Power System Test Cases

Note

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

Case 4gs

pandapower.networks.case4gs()

Calls the pickle file case4gs.p which 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.

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()

Calls the pickle file case24_ieee_rts.p which data origin is PYPOWER. Some more information about this network are given by Illinois University case 24.

OUTPUT:
net - Returns the required ieee network case24
EXAMPLE:

import pandapower.networks as pn

net = pn.case24_ieee_rts()


Case 30

pandapower.networks.case30()

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. 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 118

pandapower.networks.case118()

Calls the pickle file case118.p which data origin is PYPOWER. Some more information about this network are given by Washington case 118 and Illinois University case 118.

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

import pandapower.networks as pn

net = pn.case118()

Case 300

pandapower.networks.case300()

Calls the pickle file case300.p 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()

Calls the pickle file case1354pegase.p which 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.

OUTPUT:
net - Returns the required ieee network case1354pegase
EXAMPLE:

import pandapower.networks as pn

net = pn.case1354pegase()

Case 2869pegase

pandapower.networks.case2869pegase()

Calls the pickle file case33bw.p which 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.

OUTPUT:
net - Returns the required ieee network case2869pegase
EXAMPLE:

import pandapower.networks as pn

net = pn.case300()

Case 9241pegase

pandapower.networks.case9241pegase()

Calls the pickle file case33bw.p which 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.

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()