def run(fname):
circuit = FileOpen(fname).open()
options = PowerFlowOptions(
solver_type=SolverType.NR,
retry_with_other_methods=False,
verbose=False,
initialize_with_existing_solution=False,
tolerance=1e-4,
max_iter=5,
max_outer_loop_iter=10,
control_q=ReactivePowerControlMode.NoControl,
control_taps=TapsControlMode.NoControl,
multi_core=False,
dispatch_storage=False,
control_p=False,
apply_temperature_correction=False,
branch_impedance_tolerance_mode=BranchImpedanceMode.Specified,
q_steepness_factor=30,
distributed_slack=False,
ignore_single_node_islands=False,
correction_parameter=1e-4)
driver = PowerFlowDriver(circuit, options)
driver.run()
print(abs(driver.results.voltage))
print(driver.results.error)
for r in driver.results.convergence_reports:
print(r)
def run(fname):
circuit = FileOpen(fname).open()
options = PowerFlowOptions(
solver_type=SolverType.NR,
retry_with_other_methods=False,
verbose=False,
initialize_with_existing_solution=False,
tolerance=1e-4,
max_iter=5,
max_outer_loop_iter=10,
control_q=ReactivePowerControlMode.NoControl,
control_taps=TapsControlMode.NoControl,
multi_core=False,
dispatch_storage=False,
control_p=False,
apply_temperature_correction=False,
branch_impedance_tolerance_mode=BranchImpedanceMode.Specified,
q_steepness_factor=30,
distributed_slack=False,
ignore_single_node_islands=False,
correction_parameter=1e-4)
driver = SampledTimeSeries(grid=circuit,
options=options,
number_of_steps=100)
driver.run()
# compose the train set
nc = circuit.compile_time_series()
Pbus = nc.get_power_injections().real.T
model = KNeighborsRegressor(n_neighbors=2)
model.fit(driver.results.S.real, np.abs(driver.results.voltage))
V_pred = model.predict(Pbus)
model = KNeighborsRegressor(n_neighbors=2)
model.fit(driver.results.S.real, driver.results.Sbranch.real)
Sbr_pred = model.predict(Pbus)
return V_pred, Sbr_pred
def get_connectivity(file_name):
circuit = FileOpen(file_name).open()
# form C
threshold = 1e-5
m = len(circuit.branches)
n = len(circuit.buses)
C = lil_matrix((m, n), dtype=int)
buses_dict = {bus: i for i, bus in enumerate(circuit.buses)}
branches_to_keep_idx = list()
branches_to_remove_idx = list()
states = np.zeros(m, dtype=int)
br_idx = [None] * m
graph = DiGraph()
for i in range(len(circuit.branches)):
# get the from and to bus indices
f = buses_dict[circuit.branches[i].bus_from]
t = buses_dict[circuit.branches[i].bus_to]
graph.add_edge(f, t)
C[i, f] = 1
C[i, t] = -1
br_idx[i] = i
rx = circuit.branches[i].R + circuit.branches[i].X
if circuit.branches[i].branch_type == BranchType.Branch:
branches_to_remove_idx.append(i)
states[i] = 0
else:
branches_to_keep_idx.append(i)
states[i] = 1
C = csc_matrix(C)
return circuit, states, C, C.transpose() * C, graph
from GridCal.Engine import FileOpen
import pandas as pd
np.set_printoptions(threshold=sys.maxsize, linewidth=200000000)
# np.set_printoptions(linewidth=2000, suppress=True)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE39_1W.gridcal'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE 14.xlsx'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/lynn5buspv.xlsx'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE 118.xlsx'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/1354 Pegase.xlsx'
# fname = 'helm_data1.gridcal'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE 14 PQ only.gridcal'
# fname = 'IEEE 14 PQ only full.gridcal'
fname = '/home/santi/Descargas/matpower-fubm-master/data/case5.m'
# fname = '/home/santi/Descargas/matpower-fubm-master/data/case30.m'
grid_ = FileOpen(fname).open()
# test_voltage(grid=grid)
# test_sigma(grid=grid)
nc_ = compile_snapshot_circuit(grid_)
islands_ = split_into_islands(nc_)
circuit_ = islands_[0]
H_ = make_ptdf(circuit_, distribute_slack=False)
print(H_)
import os
import time
np.set_printoptions(linewidth=10000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# fname = os.path.join('..', '..', '..', 'Grids_and_profiles', 'grids', 'IEEE 30 Bus with storage.xlsx')
# fname = os.path.join('..', '..', '..', 'Grids_and_profiles', 'grids', 'Illinois200Bus.xlsx')
# fname = os.path.join('..', '..', '..', 'Grids_and_profiles', 'grids', 'Pegase 2869.xlsx')
# fname = os.path.join('..', '..', '..', 'Grids_and_profiles', 'grids', '1354 Pegase.xlsx')
# fname = os.path.join('..', '..', '..', 'Grids_and_profiles', 'grids', 'IEEE 14.xlsx')
# fname = '/home/santi/Documentos/Private_Grids/2026_INVIERNO_para Plexos_FINAL_9.raw'
fname = '/home/santi/Documentos/Private_Grids/201902271115 caso TReal Israel.raw'
grid = FileOpen(file_name=fname).open()
nc = compile_snapshot_circuit(grid)
islands = nc.split_into_islands(ignore_single_node_islands=True)
circuit = islands[0]
# declare figure
fig = plt.figure(figsize=(12, 7))
ax = fig.add_subplot(1, 1, 1)
# circuit.Vbus = np.ones(len(circuit.Vbus), dtype=complex)
print('Newton-Raphson-Line-search 3')
for acc in [0.5]: # [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0.1]
start_time = time.time()
# print('Ybus')
self.__cancel__ = True
if __name__ == '__main__':
import os
import pandas as pd
from GridCal.Engine import FileOpen, SolverType
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/Lynn 5 Bus pv.gridcal'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE39_1W.gridcal'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/grid_2_islands.xlsx'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/2869 Pegase.gridcal'
fname = os.path.join('..', '..', '..', '..', '..', 'Grids_and_profiles', 'grids', 'IEEE 30 Bus with storage.xlsx')
# fname = os.path.join('..', '..', '..', '..', '..', 'Grids_and_profiles', 'grids', '2869 Pegase.gridcal')
main_circuit = FileOpen(fname).open()
pf_options_ = PowerFlowOptions(solver_type=SolverType.LACPF)
options_ = NMinusKOptions(use_multi_threading=False)
simulation = NMinusK(grid=main_circuit, options=options_, pf_options=pf_options_)
simulation.run()
otdf_ = simulation.get_otdf()
# save the result
br_names = [b.name for b in main_circuit.branches]
br_names2 = ['#' + b.name for b in main_circuit.branches]
w = pd.ExcelWriter('OTDF IEEE30.xlsx')
pd.DataFrame(data=simulation.results.Sbranch.real,
columns=br_names,
index=['base'] + br_names2).to_excel(w, sheet_name='branch power')
circuit.branches.pop(br_idx)
branch_names.pop(br_idx)
# delete the bus f from the circuit
# circuit.buses.pop(f)
print('\tRemoving:', bus_f)
circuit.buses.remove(bus_f)
C[:, t] += abs(C[:, f])
C = csc_matrix(np.delete(C.toarray(), f, 1))
graph.remove_node(f)
bus_names.pop(f)
dfc = pd.DataFrame(data=C.toarray(),
columns=bus_names,
index=branch_names)
print(dfc)
print(list(graph.edges))
if __name__ == '__main__':
from matplotlib import pyplot as plt
fname = 'D:\\GitHub\\GridCal\\Grids_and_profiles\\grids\\Reduction Model 2.xlsx'
circuit_ = FileOpen(fname).open()
reduce_grid(circuit=circuit_,
rx_criteria=False,
rx_threshold=1e-5,
type_criteria=True,
selected_type=BranchType.Branch)
def run(fname):
circuit = FileOpen(fname).open()
pf_options = PowerFlowOptions(
solver_type=SolverType.NR,
retry_with_other_methods=False,
verbose=False,
initialize_with_existing_solution=False,
tolerance=1e-6,
max_iter=5,
max_outer_loop_iter=10,
control_q=ReactivePowerControlMode.NoControl,
control_taps=TapsControlMode.NoControl,
multi_core=False,
dispatch_storage=False,
control_p=False,
apply_temperature_correction=False,
branch_impedance_tolerance_mode=BranchImpedanceMode.Specified,
q_steepness_factor=30,
distributed_slack=False,
ignore_single_node_islands=False,
correction_parameter=1e-4)
nc = circuit.compile()
ts_driver = TimeSeries(circuit, pf_options)
ts_driver.run()
ptdf_driver = PtdfTimeSeries(circuit, pf_options, power_delta=10)
ptdf_driver.run()
npoints = int(len(circuit.time_profile) * 1)
lhs_driver = LatinHypercubeSampling(circuit,
pf_options,
sampling_points=npoints)
lhs_driver.run()
P = nc.get_power_injections().real.T
Q = nc.get_power_injections().imag.T
Pbr_ts = ts_driver.results.Sbranch.real
Pbr_ptdf = ptdf_driver.results.Sbranch.real
P_lhs = lhs_driver.results.S_points.real
Q_lhs = lhs_driver.results.S_points.imag
Pbr_lhs = lhs_driver.results.Sbr_points.real
# KNN
n_neighbors = 3
model = neighbors.KNeighborsRegressor(n_neighbors)
# model.fit(P[:40], Pbr_ts[:40])
# model.fit(P_lhs, Pbr_lhs) # just the LHS for training
# X = np.r_[np.c_[P_lhs, Q], np.c_[P, Q]]
# Y = np.r_[Pbr_lhs, Pbr_ts]
X = np.c_[P, Q][:60]
Y = Pbr_ts[:60]
model.fit(X, Y) # LHS + TS for training ("dreaming")
Pbr_knn = model.predict(np.c_[P, Q])
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
i = 10 # branch index
ax.plot(Pbr_ts[i, :], label='Real flow', linewidth=5, c='orange')
ax.plot(Pbr_ptdf[i, :], label='PTDF', c='b', linestyle='--')
ax.plot(Pbr_knn[i, :], label='KNN', c='k', linestyle=':')
ax.set_xlabel('Time')
ax.set_ylabel('MW')
fig.legend()
plt.show()
import pandas as pd
import numpy as np
from scipy.sparse import lil_matrix, csc_matrix
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
file_name = 'D:\\GitHub\\GridCal\\Grids_and_profiles\\grids\\Reduction Model 2.xlsx'
from GridCal.Engine import MultiCircuit, BranchType, FileOpen
circuit = FileOpen(file_name).open()
# form C
threshold = 1e-5
m = len(circuit.branches)
n = len(circuit.buses)
C = lil_matrix((m, n), dtype=int)
buses_dict = {bus: i for i, bus in enumerate(circuit.buses)}
branches_to_keep_idx = list()
branches_to_remove_idx = list()
states = np.zeros(m, dtype=int)
br_idx = [None] * m
for i in range(len(circuit.branches)):
# get the from and to bus indices
f = buses_dict[circuit.branches[i].bus_from]
t = buses_dict[circuit.branches[i].bus_to]
C[i, f] = 1
C[i, t] = -1
br_idx[i] = i