def runpm_vd(net,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
pm_model="ACPPowerModel",
pm_solver="ipopt",
correct_pm_network_data=True,
silence=True,
pm_time_limits=None,
pm_log_level=0,
pm_file_path=None,
delete_buffer_file=True,
opf_flow_lim="S",
pm_tol=1e-8,
pdm_dev_mode=False,
**kwargs):
"""
Runs non-linear problem for voltage deviation minimization from PandaModels.jl.
"""
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=True,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file="run_pandamodels_vd",
pm_model=pm_model,
pm_solver=pm_solver,
correct_pm_network_data=correct_pm_network_data,
silence=silence,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim=opf_flow_lim,
pm_tol=pm_tol)
_runpm(net,
delete_buffer_file=delete_buffer_file,
pm_file_path=pm_file_path,
pdm_dev_mode=pdm_dev_mode)
def runpm_pf(net,
julia_file=None,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
correct_pm_network_data=True,
silence=True,
pm_model="ACPPowerModel",
pm_solver="ipopt",
pm_mip_solver="cbc",
pm_nl_solver="ipopt",
pm_time_limits=None,
pm_log_level=0,
delete_buffer_file=True,
pm_file_path=None,
opf_flow_lim="S",
pm_tol=1e-8,
pdm_dev_mode=False,
**kwargs): # pragma: no cover
"""
Runs power flow from PowerModels.jl via PandaModels.jl
"""
ac = True if "DC" not in pm_model else False
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file="run_powermodels_pf",
pm_solver=pm_solver,
pm_model=pm_model,
correct_pm_network_data=correct_pm_network_data,
silence=silence,
pm_mip_solver=pm_mip_solver,
pm_nl_solver=pm_nl_solver,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim=opf_flow_lim,
pm_tol=pm_tol)
_runpm(net,
delete_buffer_file=delete_buffer_file,
pm_file_path=pm_file_path,
pdm_dev_mode=pdm_dev_mode)
def runpm_tnep(net,
julia_file=None,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
pm_model="ACPPowerModel",
pm_solver="juniper",
correct_pm_network_data=True,
silence=True,
pm_nl_solver="ipopt",
pm_mip_solver="cbc",
pm_time_limits=None,
pm_log_level=0,
delete_buffer_file=True,
pm_file_path=None,
opf_flow_lim="S",
pm_tol=1e-8,
pdm_dev_mode=False,
**kwargs):
"""
Runs transmission network extension planning (tnep) optimization from PowerModels.jl via PandaModels.jl
"""
ac = True if "DC" not in pm_model else False
if pm_solver is None:
if pm_model == "DCPPowerModel":
pm_solver = "ipopt"
else:
pm_solver = "juniper"
if "ne_line" not in net:
raise ValueError(
"ne_line DataFrame missing in net. Please define to run tnep")
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file="run_powermodels_tnep",
pm_model=pm_model,
pm_solver=pm_solver,
correct_pm_network_data=correct_pm_network_data,
silence=silence,
pm_nl_solver=pm_nl_solver,
pm_mip_solver=pm_mip_solver,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim=opf_flow_lim,
pm_tol=pm_tol)
_runpm(net,
delete_buffer_file=delete_buffer_file,
pm_file_path=pm_file_path,
pdm_dev_mode=pdm_dev_mode)
read_tnep_results(net)
def runpm_storage_opf(net,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
n_timesteps=24,
time_elapsed=1.,
correct_pm_network_data=True,
silence=True,
pm_model="ACPPowerModel",
pm_time_limits=None,
pm_log_level=0,
opf_flow_lim="S",
charge_efficiency=1.,
discharge_efficiency=1.,
standby_loss=1e-8,
p_loss=1e-8,
q_loss=1e-8,
pm_tol=1e-8,
pdm_dev_mode=False,
**kwargs):
"""
Runs a non-linear power system optimization with storages and time series using PowerModels.jl.
"""
ac = True if "DC" not in pm_model else False
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=add_storage_opf_settings,
julia_file="run_powermodels_mn_storage",
correct_pm_network_data=correct_pm_network_data,
silence=silence,
pm_model=pm_model,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim=opf_flow_lim,
pm_tol=pm_tol,
pdm_dev_mode=pdm_dev_mode)
net._options["n_time_steps"] = n_timesteps
net._options["time_elapsed"] = time_elapsed
net._options["charge_efficiency"] = charge_efficiency
net._options["discharge_efficiency"] = discharge_efficiency
net._options["standby_loss"] = standby_loss
net._options["p_loss"] = p_loss
net._options["q_loss"] = q_loss
_runpm(net)
storage_results = read_pm_storage_results(net)
return storage_results
def runpm(net,
julia_file=None,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
correct_pm_network_data=True,
silence=True,
pm_model="ACPPowerModel",
pm_solver="ipopt",
pm_mip_solver="cbc",
pm_nl_solver="ipopt",
pm_time_limits=None,
pm_log_level=0,
delete_buffer_file=True,
pm_file_path=None,
opf_flow_lim="S",
pm_tol=1e-8,
pdm_dev_mode=False,
**kwargs): # pragma: no cover
"""
Runs optimal power flow from PowerModels.jl via PandaModels.jl
Flexibilities, constraints and cost parameters are defined in the pandapower element tables.
Flexibilities can be defined in net.sgen / net.gen /net.load
net.sgen.controllable if a static generator is controllable. If False,
the active and reactive power are assigned as in a normal power flow. If True, the following
flexibilities apply:
- net.sgen.min_p_mw / net.sgen.max_p_mw
- net.sgen.min_q_mvar / net.sgen.max_q_mvar
- net.load.min_p_mw / net.load.max_p_mw
- net.load.min_q_mvar / net.load.max_q_mvar
- net.gen.min_p_mw / net.gen.max_p_mw
- net.gen.min_q_mvar / net.gen.max_q_mvar
- net.ext_grid.min_p_mw / net.ext_grid.max_p_mw
- net.ext_grid.min_q_mvar / net.ext_grid.max_q_mvar
- net.dcline.min_q_to_mvar / net.dcline.max_q_to_mvar / net.dcline.min_q_from_mvar / net.dcline.max_q_from_mvar
Controllable loads behave just like controllable static generators. It must be stated if they are controllable.
Otherwise, they are not respected as flexibilities.
Dc lines are controllable per default
Network constraints can be defined for buses, lines and transformers the elements in the following columns:
- net.bus.min_vm_pu / net.bus.max_vm_pu
- net.line.max_loading_percent
- net.trafo.max_loading_percent
- net.trafo3w.max_loading_percent
How these costs are combined into a cost function depends on the cost_function parameter.
INPUT:
**net** - The pandapower format network
OPTIONAL:
**julia_file** (str, None) - path to a custom julia optimization file
**pp_to_pm_callback** (function, None) - callback function to add data to the PowerModels data structure
**correct_pm_network_data** (bool, True) - checks if network data is correct. If not tries to correct it
**silence** (bool, True) - Suppresses information and warning messages output by PowerModels
**pm_model** (str, "ACPPowerModel") - The PowerModels.jl model to use
**pm_solver** (str, "ipopt") - The "main" power models solver
**pm_mip_solver** (str, "cbc") - The mixed integer solver (when "main" solver == juniper)
**pm_nl_solver** (str, "ipopt") - The nonlinear solver (when "main" solver == juniper)
**pm_time_limits** (Dict, None) - Time limits in seconds for power models interface. To be set as a dict like
{"pm_time_limit": 300., "pm_nl_time_limit": 300., "pm_mip_time_limit": 300.}
**pm_log_level** (int, 0) - solver log level in power models
**delete_buffer_file** (Bool, True) - If True, the .json file used by powermodels will be deleted after
optimization.
**pm_file_path** (str, None) - Specifiy the filename, under which the .json file for powermodels is stored. If
you want to keep the file after optimization, you should also set
delete_buffer_file to False!
**opf_flow_lim** (str, "I") - Quantity to limit for branch flow constraints, in line with matpower's
"opf.flowlim" parameter:
"S" - apparent power flow (limit in MVA),
"I" - current magnitude (limit in MVA at 1 p.u. voltage)
**pm_tol** (float, 1e-8) - default desired convergence tolerance for solver to use.
**pdm_dev_mode** (bool, False) - If True, the develope mode of PdM is called.
"""
ac = True if "DC" not in pm_model else False
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file="run_powermodels_opf",
pm_solver=pm_solver,
pm_model=pm_model,
correct_pm_network_data=correct_pm_network_data,
silence=silence,
pm_mip_solver=pm_mip_solver,
pm_nl_solver=pm_nl_solver,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim=opf_flow_lim,
pm_tol=pm_tol)
_runpm(net,
delete_buffer_file=delete_buffer_file,
pm_file_path=pm_file_path,
pdm_dev_mode=pdm_dev_mode)
def runpm_storage_opf(net,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
n_timesteps=24,
time_elapsed=1.0,
correct_pm_network_data=True,
pm_model="ACPPowerModel",
pm_time_limits=None,
pm_log_level=0,
**kwargs): # pragma: no cover
"""
Runs a non-linear power system optimization with storages and time series using PowerModels.jl.
INPUT:
**net** - The pandapower format network
OPTIONAL:
**n_timesteps** (int, 24) - number of time steps to optimize
**time_elapsed** (float, 1.0) - time elapsed between time steps (1.0 = 1 hour)
**pm_time_limits** (Dict, None) - Time limits in seconds for power models interface. To be set as a dict like
{"pm_time_limit": 300., "pm_nl_time_limit": 300., "pm_mip_time_limit": 300.}
**pm_log_level** (int, 0) - solver log level in power models
"""
julia_file = os.path.join(pp_dir, "opf", 'run_powermodels_mn_storage.jl')
ac = True if "DC" not in pm_model else False
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=add_storage_opf_settings,
julia_file=julia_file,
correct_pm_network_data=correct_pm_network_data,
pm_model=pm_model,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level)
net._options["n_time_steps"] = n_timesteps
net._options["time_elapsed"] = time_elapsed
_runpm(net)
storage_results = read_pm_storage_results(net)
return storage_results
def runpm_ots(net,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
pm_model="DCPPowerModel",
pm_solver="juniper",
pm_nl_solver="ipopt",
pm_mip_solver="cbc",
correct_pm_network_data=True,
pm_time_limits=None,
pm_log_level=0,
**kwargs): # pragma: no cover
"""
Runs a non-linear optimal transmission switching (OTS) optimization using PowerModels.jl.
OPTIONAL:
**julia_file** (str, None) - path to a custom julia optimization file
**pp_to_pm_callback** (function, None) - callback function to add data to the PowerModels data structure
**correct_pm_network_data** (bool, True) - checks if network data is correct. If not tries to correct it
**pm_model** (str, "ACPPowerModel") - The PowerModels.jl model to use
**pm_solver** (str, "juniper") - The "main" power models solver
**pm_mip_solver** (str, "cbc") - The mixed integer solver (when "main" solver == juniper)
**pm_nl_solver** (str, "ipopt") - The nonlinear solver (when "main" solver == juniper)
**pm_time_limits** (Dict, None) - Time limits in seconds for power models interface. To be set as a dict like
{"pm_time_limit": 300., "pm_nl_time_limit": 300., "pm_mip_time_limit": 300.}
**pm_log_level** (int, 0) - solver log level in power models
"""
julia_file = os.path.join(pp_dir, "opf", 'run_powermodels_ots.jl')
ac = True if "DC" not in pm_model else False
if pm_solver is None:
pm_solver = "juniper"
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file=julia_file,
pm_model=pm_model,
pm_solver=pm_solver,
correct_pm_network_data=correct_pm_network_data,
pm_mip_solver=pm_mip_solver,
pm_nl_solver=pm_nl_solver,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim="S")
_runpm(net)
read_ots_results(net)
def runpm_dc_opf(net,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
correct_pm_network_data=True,
pm_model="DCPPowerModel",
pm_solver="ipopt",
pm_time_limits=None,
pm_log_level=0,
**kwargs): # pragma: no cover
"""
Runs a linearized power system optimization using PowerModels.jl.
Flexibilities, constraints and cost parameters are defined in the pandapower element tables.
Flexibilities can be defined in net.sgen / net.gen /net.load
net.sgen.controllable if a static generator is controllable. If False,
the active and reactive power are assigned as in a normal power flow. If True, the following
flexibilities apply:
- net.sgen.min_p_mw / net.sgen.max_p_mw
- net.sgen.min_q_mvar / net.sgen.max_q_mvar
- net.load.min_p_mw / net.load.max_p_mw
- net.load.min_q_mvar / net.load.max_q_mvar
- net.gen.min_p_mw / net.gen.max_p_mw
- net.gen.min_q_mvar / net.gen.max_q_mvar
- net.ext_grid.min_p_mw / net.ext_grid.max_p_mw
- net.ext_grid.min_q_mvar / net.ext_grid.max_q_mvar
- net.dcline.min_q_to_mvar / net.dcline.max_q_to_mvar / net.dcline.min_q_from_mvar / net.dcline.max_q_from_mvar
Controllable loads behave just like controllable static generators. It must be stated if they are controllable.
Otherwise, they are not respected as flexibilities.
Dc lines are controllable per default
Network constraints can be defined for buses, lines and transformers the elements in the following columns:
- net.bus.min_vm_pu / net.bus.max_vm_pu
- net.line.max_loading_percent
- net.trafo.max_loading_percent
- net.trafo3w.max_loading_percent
How these costs are combined into a cost function depends on the cost_function parameter.
INPUT:
**net** - The pandapower format network
OPTIONAL:
**pp_to_pm_callback** (function, None) - callback function to add data to the PowerModels data structure
**pm_model** (str, "DCPPowerModel") - model to use. Default is DC model
**pm_solver** (str, "ipopt") - The "main" power models solver
**correct_pm_network_data** (bool, True) - checks if network data is correct. If not tries to correct it
**pm_time_limits** (Dict, None) - Time limits in seconds for power models interface. To be set as a dict like
{"pm_time_limit": 300.}
**pm_log_level** (int, 0) - solver log level in power models
"""
julia_file = os.path.join(pp_dir, "opf", 'run_powermodels.jl')
ac = True if "DC" not in pm_model else False
net._options = {}
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file=julia_file,
correct_pm_network_data=correct_pm_network_data,
pm_model=pm_model,
pm_solver=pm_solver,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
opf_flow_lim="S")
_runpm(net)
def runpm(net,
julia_file=None,
pp_to_pm_callback=None,
calculate_voltage_angles=True,
trafo_model="t",
delta=1e-8,
trafo3w_losses="hv",
check_connectivity=True,
correct_pm_network_data=True,
pm_model="ACPPowerModel",
pm_solver="ipopt",
pm_mip_solver="cbc",
pm_nl_solver="ipopt",
pm_time_limits=None,
pm_log_level=0,
report_duals=False,
branch_limits="hard",
objective="cost"): # pragma: no cover
"""
Runs a power system optimization using PowerModels.jl. with a custom julia file.
Flexibilities, constraints and cost parameters are defined in the pandapower element tables.
Flexibilities can be defined in net.sgen / net.gen /net.load
net.sgen.controllable if a static generator is controllable. If False,
the active and reactive power are assigned as in a normal power flow. If True, the following
flexibilities apply:
- net.sgen.min_p_mw / net.sgen.max_p_mw
- net.sgen.min_q_mvar / net.sgen.max_q_mvar
- net.load.min_p_mw / net.load.max_p_mw
- net.load.min_q_mvar / net.load.max_q_mvar
- net.gen.min_p_mw / net.gen.max_p_mw
- net.gen.min_q_mvar / net.gen.max_q_mvar
- net.ext_grid.min_p_mw / net.ext_grid.max_p_mw
- net.ext_grid.min_q_mvar / net.ext_grid.max_q_mvar
- net.dcline.min_q_to_mvar / net.dcline.max_q_to_mvar / net.dcline.min_q_from_mvar / net.dcline.max_q_from_mvar
Controllable loads behave just like controllable static generators. It must be stated if they are controllable.
Otherwise, they are not respected as flexibilities.
Dc lines are controllable per default
Network constraints can be defined for buses, lines and transformers the elements in the following columns:
- net.bus.min_vm_pu / net.bus.max_vm_pu
- net.line.max_loading_percent
- net.trafo.max_loading_percent
- net.trafo3w.max_loading_percent
How these costs are combined into a cost function depends on the cost_function parameter.
INPUT:
**net** - The pandapower format network
OPTIONAL:
**julia_file** (str, None) - path to a custom julia optimization file
**pp_to_pm_callback** (function, None) - callback function to add data to the PowerModels data structure
**correct_pm_network_data** (bool, True) - checks if network data is correct. If not tries to correct it
**pm_model** (str, "ACPPowerModel") - The PowerModels.jl model to use
**pm_solver** (str, "ipopt") - The "main" power models solver
**pm_mip_solver** (str, "cbc") - The mixed integer solver (when "main" solver == juniper)
**pm_nl_solver** (str, "ipopt") - The nonlinear solver (when "main" solver == juniper)
**pm_time_limits** (Dict, None) - Time limits in seconds for power models interface. To be set as a dict like
{"pm_time_limit": 300., "pm_nl_time_limit": 300., "pm_mip_time_limit": 300.}
**pm_log_level** (int, 0) - solver log level in power models
** report_duals ** (bool, False) - whether or not the dual variables should be reported
** branch_limits ** (str, "hard") - how the power flow of the branches should be imposed
- "hard": impose hard limits on the branch flows. any violation means divergence
- "soft": violates the power flow restrictions in the branches as little as possible
- "none": impose no restrictions on the branch power flows
** objective ** (str, "cost") - the objective function to be used in the DC optimal power flow
- "cost": minimize the overall generation costs
- "flow": minimize the sum of squares of branch flows
- "cost-flow": minimize "cost" + "flow" (no weights are added)
- "cost-fuel": minimize PowerModels.objective_min_fuel_and_cost_polynomial
"""
net._options = {}
ac = True if "DC" not in pm_model else False
julia_file = os.path.join(
pp_dir, "opf",
'run_powermodels.jl') if julia_file is None else julia_file
_add_ppc_options(net,
calculate_voltage_angles=calculate_voltage_angles,
trafo_model=trafo_model,
check_connectivity=check_connectivity,
mode="opf",
switch_rx_ratio=2,
init_vm_pu="flat",
init_va_degree="flat",
enforce_q_lims=True,
recycle=dict(_is_elements=False, ppc=False, Ybus=False),
voltage_depend_loads=False,
delta=delta,
trafo3w_losses=trafo3w_losses)
_add_opf_options(net,
trafo_loading='power',
ac=ac,
init="flat",
numba=True,
pp_to_pm_callback=pp_to_pm_callback,
julia_file=julia_file,
pm_solver=pm_solver,
pm_model=pm_model,
correct_pm_network_data=correct_pm_network_data,
pm_mip_solver=pm_mip_solver,
pm_nl_solver=pm_nl_solver,
pm_time_limits=pm_time_limits,
pm_log_level=pm_log_level,
report_duals=report_duals,
branch_limits=branch_limits,
objective=objective)
_runpm(net)
