# Power System Test Cases¶

Note

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

## Case 4gs¶

pandapower.networks.case4gs()

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 6ww¶

pandapower.networks.case6ww()

Calls the pickle file case6ww.p 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()

Calls the pickle file case9.p 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()

Calls the pickle file case14.p 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()

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()

This function calls the pickle file case30.p which data origin is PYPOWER. Some more information about this network are given by Washington case 30 and Illinois University case 30.

OUTPUT:
net - Returns the required ieee network case30
EXAMPLE:

import pandapower.networks as pn

net = pn.case30()

## Case 33bw¶

pandapower.networks.case33bw()

Calls the pickle file case33bw.p 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()

Calls the pickle file case39.p 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)

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()

Calls the pickle file case89pegase.p 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()

OUTPUT:
net - Returns the required ieee network case118
EXAMPLE:

import pandapower.networks as pn

net = pn.case118()

## Case 145¶

pandapower.networks.case145()

Calls the pickle file case145.p 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 300¶

pandapower.networks.case300()

OUTPUT:
net - Returns the required ieee network case300
EXAMPLE:

import pandapower.networks as pn

net = pn.case300()

## Case 1354pegase¶

pandapower.networks.case1354pegase()

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)

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)

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()

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()

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)

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)

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)

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()

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()

Calls the pickle file GBnetwork.p 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()

Calls the pickle file GBreducednetwork.p 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()

Calls the pickle file iceland.p 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()