_already_used = np.zeros(self.n_sub, dtype=dt_int)
self._what_object_where = [[] for _ in range(self.n_sub)]
# self._grid.line.sort_index(inplace=True)
# self._grid.trafo.sort_index(inplace=True)
# self._grid.gen.sort_index(inplace=True)
# self._grid.load.sort_index(inplace=True)
for i, (_, row) in enumerate(self._grid.line.iterrows()):
sub_or_id = int(row["from_bus"])
sub_ex_id = int(row["to_bus"])
self.sub_info[sub_or_id] += 1
self.sub_info[sub_ex_id] += 1
self.line_or_to_subid[i] = sub_or_id
self.line_ex_to_subid[i] = sub_ex_id
self.line_or_to_sub_pos[i] = pos_already_used[sub_or_id]
pos_already_used[sub_or_id] += 1
self.line_ex_to_sub_pos[i] = pos_already_used[sub_ex_id]
pos_already_used[sub_ex_id] += 1
self._what_object_where[sub_or_id].append(("line", "from_bus", i))
self._what_object_where[sub_ex_id].append(("line", "to_bus", i))
lag_transfo = self._grid.line.shape[0]
self._number_true_line = copy.deepcopy(self._grid.line.shape[0])
for i, (_, row) in enumerate(self._grid.trafo.iterrows()):
sub_or_id = int(row["hv_bus"])
sub_ex_id = int(row["lv_bus"])
self.sub_info[sub_or_id] += 1
self.sub_info[sub_ex_id] += 1
self.line_or_to_subid[i + lag_transfo] = sub_or_id
self.line_ex_to_subid[i + lag_transfo] = sub_ex_id
self.line_or_to_sub_pos[i + lag_transfo] = pos_already_used[sub_or_id]
pos_already_used[sub_or_id] += 1
self.line_ex_to_sub_pos[i + lag_transfo] = pos_already_used[sub_ex_id]
pos_already_used[sub_ex_id] += 1
self._what_object_where[sub_or_id].append(("trafo", "hv_bus", i))
self._what_object_where[sub_ex_id].append(("trafo", "lv_bus", i))
for i, (_, row) in enumerate(self._grid.gen.iterrows()):
sub_id = int(row["bus"])
self.sub_info[sub_id] += 1
self.gen_to_subid[i] = sub_id
self.gen_to_sub_pos[i] = pos_already_used[sub_id]
pos_already_used[sub_id] += 1
self._what_object_where[sub_id].append(("gen", "bus", i))
for i, (_, row) in enumerate(self._grid.load.iterrows()):
sub_id = int(row["bus"])
self.sub_info[sub_id] += 1
self.load_to_subid[i] = sub_id
self.load_to_sub_pos[i] = pos_already_used[sub_id]
pos_already_used[sub_id] += 1
self._what_object_where[sub_id].append(("load", "bus", i))
self._compute_pos_big_topo()
self.dim_topo = np.sum(self.sub_info)
# utilities for imeplementing apply_action
self._corresp_name_fun = {}
self._get_vector_inj = {}
self._get_vector_inj["load_p"] = self._load_grid_load_p_mw #lambda grid: grid.load["p_mw"]
self._get_vector_inj["load_q"] = self._load_grid_load_q_mvar #lambda grid: grid.load["q_mvar"]
self._get_vector_inj["prod_p"] = self._load_grid_gen_p_mw #lambda grid: grid.gen["p_mw"]
self._get_vector_inj["prod_v"] = self._load_grid_gen_vm_pu #lambda grid: grid.gen["vm_pu"]
# "hack" to handle topological changes, for now only 2 buses per substation
add_topo = copy.deepcopy(self._grid.bus)
add_topo.index += add_topo.shape[0]
add_topo["in_service"] = False
self._grid.bus = pd.concat((self._grid.bus, add_topo))
self.load_pu_to_kv = self._grid.bus["vn_kv"][self.load_to_subid].values.astype(dt_float)
self.prod_pu_to_kv = self._grid.bus["vn_kv"][self.gen_to_subid].values.astype(dt_float)
self.lines_or_pu_to_kv = self._grid.bus["vn_kv"][self.line_or_to_subid].values.astype(dt_float)
self.lines_ex_pu_to_kv = self._grid.bus["vn_kv"][self.line_ex_to_subid].values.astype(dt_float)
self.thermal_limit_a = 1000 * np.concatenate((self._grid.line["max_i_ka"].values,
self._grid.trafo["sn_mva"].values / (np.sqrt(3) * self._grid.trafo["vn_hv_kv"].values)))
self.p_or = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.q_or = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.v_or = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.a_or = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.p_ex = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.q_ex = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.v_ex = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.a_ex = np.full(self.n_line, dtype=dt_float, fill_value=np.NaN)
self.line_status = np.full(self.n_line, dtype=dt_bool, fill_value=np.NaN)
self.load_p = np.full(self.n_load, dtype=dt_float, fill_value=np.NaN)
self.load_q = np.full(self.n_load, dtype=dt_float, fill_value=np.NaN)
self.load_v = np.full(self.n_load, dtype=dt_float, fill_value=np.NaN)
self.prod_p = np.full(self.n_gen, dtype=dt_float, fill_value=np.NaN)
self.prod_v = np.full(self.n_gen, dtype=dt_float, fill_value=np.NaN)
self.prod_q = np.full(self.n_gen, dtype=dt_float, fill_value=np.NaN)
self._nb_bus_before = None
# shunts data
self.n_shunt = self._grid.shunt.shape[0]
self.shunt_to_subid = np.zeros(self.n_shunt, dtype=dt_int) - 1
name_shunt = []
for i, (_, row) in enumerate(self._grid.shunt.iterrows()):
bus = int(row["bus"])
name_shunt.append("shunt_{bus}_{index_shunt}".format(**row, index_shunt=i))
self.shunt_to_subid[i] = bus
self.name_shunt = np.array(name_shunt)
self.shunts_data_available = True
# store the topoid -> objid
self._big_topo_to_obj = [(None, None) for _ in range(self.dim_topo)]
nm_ = "load"
for load_id, pos_big_topo in enumerate(self.load_pos_topo_vect):
self._big_topo_to_obj[pos_big_topo] = (load_id, nm_)
nm_ = "gen"
for gen_id, pos_big_topo in enumerate(self.gen_pos_topo_vect):
self._big_topo_to_obj[pos_big_topo] = (gen_id, nm_)
nm_ = "lineor"
for l_id, pos_big_topo in enumerate(self.line_or_pos_topo_vect):
self._big_topo_to_obj[pos_big_topo] = (l_id, nm_)
nm_ = "lineex"
for l_id, pos_big_topo in enumerate(self.line_ex_pos_topo_vect):
self._big_topo_to_obj[pos_big_topo] = (l_id, nm_)
self._topo_vect = self._get_topo_vect()
# Create a deep copy of itself in the initial state
pp_backend_initial_state = copy.deepcopy(self)
# Store it under super private attribute
self.__pp_backend_initial_state = pp_backend_initial_state
def _convert_id_topo(self, id_big_topo):
"""
convert an id of the big topo vector into:
- the id of the object in its "only object" (eg if id_big_topo represents load 2, then it will be 2)
- the type of object among: "load", "gen", "lineor" and "lineex"
"""
return self._big_topo_to_obj[id_big_topo]
def apply_action(self, backendAction=None):
"""
Specific implementation of the method to apply an action modifying a powergrid in the pandapower format.
"""
active_bus, (prod_p, prod_v, load_p, load_q), topo__, shunts__ = backendAction()
k = "prod_p"
tmp = self._get_vector_inj[k](self._grid)
for gen_id, new_p in prod_p:
tmp.iloc[gen_id] = new_p
k = "prod_v"
tmp = self._get_vector_inj[k](self._grid)
for gen_id, new_v in prod_v:
tmp.iloc[gen_id] = new_v / self.prod_pu_to_kv[gen_id]
# convert values back to pu
if self._id_bus_added is not None:
# in this case the slack bus where not modeled as an independant generator in the
# original data
if gen_id == self._id_bus_added:
# handling of the slack bus, where "2" generators are present.
self._grid["ext_grid"]["vm_pu"] = tmp[gen_id]
k = "load_p"
tmp = self._get_vector_inj[k](self._grid)
for gen_id, new_p in load_p:
tmp.iloc[gen_id] = new_p
k = "load_q"
tmp = self._get_vector_inj[k](self._grid)
for gen_id, new_q in load_q:
tmp.iloc[gen_id] = new_q
if self.shunts_data_available:
shunt_p, shunt_q, shunt_bus = shunts__
for sh_id, new_p in shunt_p:
self._grid.shunt["p_mw"].iloc[sh_id] = new_p
for sh_id, new_q in shunt_q:
self._grid.shunt["q_mvar"].iloc[sh_id] = new_q
for sh_id, new_bus in shunt_bus:
if new_bus == -1:
self._grid.shunt["in_service"].iloc[sh_id] = False
elif new_bus == 1:
self._grid.shunt["in_service"].iloc[sh_id] = True
self._grid.shunt["bus"] = self.shunt_to_subid[sh_id]
elif new_bus == 2:
self._grid.shunt["in_service"].iloc[sh_id] = True
self._grid.shunt["bus"] = self.shunt_to_subid[sh_id] + self.__nb_bus_before
# i made at least a real change, so i implement it in the backend
for id_el, new_bus in topo__:
id_el_backend, type_obj = self._convert_id_topo(id_el)
if type_obj == "load":
new_bus_backend = self._pp_bus_from_grid2op_bus(new_bus, self._init_bus_load[id_el_backend])
self._grid.load["bus"].iloc[id_el_backend] = new_bus_backend
elif type_obj == "gen":
new_bus_backend = self._pp_bus_from_grid2op_bus(new_bus, self._init_bus_gen[id_el_backend])
self._grid.gen["bus"].iloc[id_el_backend] = new_bus_backend
if self._iref_slack is not None:
# remember in this case slack bus is actually 2 generators for pandapower !
if id_el_backend == self._grid.gen.shape[0] -1:
self._grid.ext_grid["bus"].iloc[0] = new_bus_backend
elif type_obj == "lineor":
new_bus_backend = self._pp_bus_from_grid2op_bus(new_bus, self._init_bus_lor[id_el_backend])
if id_el_backend < self.__nb_powerline:
# it's a powerline
self.change_bus_powerline_or(id_el_backend, new_bus_backend)
else:
# it's a trafo
self.change_bus_trafo_hv(id_el_backend - self.__nb_powerline, new_bus_backend)
elif type_obj == "lineex":
new_bus_backend = self._pp_bus_from_grid2op_bus(new_bus, self._init_bus_lex[id_el_backend])
if id_el_backend < self.__nb_powerline:
# it's a powerline
self.change_bus_powerline_ex(id_el_backend, new_bus_backend)
else:
# it's a trafo
self.change_bus_trafo_lv(id_el_backend - self.__nb_powerline, new_bus_backend)
bus_is = self._grid.bus["in_service"]
for i, (bus1_status, bus2_status) in enumerate(active_bus):
bus_is[i] = bus1_status # no iloc for bus, don't ask me why please :-/
bus_is[i + self.__nb_bus_before] = bus2_status
def change_bus_powerline_or(self, id_powerline_backend, new_bus_backend):
if new_bus_backend < 0:
self._grid.line["in_service"].iloc[id_powerline_backend] = False
else:
self._grid.line["in_service"].iloc[id_powerline_backend] = True
self._grid.line["from_bus"].iloc[id_powerline_backend] = new_bus_backend
def change_bus_powerline_ex(self, id_powerline_backend, new_bus_backend):
if new_bus_backend < 0:
self._grid.line["in_service"].iloc[id_powerline_backend] = False
else:
self._grid.line["in_service"].iloc[id_powerline_backend] = True
self._grid.line["to_bus"].iloc[id_powerline_backend] = new_bus_backend
def change_bus_trafo_hv(self, id_powerline_backend, new_bus_backend):
if new_bus_backend < 0:
self._grid.trafo["in_service"].iloc[id_powerline_backend] = False
else:
self._grid.trafo["in_service"].iloc[id_powerline_backend] = True
self._grid.trafo["hv_bus"].iloc[id_powerline_backend] = new_bus_backend
def change_bus_trafo_lv(self, id_powerline_backend, new_bus_backend):
if new_bus_backend < 0:
self._grid.trafo["in_service"].iloc[id_powerline_backend] = False
else:
self._grid.trafo["in_service"].iloc[id_powerline_backend] = True
self._grid.trafo["lv_bus"].iloc[id_powerline_backend] = new_bus_backend
def _pp_bus_from_grid2op_bus(self, grid2op_bus, grid2op_bus_init):
if grid2op_bus == 1:
res = grid2op_bus_init
elif grid2op_bus == 2:
res = grid2op_bus_init + self.__nb_bus_before
elif grid2op_bus == -1:
res = -1
else:
raise BackendError("grid2op bus must be -1, 1 or 2")
return int(res)
def _aux_get_line_info(self, colname1, colname2):
res = np.concatenate((self._grid.res_line[colname1].values, self._grid.res_trafo[colname2].values))
return res
def runpf(self, is_dc=False):
"""
Run a power flow on the underlying _grid. This implements an optimization of the powerflow
computation: if the number of
buses has not changed between two calls, the previous results are re used. This speeds up the computation
in case of "do nothing" action applied.
"""
# print("I called runpf")
conv = True
nb_bus = self.get_nb_active_bus()
try:
with warnings.catch_warnings():
# remove the warning if _grid non connex. And it that case load flow as not converged
warnings.filterwarnings("ignore", category=scipy.sparse.linalg.MatrixRankWarning)
warnings.filterwarnings("ignore", category=RuntimeWarning)
if self._nb_bus_before is None:
self._pf_init = "dc"
elif nb_bus == self._nb_bus_before:
self._pf_init = "results"
else:
self._pf_init = "auto"
if is_dc:
pp.rundcpp(self._grid, check_connectivity=False)
self._nb_bus_before = None # if dc i start normally next time i call an ac powerflow
else:
pp.runpp(self._grid, check_connectivity=False, init=self._pf_init, numba=numba_)
if self._grid.res_gen.isnull().values.any():
# TODO see if there is a better way here
# sometimes pandapower does not detect divergence and put Nan.
raise pp.powerflow.LoadflowNotConverged
self.load_p[:], self.load_q[:], self.load_v[:] = self._loads_info()
if not is_dc:
if not np.all(np.isfinite(self.load_v)):
# TODO see if there is a better way here
# some loads are disconnected: it's a game over case!
raise pp.powerflow.LoadflowNotConverged
self.line_status[:] = self._get_line_status()
# I retrieve the data once for the flows, so has to not re read multiple dataFrame
self.p_or[:] = self._aux_get_line_info("p_from_mw", "p_hv_mw")
self.q_or[:] = self._aux_get_line_info("q_from_mvar", "q_hv_mvar")
self.v_or[:] = self._aux_get_line_info("vm_from_pu", "vm_hv_pu")
self.a_or[:] = self._aux_get_line_info("i_from_ka", "i_hv_ka") * 1000
self.a_or[~np.isfinite(self.a_or)] = 0.
self.v_or[~np.isfinite(self.v_or)] = 0.
# it seems that pandapower does not take into account disconencted powerline for their voltage
self.v_or[~self.line_status] = 0.
self.v_ex[~self.line_status] = 0.
self.p_ex[:] = self._aux_get_line_info("p_to_mw", "p_lv_mw")
self.q_ex[:] = self._aux_get_line_info("q_to_mvar", "q_lv_mvar")
self.v_ex[:] = self._aux_get_line_info("vm_to_pu", "vm_lv_pu")
self.a_ex[:] = self._aux_get_line_info("i_to_ka", "i_lv_ka") * 1000
self.a_ex[~np.isfinite(self.a_ex)] = 0.
self.v_ex[~np.isfinite(self.v_ex)] = 0.
self.v_or[:] *= self.lines_or_pu_to_kv
self.v_ex[:] *= self.lines_ex_pu_to_kv
self.prod_p[:], self.prod_q[:], self.prod_v[:] = self._gens_info()
# for attr_nm in ["load_p", "load_q", "load_v", "p_or", "q_or", "v_or", "a_or", "p_ex", "q_ex",
# "v_ex", "a_ex", "prod_p", "prod_q", "prod_v"]:
# setattr(self, attr_nm, getattr(self, attr_nm).astype(dt_float))
self._nb_bus_before = None
self._grid._ppc["gen"][self._iref_slack, 1] = 0.
self._topo_vect[:] = self._get_topo_vect()
return self._grid.converged
except pp.powerflow.LoadflowNotConverged:
# of the powerflow has not converged, results are Nan
self.p_or[:] = np.NaN
self.q_or[:] = np.NaN
self.v_or[:] = np.NaN
self.a_or[:] = np.NaN
self.p_ex[:] = np.NaN
self.q_ex[:] = np.NaN
self.v_ex[:] = np.NaN
self.a_ex[:] = np.NaN
self.prod_p[:] = np.NaN
self.prod_q[:] = np.NaN
self.prod_v[:] = np.NaN
self.load_p[:] = np.NaN
self.load_q[:] = np.NaN
self.load_v[:] = np.NaN
self._nb_bus_before = None
return False
def copy(self):
"""
Performs a deep copy of the power :attr:`_grid`.
As pandapower is pure python, the deep copy operator is perfectly suited for the task.
"""
res = copy.deepcopy(self)
return res
def close(self):
"""
Called when the :class:`grid2op;Environment` has terminated, this function only reset the grid to a state
where it has not been loaded.
"""
del self._grid
self._grid = None
def save_file(self, full_path):
"""
Save the file to json.
:param full_path:
:return:
"""
pp.to_json(self._grid, full_path)
def get_line_status(self):
"""
As all the functions related to powerline, pandapower split them into multiple dataframe (some for transformers,
some for 3 winding transformers etc.). We make sure to get them all here.
"""
return self.line_status
def _get_line_status(self):
return np.concatenate((self._grid.line["in_service"].values, self._grid.trafo["in_service"].values)).astype(dt_bool)
def get_line_flow(self):
"""
return the powerflow in amps in all powerlines.
:return:
"""
return self.a_or
def _disconnect_line(self, id):
if id < self._number_true_line:
self._grid.line["in_service"].iloc[id] = False
else:
self._grid.trafo["in_service"].iloc[id - self._number_true_line] = False
def _reconnect_line(self, id):
if id < self._number_true_line:
self._grid.line["in_service"].iloc[id] = True
else:
self._grid.trafo["in_service"].iloc[id - self._number_true_line] = True
def get_topo_vect(self):
return self._topo_vect
def _get_topo_vect(self):
res = np.full(self.dim_topo, fill_value=np.NaN, dtype=dt_int)
line_status = self.get_line_status()
i = 0
for row in self._grid.line[["from_bus", "to_bus"]].values:
bus_or_id = row[0]
bus_ex_id = row[1]
if line_status[i]:
res[self.line_or_pos_topo_vect[i]] = 1 if bus_or_id == self.line_or_to_subid[i] else 2
res[self.line_ex_pos_topo_vect[i]] = 1 if bus_ex_id == self.line_ex_to_subid[i] else 2
else:
res[self.line_or_pos_topo_vect[i]] = -1
res[self.line_ex_pos_topo_vect[i]] = -1
i += 1
nb = self._number_true_line
i = 0
for row in self._grid.trafo[["hv_bus", "lv_bus"]].values:
bus_or_id = row[0]
bus_ex_id = row[1]
j = i + nb
if line_status[j]:
res[self.line_or_pos_topo_vect[j]] = 1 if bus_or_id == self.line_or_to_subid[j] else 2
res[self.line_ex_pos_topo_vect[j]] = 1 if bus_ex_id == self.line_ex_to_subid[j] else 2
else:
res[self.line_or_pos_topo_vect[j]] = -1
res[self.line_ex_pos_topo_vect[j]] = -1
i += 1
i = 0
for bus_id in self._grid.gen["bus"].values:
res[self.gen_pos_topo_vect[i]] = 1 if bus_id == self.gen_to_subid[i] else 2
i += 1
i = 0
for bus_id in self._grid.load["bus"].values:
res[self.load_pos_topo_vect[i]] = 1 if bus_id == self.load_to_subid[i] else 2
i += 1
return res
def _gens_info(self):
prod_p = self.cst_1 * self._grid.res_gen["p_mw"].values.astype(dt_float)
prod_q = self.cst_1 * self._grid.res_gen["q_mvar"].values.astype(dt_float)
prod_v = self.cst_1 * self._grid.res_gen["vm_pu"].values.astype(dt_float) * self.prod_pu_to_kv
if self._iref_slack is not None:
# slack bus and added generator are on same bus. I need to add power of slack bus to this one.
# if self._grid.gen["bus"].iloc[self._id_bus_added] == self.gen_to_subid[self._id_bus_added]:
if "gen" in self._grid._ppc["internal"]:
prod_p[self._id_bus_added] += self._grid._ppc["internal"]["gen"][self._iref_slack, 1]
prod_q[self._id_bus_added] += self._grid._ppc["internal"]["gen"][self._iref_slack, 2]
return prod_p, prod_q, prod_v
def generators_info(self):
return self.cst_1 * self.prod_p, self.cst_1 * self.prod_q, self.cst_1 * self.prod_v
def _loads_info(self):
load_p = self.cst_1 * self._grid.res_load["p_mw"].values.astype(dt_float)
load_q = self.cst_1 * self._grid.res_load["q_mvar"].values.astype(dt_float)
load_v = self._grid.res_bus.loc[self._grid.load["bus"].values]["vm_pu"].values.astype(dt_float) * self.load_pu_to_kv
return load_p, load_q, load_v
def loads_info(self):
return self.cst_1 * self.load_p, self.cst_1 * self.load_q, self.cst_1 * self.load_v
def lines_or_info(self):
return self.cst_1 * self.p_or, self.cst_1 * self.q_or, self.cst_1 * self.v_or, self.cst_1 * self.a_or
def lines_ex_info(self):
return self.cst_1 * self.p_ex, self.cst_1 * self.q_ex,self.cst_1 * self.v_ex, self.cst_1 * self.a_ex
def shunt_info(self):
shunt_p = self.cst_1 * self._grid.res_shunt["p_mw"].values.astype(dt_float)
shunt_q = self.cst_1 * self._grid.res_shunt["q_mvar"].values.astype(dt_float)
shunt_v = self._grid.res_bus["vm_pu"].values[self._grid.shunt["bus"].values]
shunt_v *= self._grid.bus["vn_kv"].values[self._grid.shunt["bus"]]
shunt_bus = self._grid.shunt["bus"].values < self.__nb_bus_before
shunt_bus = 1 * shunt_bus
shunt_bus = shunt_bus.astype(dt_int)
return shunt_p, shunt_q, shunt_v, shunt_bus
def sub_from_bus_id(self, bus_id):
if bus_id >= self._number_true_line:
return bus_id - self._number_true_line
return bus_id