# pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems

arXiv: https://arxiv.org/abs/1709.06743 · HTML: https://mlsanity.com/paper/1709.06743/

## Key facts

- Cited by 85 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 30 of 85)

- [Learning to run a Power Network Challenge: a Retrospective Analysis](https://mlsanity.com/paper/2103.03104/) (2021)
- [Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks](https://mlsanity.com/paper/2110.14300/) (2021)
- [Foundation Models for the Electric Power Grid](https://mlsanity.com/paper/2407.09434/) (2024)
- [CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations](https://mlsanity.com/paper/2403.17660/) (2024)
- [Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids](https://mlsanity.com/paper/2104.02012/) (2021)
- [PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks](https://mlsanity.com/paper/2311.03415/) (2023)
- [PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems](https://mlsanity.com/paper/2111.05969/) (2021)
- [Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks](https://mlsanity.com/paper/2104.11846/) (2021)
- [Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control](https://mlsanity.com/paper/2508.05702/) (2025)
- [Unsupervised Optimal Power Flow Using Graph Neural Networks](https://mlsanity.com/paper/2210.09277/) (2022)
- [GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Management](https://mlsanity.com/paper/2110.06396/) (2021)
- [Hierarchical Reinforcement Learning for Power Network Topology Control](https://mlsanity.com/paper/2311.02129/) (2023)
- [A Review of Safe Reinforcement Learning Methods for Modern Power Systems](https://mlsanity.com/paper/2407.00304/) (2024)
- [Deep Statistical Solver for Distribution System State Estimation](https://mlsanity.com/paper/2301.01835/) (2023)
- [HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approach](https://mlsanity.com/paper/2405.00629/) (2024)
- [X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysis](https://mlsanity.com/paper/2512.20789/) (2025)
- [Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networks](https://mlsanity.com/paper/2112.13166/) (2021)
- [Use of Graph Neural Networks in Aiding Defensive Cyber Operations](https://mlsanity.com/paper/2401.05680/) (2024)
- [Graph Reinforcement Learning for Power Grids: A Comprehensive Survey](https://mlsanity.com/paper/2407.04522/) (2024)
- [Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education](https://mlsanity.com/paper/2105.08846/) (2021)
- [Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map](https://mlsanity.com/paper/2405.15831/) (2024)
- [GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis](https://mlsanity.com/paper/2608.09921/) (2026)
- [Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer](https://mlsanity.com/paper/2206.03721/) (2022)
- [SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids](https://mlsanity.com/paper/2407.12421/) (2024)
- [Multi-Source Data Fusion for Cyberattack Detection in Power Systems](https://mlsanity.com/paper/2101.06897/) (2021)
- [Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow](https://mlsanity.com/paper/2212.12470/) (2022)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [On data-driven chance constraint learning for mixed-integer optimization problems](https://mlsanity.com/paper/2207.03844/) (2022)
- [Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts](https://mlsanity.com/paper/2108.10781/) (2021)
- [Data-Driven Stochastic AC-OPF using Gaussian Processes](https://mlsanity.com/paper/2207.10781/) (2022)

## Abstract (arXiv)

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.