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()
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
end = time.time()
elapsed = end - start
return voltage, converged, normF, Scalc, iter_, elapsed, error_list
if __name__ == '__main__':
# fname = r'/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE 9 Bus.gridcal'
fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE39_1W.gridcal'
from matplotlib import pyplot as plt
from GridCal.Engine import FileOpen
circuit = FileOpen(fname).open()
nc = circuit.compile()
islands = nc.compute()
island = islands[0]
voltage, converged, normF, Scalc, iter_, elapsed, err_lst = FDPF(
Vbus=island.Vbus,
Sbus=island.Sbus,
Ibus=island.Ibus,
Ybus=island.Ybus,
B1=island.B1,
B2=island.B2,
pq=island.pq,
pv=island.pv,
pqpv=island.pqpv,
tol=1e-9,
max_it=100)
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()
from matplotlib import pyplot as plt
# grid.load_file('lynn5buspq.xlsx')
# grid.load_file('lynn5buspv.xlsx')
# grid.load_file('IEEE30.xlsx')
# grid.load_file('/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE 14.xlsx')
# grid.load_file('/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE39.xlsx')
# grid.load_file('/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/1354 Pegase.xlsx')
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE 14.xlsx'
fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/IEEE39_1W.gridcal'
# fname = '/home/santi/Documentos/GitHub/GridCal/Grids_and_profiles/grids/1354 Pegase.xlsx'
grid = FileOpen(fname).open()
numerical_circuit = grid.compile()
circuits = numerical_circuit.compute()
circuit = circuits[0]
print('\nYbus:\n', circuit.Ybus.todense())
print('\nSbus:\n', circuit.Sbus)
print('\nIbus:\n', circuit.Ibus)
print('\nVbus:\n', circuit.Vbus)
print('\ntypes:\n', circuit.types)
print('\npq:\n', circuit.pq)
print('\npv:\n', circuit.pv)
print('\nvd:\n', circuit.ref)
start_time = time.time()
import pandas as pd
import os
import time
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 = '/home/santi/Documentos/Private_Grids/2026_INVIERNO_para Plexos_FINAL_9.raw'
grid = FileOpen(file_name=fname).open()
nc = grid.compile()
islands = nc.compute()
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 1')
for acc in [1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 0.1]:
start_time = time.time()
error_data1 = list()
V1, converged_, err, S, el = NR_LS(Ybus=circuit.Ybus,