mlsanityData to 7 Oct 2026

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

arXiv 1709.06743 ↗First posted 20 Sep 2017cs.CEcited by 79

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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.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 12; 2022: 13; 2023: 8; 2024: 17; 2025: 13; 2026: 16.

79 papers.

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  1. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  2. 2021Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networkscited by 52
  3. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  4. 2024Foundation Models for the Electric Power Gridcited by 27
  5. 2021Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Gridscited by 24
  6. 2023PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networkscited by 19
  7. 2021Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networkscited by 15
  8. 2021PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systemscited by 15
  9. 2025Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Controlcited by 15
  10. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 13
  11. 2021GridLearn: Multiagent Reinforcement Learning for Grid-Aware Building Energy Managementcited by 12
  12. 2023Hierarchical Reinforcement Learning for Power Network Topology Controlcited by 11
38 more
  1. 2024HUGO -- Highlighting Unseen Grid Options: Combining Deep Reinforcement Learning with a Heuristic Target Topology Approachcited by 10
  2. 2025X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysiscited by 10
  3. 2023Deep Statistical Solver for Distribution System State Estimationcited by 9
  4. 2021Cyberattack Detection in Large-Scale Smart Grids using Chebyshev Graph Convolutional Networkscited by 8
  5. 2024Use of Graph Neural Networks in Aiding Defensive Cyber Operationscited by 7
  6. 2021Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and educationcited by 6
  7. 2024Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Mapcited by 6
  8. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  9. 2022Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformercited by 5
  10. 2024SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Gridscited by 5
  11. 2022Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flowcited by 4
  12. 2026GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysiscited by 4
  13. 2021Multi-Source Data Fusion for Cyberattack Detection in Power Systemscited by 3
  14. 2024CommonPower: A Framework for Safe Data-Driven Smart Grid Controlcited by 3
  15. 2021Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecastscited by 2
  16. 2022On data-driven chance constraint learning for mixed-integer optimization problemscited by 2
  17. 2022Data-Driven Stochastic AC-OPF using Gaussian Processescited by 2
  18. 2023Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learningcited by 2
  19. 2024Safety Constrained Multi-Agent Reinforcement Learning for Active Voltage Controlcited by 2
  20. 2024Optimal Power Grid Operations with Foundation Modelscited by 2
  21. 2022Sampling Strategies for Static Powergrid Modelscited by 1
  22. 2022Multi-fidelity power flow solvercited by 1
  23. 2022Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processescited by 1
  24. 2022Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learningcited by 1
  25. 2023ANALYSE -- Learning to Attack Cyber-Physical Energy Systems With Intelligent Agentscited by 1
  26. 2024End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availabilitycited by 1
  27. 2025Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessmentcited by 1
  28. 2025Partially-Supervised Neural Network Model For Quadratic Multiparametric Programmingcited by 1
  29. 2025Physics-Informed Inductive Biases for Voltage Prediction in Distribution Gridscited by 1
  30. 2025Reinforcement Learning to Initialize Newton-Raphson for AC Power Flow with Quantum Annealing-Based Environment Updatescited by 1
  31. 2025T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Methodcited by 1
  32. 2026Knowledge boundary probing and demand-guided intervention for LLM-based power system code generationcited by 1
  33. 2026Audited Selective Verification for Risk-Controlled N-1 Thermal Contingency Screening under Deployment Shiftcited by 1
  34. 2026Task-Aware Harness Provisioning for LLM Agents in Mission-Critical Infrastructure Operationscited by 1
  35. 2021Bayesian Error-in-Variables Models for the Identification of Power Networkscited by 0
  36. 2021Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedulescited by 0
  37. 2022OpenGridGym: An Open-Source AI-Friendly Toolkit for Distribution Market Simulationcited by 0
  38. 2022Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0

50 most cited of 79 All 79 (JSON)