def run_multi_thread(self):
"""
Run the monte carlo simulation
@return:
"""
self.__cancel__ = False
# initialize the grid time series results
# we will append the island results with another function
self.circuit.time_series_results = TimeSeriesResults(0, 0, 0, 0, 0)
it = 0
variance_sum = 0.0
std_dev_progress = 0
v_variance = 0
n = len(self.circuit.buses)
m = len(self.circuit.branches)
mc_results = MonteCarloResults(n, m)
avg_res = PowerFlowResults()
avg_res.initialize(n, m)
# compile circuits
numerical_circuit = self.circuit.compile()
# perform the topological computation
calc_inputs_dict = numerical_circuit.compute_ts(
branch_tolerance_mode=self.options.branch_impedance_tolerance_mode,
ignore_single_node_islands=self.options.ignore_single_node_islands)
mc_results.bus_types = numerical_circuit.bus_types
v_sum = zeros(n, dtype=complex)
self.progress_signal.emit(0.0)
while (std_dev_progress <
100.0) and (it < self.max_mc_iter) and not self.__cancel__:
self.progress_text.emit('Running Monte Carlo: Variance: ' +
str(v_variance))
mc_results = MonteCarloResults(n, m, self.batch_size)
# for each partition of the profiles...
for t_key, calc_inputs in calc_inputs_dict.items():
# For every island, run the time series
for island_index, numerical_island in enumerate(calc_inputs):
# set the time series as sampled
monte_carlo_input = make_monte_carlo_input(
numerical_island)
mc_time_series = monte_carlo_input(
self.batch_size, use_latin_hypercube=False)
Vbus = numerical_island.Vbus
branch_rates = numerical_island.branch_rates
# short cut the indices
b_idx = numerical_island.original_bus_idx
br_idx = numerical_island.original_branch_idx
self.returned_results = list()
t = 0
while t < self.batch_size and not self.__cancel__:
Ysh, Ibus, Sbus = mc_time_series.get_at(t)
args = (t, self.options, numerical_island, Vbus, Sbus,
Ibus, branch_rates)
self.pool.apply_async(power_flow_worker_args, (args, ),
callback=self.update_progress_mt)
# wait for all jobs to complete
self.pool.close()
self.pool.join()
# collect results
self.progress_text.emit('Collecting batch results...')
for t, res in self.returned_results:
# store circuit results at the time index 't'
mc_results.S_points[
t, numerical_island.original_bus_idx] = res.Sbus
mc_results.V_points[
t, numerical_island.original_bus_idx] = res.voltage
mc_results.I_points[
t,
numerical_island.original_branch_idx] = res.Ibranch
mc_results.loading_points[
t,
numerical_island.original_branch_idx] = res.loading
mc_results.losses_points[
t,
numerical_island.original_branch_idx] = res.losses
# compile MC results
self.progress_text.emit('Compiling results...')
mc_results.compile()
# compute the island branch results
island_avg_res = numerical_island.compute_branch_results(
mc_results.voltage[b_idx])
# apply the island averaged results
avg_res.apply_from_island(island_avg_res,
b_idx=b_idx,
br_idx=br_idx)
# Compute the Monte Carlo values
it += self.batch_size
mc_results.append_batch(mc_results)
v_sum += mc_results.get_voltage_sum()
v_avg = v_sum / it
v_variance = abs(
(power(mc_results.V_points - v_avg, 2.0) / (it - 1)).min())
# progress
variance_sum += v_variance
err = variance_sum / it
if err == 0:
err = 1e-200 # to avoid division by zeros
mc_results.error_series.append(err)
# emmit the progress signal
std_dev_progress = 100 * self.mc_tol / err
if std_dev_progress > 100:
std_dev_progress = 100
self.progress_signal.emit(
max((std_dev_progress, it / self.max_mc_iter * 100)))
# print(iter, '/', max_mc_iter)
# print('Vmc:', Vavg)
# print('Vstd:', Vvariance, ' -> ', std_dev_progress, ' %')
# compute the averaged branch magnitudes
mc_results.sbranch = avg_res.Sbranch
mc_results.losses = avg_res.losses
# print('V mc: ', mc_results.voltage)
# send the finnish signal
self.progress_signal.emit(0.0)
self.progress_text.emit('Done!')
self.done_signal.emit()
return mc_results
def run_multi_thread(self):
"""
Run the monte carlo simulation
@return:
"""
# print('LHS run')
self.__cancel__ = False
# initialize vars
batch_size = self.sampling_points
n = len(self.circuit.buses)
m = self.circuit.get_branch_number()
n_cores = multiprocessing.cpu_count()
self.pool = multiprocessing.Pool()
self.progress_signal.emit(0.0)
self.progress_text.emit(
'Running Latin Hypercube Sampling in parallel using ' +
str(n_cores) + ' cores ...')
lhs_results = MonteCarloResults(n,
m,
batch_size,
name='Latin Hypercube')
avg_res = PowerFlowResults()
avg_res.initialize(n, m)
# compile the multi-circuit
numerical_circuit = self.circuit.compile_time_series()
# perform the topological computation
calc_inputs_dict = numerical_circuit.compute(
branch_tolerance_mode=self.options.branch_impedance_tolerance_mode,
ignore_single_node_islands=self.options.ignore_single_node_islands)
# for each partition of the profiles...
for t_key, calc_inputs in calc_inputs_dict.items():
# For every island, run the time series
for island_index, numerical_island in enumerate(calc_inputs):
lhs_results.bus_types = numerical_circuit.bus_types
monte_carlo_input = make_monte_carlo_input(numerical_island)
mc_time_series = monte_carlo_input(batch_size,
use_latin_hypercube=True)
Vbus = numerical_island.Vbus
branch_rates = numerical_island.branch_rates
# short cut the indices
b_idx = numerical_island.original_bus_idx
br_idx = numerical_island.original_branch_idx
# Start jobs
self.returned_results = list()
t = 0
while t < batch_size and not self.__cancel__:
Ysh, Ibus, Sbus = mc_time_series.get_at(t)
args = (t, self.options, numerical_island, Vbus, Sbus,
Ibus, branch_rates)
self.pool.apply_async(power_flow_worker_args, (args, ),
callback=self.update_progress_mt)
# wait for all jobs to complete
self.pool.close()
self.pool.join()
# collect results
self.progress_text.emit('Collecting results...')
for t, res in self.returned_results:
# store circuit results at the time index 't'
lhs_results.S_points[
t, numerical_island.original_bus_idx] = res.Sbus
lhs_results.V_points[
t, numerical_island.original_bus_idx] = res.voltage
lhs_results.Sbr_points[
t, numerical_island.original_branch_idx] = res.Sf
lhs_results.loading_points[
t, numerical_island.original_branch_idx] = res.loading
lhs_results.losses_points[
t, numerical_island.original_branch_idx] = res.losses
# compile MC results
self.progress_text.emit('Compiling results...')
lhs_results.compile()
# compute the island branch results
island_avg_res = numerical_island.compute_branch_results(
lhs_results.voltage[b_idx])
# apply the island averaged results
avg_res.apply_from_island(island_avg_res,
b_idx=b_idx,
br_idx=br_idx)
# lhs_results the averaged branch magnitudes
lhs_results.sbranch = avg_res.Sf
lhs_results.losses = avg_res.losses
self.results = lhs_results
# send the finnish signal
self.progress_signal.emit(0.0)
self.progress_text.emit('Done!')
self.done_signal.emit()
return lhs_results
def run_multi_thread(self):
"""
Run the monte carlo simulation
@return:
"""
# print('LHS run')
self.__cancel__ = False
# initialize vars
batch_size = self.sampling_points
n = len(self.circuit.buses)
m = len(self.circuit.branches)
n_cores = multiprocessing.cpu_count()
self.progress_signal.emit(0.0)
self.progress_text.emit(
'Running Latin Hypercube Sampling in parallel using ' +
str(n_cores) + ' cores ...')
lhs_results = MonteCarloResults(n, m, batch_size)
avg_res = PowerFlowResults()
avg_res.initialize(n, m)
# compile
# print('Compiling...', end='')
numerical_circuit = self.circuit.compile()
numerical_islands = numerical_circuit.compute(
branch_tolerance_mode=self.options.branch_impedance_tolerance_mode)
lhs_results.bus_types = numerical_circuit.bus_types
max_iter = batch_size * len(numerical_islands)
Sbase = self.circuit.Sbase
it = 0
# For every circuit, run the time series
for numerical_island in numerical_islands:
# try:
# set the time series as sampled in the circuit
monte_carlo_input = make_monte_carlo_input(numerical_island)
mc_time_series = monte_carlo_input(batch_size,
use_latin_hypercube=True)
Vbus = numerical_island.Vbus
# short cut the indices
b_idx = numerical_island.original_bus_idx
br_idx = numerical_island.original_branch_idx
manager = multiprocessing.Manager()
return_dict = manager.dict()
t = 0
while t < batch_size and not self.__cancel__:
k = 0
jobs = list()
# launch only n_cores jobs at the time
while k < n_cores + 2 and (t + k) < batch_size:
# set the power values
Y, I, S = mc_time_series.get_at(t)
# run power flow at the circuit
p = multiprocessing.Process(
target=power_flow_worker,
args=(t, self.options, numerical_island, Vbus,
S / Sbase, I / Sbase, return_dict))
jobs.append(p)
p.start()
k += 1
t += 1
# wait for all jobs to complete
for proc in jobs:
proc.join()
progress = ((t + 1) / batch_size) * 100
self.progress_signal.emit(progress)
# collect results
self.progress_text.emit('Collecting results...')
for t in return_dict.keys():
# store circuit results at the time index 't'
res = return_dict[t]
lhs_results.S_points[
t, numerical_island.original_bus_idx] = res.Sbus
lhs_results.V_points[
t, numerical_island.original_bus_idx] = res.voltage
lhs_results.I_points[
t, numerical_island.original_branch_idx] = res.Ibranch
lhs_results.loading_points[
t, numerical_island.original_branch_idx] = res.loading
lhs_results.losses_points[
t, numerical_island.original_branch_idx] = res.losses
# except Exception as ex:
# print(c.name, ex)
if self.__cancel__:
break
# compile MC results
self.progress_text.emit('Compiling results...')
lhs_results.compile()
# compute the island branch results
island_avg_res = numerical_island.compute_branch_results(
lhs_results.voltage[b_idx])
# apply the island averaged results
avg_res.apply_from_island(island_avg_res,
b_idx=b_idx,
br_idx=br_idx)
# lhs_results the averaged branch magnitudes
lhs_results.sbranch = avg_res.Sbranch
lhs_results.losses = avg_res.losses
self.results = lhs_results
# send the finnish signal
self.progress_signal.emit(0.0)
self.progress_text.emit('Done!')
self.done_signal.emit()
return lhs_results
def run_multi_thread(self):
"""
Run the monte carlo simulation
@return:
"""
self.__cancel__ = False
# initialize the grid time series results
# we will append the island results with another function
self.circuit.time_series_results = TimeSeriesResults(0, 0, 0, 0, 0)
Sbase = self.circuit.Sbase
n_cores = multiprocessing.cpu_count()
it = 0
variance_sum = 0.0
std_dev_progress = 0
v_variance = 0
n = len(self.circuit.buses)
m = len(self.circuit.branches)
mc_results = MonteCarloResults(n, m)
avg_res = PowerFlowResults()
avg_res.initialize(n, m)
# compile circuits
numerical_circuit = self.circuit.compile()
numerical_input_islands = numerical_circuit.compute(branch_tolerance_mode=self.options.branch_impedance_tolerance_mode)
mc_results.bus_types = numerical_circuit.bus_types
v_sum = zeros(n, dtype=complex)
self.progress_signal.emit(0.0)
while (std_dev_progress < 100.0) and (it < self.max_mc_iter) and not self.__cancel__:
self.progress_text.emit('Running Monte Carlo: Variance: ' + str(v_variance))
mc_results = MonteCarloResults(n, m, self.batch_size)
# For every circuit, run the time series
for numerical_island in numerical_input_islands:
# set the time series as sampled
monte_carlo_input = make_monte_carlo_input(numerical_island)
mc_time_series = monte_carlo_input(self.batch_size, use_latin_hypercube=False)
Vbus = numerical_island.Vbus
manager = multiprocessing.Manager()
return_dict = manager.dict()
# short cut the indices
b_idx = numerical_island.original_bus_idx
br_idx = numerical_island.original_branch_idx
t = 0
while t < self.batch_size and not self.__cancel__:
k = 0
jobs = list()
# launch only n_cores jobs at the time
while k < n_cores + 2 and (t + k) < self.batch_size:
# set the power values
Y, I, S = mc_time_series.get_at(t)
# run power flow at the circuit
p = multiprocessing.Process(target=power_flow_worker,
args=(t, self.options, numerical_island, Vbus, S / Sbase, I / Sbase, return_dict))
jobs.append(p)
p.start()
k += 1
t += 1
# wait for all jobs to complete
for proc in jobs:
proc.join()
# progress = ((t + 1) / self.batch_size) * 100
# self.progress_signal.emit(progress)
# collect results
self.progress_text.emit('Collecting batch results...')
for t in return_dict.keys():
# store circuit results at the time index 't'
res = return_dict[t]
mc_results.S_points[t, numerical_island.original_bus_idx] = res.Sbus
mc_results.V_points[t, numerical_island.original_bus_idx] = res.voltage
mc_results.I_points[t, numerical_island.original_branch_idx] = res.Ibranch
mc_results.loading_points[t, numerical_island.original_branch_idx] = res.loading
mc_results.losses_points[t, numerical_island.original_branch_idx] = res.losses
# compile MC results
self.progress_text.emit('Compiling results...')
mc_results.compile()
# compute the island branch results
island_avg_res = numerical_island.compute_branch_results(mc_results.voltage[b_idx])
# apply the island averaged results
avg_res.apply_from_island(island_avg_res, b_idx=b_idx, br_idx=br_idx)
# Compute the Monte Carlo values
it += self.batch_size
mc_results.append_batch(mc_results)
v_sum += mc_results.get_voltage_sum()
v_avg = v_sum / it
v_variance = abs((power(mc_results.V_points - v_avg, 2.0) / (it - 1)).min())
# progress
variance_sum += v_variance
err = variance_sum / it
if err == 0:
err = 1e-200 # to avoid division by zeros
mc_results.error_series.append(err)
# emmit the progress signal
std_dev_progress = 100 * self.mc_tol / err
if std_dev_progress > 100:
std_dev_progress = 100
self.progress_signal.emit(max((std_dev_progress, it / self.max_mc_iter * 100)))
# print(iter, '/', max_mc_iter)
# print('Vmc:', Vavg)
# print('Vstd:', Vvariance, ' -> ', std_dev_progress, ' %')
# compute the averaged branch magnitudes
mc_results.sbranch = avg_res.Sbranch
mc_results.losses = avg_res.losses
# print('V mc: ', mc_results.voltage)
# send the finnish signal
self.progress_signal.emit(0.0)
self.progress_text.emit('Done!')
self.done_signal.emit()
return mc_results
