Power System Test Cases

Note

All Power System Test Cases were converted from PYPOWER or MATPOWER case files.

Case 4gs

pandapower.networks.case4gs(**kwargs)

This is the 4 bus example from J. J. Grainger and W. D. Stevenson, Power system analysis. McGraw-Hill, 1994. pp. 337-338. Its data origin is PYPOWER.

OUTPUT:

net - Returns the required ieee network case4gs

EXAMPLE:

import pandapower.networks as pn

net = pn.case4gs()

Case 5

pandapower.networks.case5(**kwargs)

This is the 5 bus example from F.Li and R.Bo, “Small Test Systems for Power System Economic Studies” Its data origin is MATPOWER.

OUTPUT:

net - Returns the required ieee network case5

EXAMPLE:

import pandapower.networks as pn

net = pn.case5()

Case 6ww

pandapower.networks.case6ww(**kwargs)

Calls the json file case6ww.json which data origin is PYPOWER. It represents the 6 bus example from pp. 104, 112, 119, 123-124, 549 from A. J. Wood and B. F. Wollenberg, Power generation, operation, and control. John Wiley & Sons, 2012..

OUTPUT:

net - Returns the required ieee network case6ww

EXAMPLE:

import pandapower.networks as pn

net = pn.case6ww()

Case 9

pandapower.networks.case9(**kwargs)

Calls the json file case9.json which data origin is PYPOWER. This network was published in Anderson and Fouad’s book ‘Power System Control and Stability’ for the first time in 1980.

OUTPUT:

net - Returns the required ieee network case9

EXAMPLE:

import pandapower.networks as pn

net = pn.case9()

Case 14

pandapower.networks.case14(**kwargs)

Calls the json file case14.json which data origin is PYPOWER. This network was converted from IEEE Common Data Format (ieee14cdf.txt) on 20-Sep-2004 by cdf2matp, rev. 1.11, to matpower format and finally converted to pandapower format by pandapower.converter.from_ppc. The vn_kv was adapted considering the proposed voltage levels in Washington case 14

OUTPUT:

net - Returns the required ieee network case14

EXAMPLE:

import pandapower.networks as pn

net = pn.case14()

Case 24_ieee_rts

pandapower.networks.case24_ieee_rts(**kwargs)

The IEEE 24-bus reliability test system was developed by the IEEE reliability subcommittee and published in 1979. Some more information about this network are given by Illinois University case 24. The data origin for this network data is PYPOWER.

OUTPUT:

net - Returns the required ieee network case24

EXAMPLE:

import pandapower.networks as pn

net = pn.case24_ieee_rts()

Case 30

pandapower.networks.case30(**kwargs)

This function calls the json file case30.json which data origin is PYPOWER. The PYPOWER data are derived from Washington 30 Bus Dynamic Test Case.

OUTPUT:

net - Returns the required ieee network case30

EXAMPLE:

import pandapower.networks as pn

net = pn.case30()

Case IEEE30

pandapower.networks.case_ieee30(**kwargs)

This function calls the json file case_ieee30.json which data origin is MATPOWER. The MATPOWER data are derived from Washington IEEE 30 bus Case. Additional information about this network are available at Illinois University case 30.

OUTPUT:

net - Returns the required ieee network case30

EXAMPLE:

import pandapower.networks as pn

net = pn.case_ieee30()

Case 33bw

pandapower.networks.case33bw(**kwargs)

Calls the json file case33bw.json which data is provided by MATPOWER. The data origin is the paper M. Baran, F. Wu, Network reconfiguration in distribution systems for loss reduction and load balancing IEEE Transactions on Power Delivery, 1989.

OUTPUT:

net - Returns the required ieee network case33bw

EXAMPLE:

import pandapower.networks as pn

net = pn.case33bw()

Case 39

pandapower.networks.case39(**kwargs)

Calls the json file case39.json which data origin is PYPOWER. This network was published the first time in G. Bills et al., On-line stability analysis study, RP 90-1, E. P. R. I. North American Rockwell Corporation, Edison Electric Institute, Ed. IEEE Press, Oct. 1970,. Some more information about this network are given by Illinois University case 39. Because the Pypower data origin proposes vn_kv=345 for all nodes the transformers connect node of the same voltage level.

OUTPUT:

net - Returns the required ieee network case39

EXAMPLE:

import pandapower.networks as pn

net = pn.case39()

Case 57

pandapower.networks.case57(vn_kv_area1=115, vn_kv_area2=500, vn_kv_area3=138, vn_kv_area4=345, vn_kv_area5=230, vn_kv_area6=161, **kwargs)

This function provides the ieee case57 network with the data origin PYPOWER case 57. Some more information about this network are given by Illinois University case 57. Because the Pypower data origin proposes no vn_kv some assumption must be made. There are six areas with coinciding voltage level. These are:

area 1 with coinciding voltage level comprises node 1-17
area 2 with coinciding voltage level comprises node 18-20
area 3 with coinciding voltage level comprises node 21-24 + 34-40 + 44-51
area 4 with coinciding voltage level comprises node 25 + 30-33
area 5 with coinciding voltage level comprises node 41-43 + 56-57
area 6 with coinciding voltage level comprises node 52-55 + 26-29

OUTPUT:

net - Returns the required ieee network case57

EXAMPLE:

import pandapower.networks as pn

net = pn.case57()

Case 89pegase

pandapower.networks.case89pegase(**kwargs)

Calls the json file case89pegase.json which data is provided by MATPOWER. The data origin are the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

OUTPUT:

net - Returns the required ieee network case89pegase

EXAMPLE:

import pandapower.networks as pn

net = pn.case89pegase()

Case 118

pandapower.networks.case118(**kwargs)

Calls the json file case118.json which data origin is PYPOWER. Some more information about this network are given by Washington case 118 and Illinois University case 118. The PYPOWER case data are corrected at Vm of buses 68 and 116.

OUTPUT:

net - Returns the required ieee network case118

EXAMPLE:

import pandapower.networks as pn

net = pn.case118()

Case 145

pandapower.networks.case145(**kwargs)

Calls the json file case145.json which data origin is MATPOWER. This data is converted by MATPOWER 5.1 using CDF2MPC on 18-May-2016 from ‘dd50cdf.txt’.

OUTPUT:

net - Returns the required ieee network case145

EXAMPLE:

import pandapower.networks as pn

net = pn.case145()

Case case_illinois200

pandapower.networks.case_illinois200(**kwargs)

This function calls the json file case_illinois200.json which data origin is MATPOWER. This network was published in A.B. Birchfield, T. Xu, K.M. Gegner, K.S. Shetye, T.J. Overbye, “Grid Structural Characteristics as Validation Criteria for Synthetic Networks,” IEEE Transactions on Power Systems, 2017. Some additional information about this network are available at Illinois University Illinois 200.

OUTPUT:

net - Returns the required ieee network case30

EXAMPLE:

import pandapower.networks as pn

net = pn.case_illinois200()

Case 300

pandapower.networks.case300(**kwargs)

Calls the json file case300.json which data origin is PYPOWER. Some more information about this network are given by Washington case 300 and Illinois University case 300.

OUTPUT:

net - Returns the required ieee network case300

EXAMPLE:

import pandapower.networks as pn

net = pn.case300()

Case 1354pegase

pandapower.networks.case1354pegase(**kwargs)

This grid represents a part of the European high voltage transmission network. The data is provided by MATPOWER. The data origin are the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

OUTPUT:

net - Returns the required ieee network case1354pegase

EXAMPLE:

import pandapower.networks as pn

net = pn.case1354pegase()

Case 1888rte

pandapower.networks.case1888rte(ref_bus_idx=1246, **kwargs)

This case accurately represents the size and complexity of French very high voltage and high voltage transmission network. The data is provided by MATPOWER. The data origin is the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016.

OPTIONAL:

ref_bus_idx - Since the MATPOWER case provides a reference bus without connected generator, because a distributed slack is assumed, to convert the data to pandapower, another bus has been assumed as reference bus. Via ‘ref_bus_idx’ the User can choose a reference bus, which should have a generator connected to. Please be aware that by changing the reference bus to another bus than the proposed default value, maybe a powerflow does not converge anymore!

OUTPUT:

net - Returns the required ieee network case1888rte

EXAMPLE:

import pandapower.networks as pn

net = pn.case1888rte()

Case 2848rte

pandapower.networks.case2848rte(ref_bus_idx=271, **kwargs)

This case accurately represents the size and complexity of French very high voltage and high voltage transmission network. The data is provided by MATPOWER. The data origin is the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016.

OPTIONAL:

ref_bus_idx - Since the MATPOWER case provides a reference bus without connected generator, because a distributed slack is assumed, to convert the data to pandapower, another bus has been assumed as reference bus. Via ‘ref_bus_idx’ the User can choose a reference bus, which should have a generator connected to. Please be aware that by changing the reference bus to another bus than the proposed default value, maybe a powerflow does not converge anymore!

OUTPUT:

net - Returns the required ieee network case2848rte

EXAMPLE:

import pandapower.networks as pn

net = pn.case2848rte()

Case 2869pegase

pandapower.networks.case2869pegase(**kwargs)

This grid represents a part of the European high voltage transmission network. The data is provided by MATPOWER. The data origin i the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

OUTPUT:

net - Returns the required ieee network case2869pegase

EXAMPLE:

import pandapower.networks as pn

net = pn.case2869pegase()

Case 3120sp

pandapower.networks.case3120sp(**kwargs)

This case represents the Polish 400, 220 and 110 kV networks during summer 2008 morning peak conditions. The data was provided by Roman Korab <roman.korab@polsl.pl> and to pandapower converted from MATPOWER.

OUTPUT:

net - Returns the required ieee network case3120sp

EXAMPLE:

import pandapower.networks as pn

net = pn.case3120sp()

Case 6470rte

pandapower.networks.case6470rte(ref_bus_idx=5988, **kwargs)

This case accurately represents the size and complexity of French very high voltage and high voltage transmission network. The data is provided by MATPOWER. The data origin is the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016.

OPTIONAL:

ref_bus_idx - Since the MATPOWER case provides a reference bus without connected generator, because a distributed slack is assumed, to convert the data to pandapower, another bus has been assumed as reference bus. Via ‘ref_bus_idx’ the User can choose a reference bus, which should have a generator connected to. Please be aware that by changing the reference bus to another bus than the proposed default value, maybe a powerflow does not converge anymore!

OUTPUT:

net - Returns the required ieee network case6470rte

EXAMPLE:

import pandapower.networks as pn

net = pn.case6470rte()

Case 6495rte

pandapower.networks.case6495rte(ref_bus_idx=None, **kwargs)

This case accurately represents the size and complexity of French very high voltage and high voltage transmission network. The data is provided by MATPOWER. The data origin is the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016.

OPTIONAL:

ref_bus_idx - Since the MATPOWER case provides a reference bus without connected generator, because a distributed slack is assumed, to convert the data to pandapower, another buses (6077, 6161, 6305, 6306, 6307, 6308) has been assumed as reference bus. Via ‘ref_bus_idx’ the User can choose a reference bus, which should have a generator connected to. Please be aware that by changing the reference bus to another bus than the proposed default value, maybe a powerflow does not converge anymore!

OUTPUT:

net - Returns the required ieee network case6495rte

EXAMPLE:

import pandapower.networks as pn

net = pn.case6495rte()

Case 6515rte

pandapower.networks.case6515rte(ref_bus_idx=6171, **kwargs)

This case accurately represents the size and complexity of French very high voltage and high voltage transmission network. The data is provided by MATPOWER. The data origin is the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016.

OPTIONAL:

ref_bus_idx - Since the MATPOWER case provides a reference bus without connected generator, because a distributed slack is assumed, to convert the data to pandapower, another bus has been assumed as reference bus. Via ‘ref_bus_idx’ the User can choose a reference bus, which should have a generator connected to. Please be aware that by changing the reference bus to another bus than the proposed default value, maybe a powerflow does not converge anymore!

OUTPUT:

net - Returns the required ieee network case6515rte

EXAMPLE:

import pandapower.networks as pn

net = pn.case6515rte()

Case 9241pegase

pandapower.networks.case9241pegase(**kwargs)

This grid represents a part of the European high voltage transmission network. The data is provided by MATPOWER. The data origin are the paper C. Josz, S. Fliscounakis, J. Maenght, P. Panciatici, AC power flow data in MATPOWER and QCQP format: iTesla, RTE snapshots, and PEGASE, 2016 and S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909-4917, Nov 2013..

OUTPUT:

net - Returns the required ieee network case9241pegase

EXAMPLE:

import pandapower.networks as pn

net = pn.case9241pegase()

Case GB network

pandapower.networks.GBnetwork(**kwargs)

Calls the json file GBnetwork.json which data is provided by W. A. Bukhsh, Ken McKinnon, Network data of real transmission networks, April 2013. This data represents detailed model of electricity transmission network of Great Britian (GB). It consists of 2224 nodes, 3207 branches and 394 generators. This data is obtained from publically available data on National grid website. The data was originally pointing out by Manolis Belivanis, University of Strathclyde.

OUTPUT:

net - Returns the required ieee network GBreducednetwork

EXAMPLE:

import pandapower.networks as pn

net = pn.GBnetwork()

Case GB reduced network

pandapower.networks.GBreducednetwork(**kwargs)

Calls the json file GBreducednetwork.json which data is provided by W. A. Bukhsh, Ken McKinnon, Network data of real transmission networks, April 2013. This data is a representative model of electricity transmission network in Great Britain (GB). It was originally developed at the University of Strathclyde in 2010.

OUTPUT:

net - Returns the required ieee network GBreducednetwork

EXAMPLE:

import pandapower.networks as pn

net = pn.GBreducednetwork()

Case iceland

pandapower.networks.iceland(**kwargs)

Calls the json file iceland.json which data is provided by W. A. Bukhsh, Ken McKinnon, Network data of real transmission networks, April 2013. This data represents electricity transmission network of Iceland. It consists of 118 nodes, 206 branches and 35 generators. It was originally developed in PSAT format by Patrick McNabb, Durham University in January 2011.

OUTPUT:

net - Returns the required ieee network iceland

EXAMPLE:

import pandapower.networks as pn

net = pn.iceland()