Optimization with PYPOWER
You can run an Optimal Power Flow using the PYPOWER OPF
AC OPF
- pandapower.runopp(net, verbose=False, calculate_voltage_angles=True, check_connectivity=True, suppress_warnings=True, switch_rx_ratio=2, delta=1e-10, init='flat', numba=True, trafo3w_losses='hv', consider_line_temperature=False, **kwargs)
Runs the pandapower Optimal Power Flow. Flexibilities, constraints and cost parameters are defined in the pandapower element tables.
Flexibilities can be defined in net.sgen / net.gen /net.load / net.storage /net.ext_grid 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.gen.min_p_mw / net.gen.max_p_mw
net.gen.min_q_mvar / net.gen.max_q_mvar
net.sgen.min_p_mw / net.sgen.max_p_mw
net.sgen.min_q_mvar / net.sgen.max_q_mvar
net.dcline.max_p_mw
net.dcline.min_q_to_mvar / net.dcline.max_q_to_mvar / net.dcline.min_q_from_mvar / net.dcline.max_q_from_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.load.min_p_mw / net.load.max_p_mw
net.load.min_q_mvar / net.load.max_q_mvar
net.storage.min_p_mw / net.storage.max_p_mw
net.storage.min_q_mvar / net.storage.max_q_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
If the external grid ist controllable, the voltage setpoint of the external grid can be optimized within the voltage constraints by the OPF. The same applies to the voltage setpoints of the controllable generator elements.
How these costs are combined into a cost function depends on the cost_function parameter.
- INPUT:
net - The pandapower format network
- OPTIONAL:
verbose (bool, False) - If True, some basic information is printed
suppress_warnings (bool, True) - suppress warnings in pypower
If set to True, warnings are disabled during the loadflow. Because of the way data is processed in pypower, ComplexWarnings are raised during the loadflow. These warnings are suppressed by this option, however keep in mind all other pypower warnings are suppressed, too.
init (str, “flat”) - init of starting opf vector. Options are “flat”, “pf” or “results”
Starting solution vector (x0) for opf calculations is determined by this flag. Options are: “flat” (default): starting vector is (upper bound - lower bound) / 2 “pf”: a power flow is executed prior to the opf and the pf solution is the starting vector. This may improve convergence, but takes a longer runtime (which are probably neglectible for opf calculations) “results”: voltage magnitude vector is taken from result table
delta (float, 1e-10) - power tolerance
trafo3w_losses (str, “hv”) - defines where open loop losses of three-winding transformers are considered. Valid options are “hv”, “mv”, “lv” for HV/MV/LV side or “star” for the star point.
consider_line_temperature (bool, False) - adjustment of line impedance based on provided line temperature. If True, net.line must contain a column “temperature_degree_celsius”. The temperature dependency coefficient alpha must be provided in the net.line.alpha column, otherwise the default value of 0.004 is used
kwargs - Pypower / Matpower keyword arguments:
OPF_VIOLATION (5e-6) constraint violation tolerance
PDIPM_COSTTOL (1e-6) optimality tolerance
PDIPM_GRADTOL (1e-6) gradient tolerance
PDIPM_COMPTOL (1e-6) complementarity condition (inequality) tolerance
PDIPM_FEASTOL (set to OPF_VIOLATION if not specified) feasibiliy (equality) tolerance
PDIPM_MAX_IT (150) maximum number of iterations
SCPDIPM_RED_IT(20) maximum number of step size reductions per iteration
The internal solver uses the interior point method. By default, the initial state is the center of the operational constraints. Another option would be to initialize the optimisation with a valid loadflow solution. For optimiation of a timeseries, this warm start possibilty could imply a significant speedup. This is not yet provided in the actual version, but could be an useful extension in the future.
- References:
“On the Computation and Application of Multi-period Security-Constrained Optimal Power Flow for Real-time Electricity Market Operations”, Cornell University, May 2007.
H. Wang, C. E. Murillo-Sanchez, R. D. Zimmerman, R. J. Thomas, “On Computational Issues of Market-Based Optimal Power Flow”, IEEE Transactions on Power Systems, Vol. 22, No. 3, Aug. 2007, pp. 1185-1193.
R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: Steady-State Operations, Planning and Analysis Tools for Power Systems Research and Education,” Power Systems, IEEE Transactions on, vol. 26, no. 1, pp. 12-19, Feb. 2011.
Note
The optimization with pypower does not have the best convergence properties. Therefore, even if the network configuration is appropriate as an optimization problem, e.g. easily checked by pandapower.opf_task, pandapower.runopp may not converge.
DC OPF
The dc optimal power flow is a linearized optimization of the grid state. It offers two cost function options, that are fitting special use cases. To understand the usage, the DC OPF tutorial is recommended.
- pandapower.rundcopp(net, verbose=False, check_connectivity=True, suppress_warnings=True, switch_rx_ratio=0.5, delta=1e-10, trafo3w_losses='hv', **kwargs)
Runs the pandapower Optimal Power Flow. Flexibilities, constraints and cost parameters are defined in the pandapower element tables.
Flexibilities for generators can be defined in net.sgen / net.gen. net.sgen.controllable / net.gen.controllable signals if a generator is controllable. If False, the active and reactive power are assigned as in a normal power flow. If yes, the following flexibilities apply: - net.sgen.min_p_mw / net.sgen.max_p_mw - net.gen.min_p_mw / net.gen.max_p_mw - net.load.min_p_mw / net.load.max_p_mw
Network constraints can be defined for buses, lines and transformers the elements in the following columns: - net.line.max_loading_percent - net.trafo.max_loading_percent - net.trafo3w.max_loading_percent
- INPUT:
net - The pandapower format network
- OPTIONAL:
verbose (bool, False) - If True, some basic information is printed
suppress_warnings (bool, True) - suppress warnings in pypower
If set to True, warnings are disabled during the loadflow. Because of the way data is processed in pypower, ComplexWarnings are raised during the loadflow. These warnings are suppressed by this option, however keep in mind all other pypower warnings are suppressed, too.
delta (float, 1e-10) - power tolerance
trafo3w_losses (str, “hv”) - defines where open loop losses of three-winding transformers are considered. Valid options are “hv”, “mv”, “lv” for HV/MV/LV side or “star” for the star point.
Flexibilities, costs and constraints (except voltage constraints) are handled as in the Optimisation problem. Voltage constraints are not considered in the DC OPF, since voltage magnitutes are not part of the linearized power flow equations.
Note
If you are interested in the pypower casefile that pandapower is using for power flow, you can find it in net[“_ppc_opf”]. However all necessary informations are written into the pandpower format net, so the pandapower user should not usually have to deal with pypower.