Optimal Power Flow

Pandapower provides an interface for AC and DC optimal power flow calculations. In the following, it is presented how the optimisation problem can be formulated with the pandapower data format.

Note

We highly recommend the tutorials for the usage of the optimal power flow.

Optimisation problem

The equation describes the basic formulation of the optimal power flow problem. The pandapower optimal power flow can be constrained by either, AC and DC loadflow equations. The branch constraints represent the maximum apparent power loading of transformers and the maximum line current loadings. The bus constraints can contain maximum and minimum voltage magnitude and angle. For the external grid, generators, loads, DC lines and static generators, the maximum and minimum active resp. reactive power can be considered as operational constraints for the optimal power flow. The constraints are defined element wise in the respective element tables.

\[\begin{split}& min & \sum_{i \ \epsilon \ gen, sgen, load, extgrid }{P_{i} * f_{i}(P_i)} \\ & subject \ to \\ & & Loadflow \ equations \\ & & branch \ constraints \\ & & bus \ constraints \\ & & operational \ power \ constraints \\\end{split}\]

Generator Flexibilities / Operational power constraints

The active and reactive power generation of generators and static generators can be defined as a flexibility for the OPF.

Constraint Defined in
\(P_{min,i} \leq P_{g} \leq P_{max,g}, g \ \epsilon \ gen\) net.gen.min_p_kw / net.gen.max_p_kw
\(Q_{min,g} \leq Q_{g} \leq Q_{max,g}, g \ \epsilon \ gen\) net.gen.min_q_kvar / net.gen.max_q_kvar
\(P_{min,sg} \leq P_{sg} \leq P_{max,sg}, sg \ \epsilon \ sgen\) net.sgen.min_p_kw / net.sgen.max_p_kw
\(Q_{min,sg} \leq Q_{sg} \leq Q_{max,sg}, sg \ \epsilon \ sgen\) net.sgen.min_q_kvar / net.sgen.max_q_kvar
\(P_{max,g}, g \ \epsilon \ dcline\) net.dcline.max_p_kw
\(Q_{min,g} \leq Q_{g} \leq Q_{max,g}, g \ \epsilon \ dcline\) net.dcline.min_q_from_kvar / net.dcline.max_q_from_kvar / net.dcline.min_q_to_kvar / net.dcline.max_q_to_kvar
\(P_{min,eg} \leq P_{eg} \leq P_{max,eg}, eg \ \epsilon \ ext_grid\) net.ext_grid.min_p_kw / net.ext_grid.max_p_kw
\(Q_{min,eg} \leq Q_{eg} \leq Q_{max,eg}, eg \ \epsilon \ ext_grid\) net.ext_grid.min_q_kvar / net.ext_grid.max_q_kvar

Network Constraints

The network constraints contain constraints for bus voltages and branch flows:

Constraint Defined in
\(V_{min,j} \leq V_{g,i} \leq V_{min,i}, j \ \epsilon \ bus\) net.bus.min_vm_pu / net.bus.max_vm_pu
\(L_{k} \leq L_{max,k}, k \ \epsilon \ trafo\) net.trafo.max_loading_percent
\(L_{l} \leq L_{max,l}, l \ \epsilon \ line\) net.line.max_loading_percent
\(L_{l} \leq L_{max,l}, l \ \epsilon \ trafo_{3w}\) net.trafo3w.max_loading_percent

Cost functions

The cost function is specified element wise and is organized in tables as well, which makes the parametrization user friendly. There are two options formulating a cost function for each element: A piecewise linear function with $n$ data points.

\[\begin{split}f_{pwl}(p) = f_{\alpha} +(p-p_{\alpha}) \frac{f_{\alpha + 1}-f_{\alpha}}{p_{\alpha + 1}-p_{\alpha}} \ , \ (p_{\alpha},f_{\alpha}) \ =\begin{cases} (p_{0},f_{0}) \ , \ & p_{0} < p <p_{1}) \\ ...\\ (p_{n-1},f_{n-1}) \ , & \ p_{n-1} < p <p_{n}) \end{cases} \\ \\ f_{pwl}(q) = f_{1} +(q-q_{1}) \frac{f_{2}-f_{1}}{q_{2}-q_{1}}\end{split}\]

Piecewise linear cost functions can be specified using create_piecewise_linear_costs():

pandapower.create_piecewise_linear_cost(net, element, element_type, data_points, type='p', index=None)
Creates an entry for piecewise linear costs for an element. The currently supported elements are
  • Generator
  • External Grid
  • Static Generator
  • Load
  • Dcline
INPUT:

element (int) - ID of the element in the respective element table

element_type (string) - Type of element [“gen”, “sgen”, “ext_grid”, “load”, “dcline”] are possible

data_points - (numpy array) Numpy array containing n data points (see example)

OPTIONAL:

type - (string) - Type of cost [“p”, “q”] are allowed

index (int) - Force a specified ID if it is available

OUTPUT:
(int) Index of cost entry
EXAMPLE:
create_piecewise_linear_cost(net, 0, “load”, np.array([[0, 0], [75, 50], [150, 100]]))
NOTE:
costs for reactive power can only be quadratic, linear or constant. No higher grades supported.

The other option is to formulate a n-polynomial cost function:

\[\begin{split}f_{pol}(p) = c_n p^n + ... + c_1 p + c_0 \\ f_{pol}(q) = c_2 q^2 + c_1 q + c_0\end{split}\]

Polynomial cost functions can be speciefied using create_polynomial_cost():

pandapower.create_polynomial_cost(net, element, element_type, coefficients, type='p', index=None)
Creates an entry for polynomial costs for an element. The currently supported elements are
  • Generator
  • External Grid
  • Static Generator
  • Load
  • Dcline
INPUT:

element (int) - ID of the element in the respective element table

element_type (string) - Type of element [“gen”, “sgen”, “ext_grid”, “load”, “dcline”] are possible

data_points - (numpy array) Numpy array containing n cost coefficients (see example)

OPTIONAL:

type - (string) - Type of cost [“p”, “q”] are allowed

index (int) - Force a specified ID if it is available

OUTPUT:
(int) Index of cost entry
EXAMPLE:
create_polynomial_cost(net, 0, “gen”, np.array([0, 1, 0]))

Note

Please note, that polynomial costs for reactive power can only be quadratic, linear or constant. Piecewise linear cost funcions for reactive power are not working at the moment with 2 segments or more. Loads can only have 2 data points in their piecewise linear cost function for active power.

Active and reactive power costs are calculted seperately. The costs of all types are summed up to determine the overall costs for a grid state.

Parametrisation of the calculation

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. Another parametrisation for the AC OPF is, if voltage angles should be considered, which is the same option than for the loadflow calculation with pandapower.runpp:

pandapower.runopp(net, verbose=False, calculate_voltage_angles=False, check_connectivity=True, suppress_warnings=True, r_switch=0.0, delta=1e-10, **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_kw / net.sgen.max_p_kw
  • net.sgen.min_q_kvar / net.sgen.max_q_kvar
  • net.gen.min_p_kw / net.gen.max_p_kw
  • net.gen.min_q_kvar / net.gen.max_q_kvar
  • net.ext_grid.min_p_kw / net.ext_grid.max_p_kw
  • net.ext_grid.min_q_kvar / net.ext_grid.max_q_kvar
  • net.dcline.min_q_to_kvar / net.dcline.max_q_to_kvar / net.dcline.min_q_from_kvar / net.dcline.max_q_from_kvar
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:

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.
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.