README.rst

pandapower

pandapower combines the data analysis library pandas and the power flow solver PYPOWER to create an easy to use network calculation program aimed at automation of analysis and optimization in power systems.

pandapower is a joint development of the research group Energy Management and Power System Operation, University of Kassel and the Department for Distribution System Operation at the Fraunhofer Institute for Wind Energy and Energy System Technology (IWES), Kassel.

Element Models

pandapower is an element based network calculation tool that supports the following components:

• lines
• two-winding and three-winding transformers
• ideal bus-bus and bus-branch switches
• static generators
• ZIP loads
• shunts
• external grid connections
• synchronous generators
• DC lines
• unsymmetric impedances
• ward equivalents

Network Analysis

pandapower supports the following network analysis functions:

• power flow
• optimal power flow
• state estimation
• short-circuit calculation according to IEC 60909
• topological graph searches

For more information, please refer to the documentation.

Installation notes can be found here, for a comfortable introduction into pandapower see the interactive tutorials.

If you are interested in getting release notes for new pandapower versions, please subscribe to the pandapower mailing list.

There is a project to develop a GUI for pandapower: https://github.com/Tooblippe/pandapower_gui - developers wanted!

Citing pandapower

We are currently working on publishing a paper about pandapower, which is available as a preprint on arxiv. Until the paper has passed peer-review, please acknowledge the usage of pandapower in your paper as follows:

@online{pandapower,
   author = {{Thurner}, L. and {Scheidler}, A. and {Sch{\"a}fer}, F. and {Menke}, J.-H. and {Dollichon}, J. and {Meier}, F. and {Meinecke}, S. and {Braun}, M.},
   title = "{pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems}",
   year = 2017,
   url = {https://arxiv.org/abs/1709.06743},
   note = {preprint}
}

Minimal Example

A network in pandapower is represented in a pandapowerNet object, which is a collection of pandas Dataframes. Each dataframe in a pandapowerNet contains the information about one pandapower element, such as line, load transformer etc.

We consider the following simple 3-bus example network as a minimal example:

Creating a Network

The above network can be created in pandapower as follows:

import pandapower as pp
#create empty net
net = pp.create_empty_network()

#create buses
b1 = pp.create_bus(net, vn_kv=20., name="Bus 1")
b2 = pp.create_bus(net, vn_kv=0.4, name="Bus 2")
b3 = pp.create_bus(net, vn_kv=0.4, name="Bus 3")

#create bus elements
pp.create_ext_grid(net, bus=b1, vm_pu=1.02, name="Grid Connection")
pp.create_load(net, bus=b3, p_kw=100, q_kvar=50, name="Load")

#create branch elements
tid = pp.create_transformer(net, hv_bus=b1, lv_bus=b2, std_type="0.4 MVA 20/0.4 kV",
                            name="Trafo")
pp.create_line(net, from_bus=b2, to_bus=b3, length_km=0.1, name="Line",
               std_type="NAYY 4x50 SE")

Note that you do not have to calculate any impedances or tap ratio for the equivalent circuit, this is handled internally by pandapower according to the pandapower transformer model. The standard type library allows comfortable creation of line and transformer elements.

The pandapower representation now looks like this:

Running a Power Flow

A powerflow can be carried out with the runpp function:

pp.runpp(net)

When a power flow is run, pandapower combines the information of all element tables into one pypower case file and uses pypower to run the power flow. The results are then processed and written back into pandapower:

For the 3-bus example network, the result tables look like this:

All other pandapower elements and network analysis functionality (e.g. optimal power flow, state estimation or short-circuit calculation) is also fully integrated into the tabular pandapower datastructure.

This minimal example is also available as a jupyter notebook.