Pandapower—An Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems
By Leon Thurner, Alexander Scheidler, Florian Schäfer, Jan-Hendrik Menke, Julian Dollichon, F. Meier, Steffen Meinecke, Martin Braun. IEEE Transactions on Power Systems, 2017
1,050 citations recorded by the source corpus.
Pandapower is a Python-based BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches, and short-circuit calculations according to IEC 60909. pandapower includes a Newton–Raphson power flow solver formerly based on pypower, which has been accelerated with just-in-time compilation. Additional enhancements to the solver include the capability to model constant current loads, grids with multiple reference nodes, and a connectivity check. The pandapower network model is based on electric elements, such as lines, two- and three-winding transformers, or ideal switches. All elements can be defined with nameplate parameters and are internally processed with equivalent circuit models, which have been validated against industry standard software tools. The tabular data structure used to define networks is based on the Python library pandas, which allows comfortable handling of input and output parameters. The implementation in Python makes pandapower easy to use and allows comfortable extension with third-party libraries. pandapower has been successfully applied in several grid studies as well as for educational purposes. A comprehensive publicly available case study demonstrates a possible application of pandapower in an automated time-series calculation.
Subfields: Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/b13747e12272af95eb5c8476902b55342fdb41a2
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In Control and Systems Engineering
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- Tom Brown
- David P. Schlachtberger
- Jonas Horsch
- Steffen Meinecke
- Alejandro Ribeiro
- Martin Braun
- Fernando Gama
- Damian Owerko
Citations in the corpus
- SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis (2020, methodology): “These include the format of the open-source power system analyzing and optimizing tool pandapower [22], formats of the commercial tools DIgSILENT PowerFactory [23] and INTEGRAL [24] and a format especially created for the SimBench data.”
- Optimal Power Flow Using Graph Neural Networks (2019, methodology): “More specifically, we adopt a parametrized GNN model, which is local and scalable by design, and we train it to imitate the optimal solution obtained using an interior point solver [16], which is centralized and does not converge for large networks.”
- PyPSA: Python for Power System Analysis (2017, methodology): “Examples of such tools include commercial products like DIgSILENT PowerFactory [1], NEPLAN [2], PowerWorld [3], PSS/E [4] and PSS/SINCAL [5], and open tools such as MATPOWER [6], PSAT [7], PYPOWER [8] and pandapower [9] (see [10] for a full list of power system analysis tools).”