mlsanityData to 14 Oct 2026

PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulations

arXiv 1711.01728 ↗First posted 6 Nov 2017math.OC cs.CEcited by 33

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Abstract (arXiv)

In recent years, the power system research community has seen an explosion of novel methods for formulating and solving power network optimization problems. These emerging methods range from new power flow approximations, which go beyond the traditional DC power flow by capturing reactive power, to convex relaxations, which provide solution quality and runtime performance guarantees. Unfortunately, the sophistication of these emerging methods often presents a significant barrier to evaluating them on a wide variety of power system optimization applications. To address this issue, this work proposes PowerModels, an open-source platform for comparing power flow formulations. From its inception, PowerModels was designed to streamline the process of evaluating different power flow formulations on shared optimization problem specifications. This work provides a brief introduction to the design of PowerModels, validates its implementation, and demonstrates its effectiveness with a proof-of-concept study analyzing five different formulations of the Optimal Power Flow problem.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2017.

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Citing papers per year: 2021: 7; 2022: 4; 2023: 3; 2024: 6; 2025: 8; 2026: 5.

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  1. 2022Self-Supervised Primal-Dual Learning for Constrained Optimizationcited by 61
  2. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  3. 2024OPFData: Large-scale datasets for AC optimal power flow with topological perturbationscited by 24
  4. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 22
  5. 2021Learning Hard Optimization Problems: A Data Generation Perspectivecited by 21
  6. 2021OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasetscited by 18
  7. 2021Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitmentcited by 17
  8. 2025GridMind: LLMs-Powered Agents for Power System Analysis and Operationscited by 16
  9. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 14
  10. 2023Compact Optimization Learning for AC Optimal Power Flowcited by 11
  11. 2023Dual Conic Proxies for AC Optimal Power Flowcited by 11
  12. 2024A Review of Safe Reinforcement Learning Methods for Modern Power Systemscited by 11
21 more
  1. 2025PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flowcited by 11
  2. 2025PFDelta: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variationscited by 7
  3. 2021Adversarially Robust Learning for Security-Constrained Optimal Power Flowcited by 6
  4. 2026GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysiscited by 6
  5. 2024SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Gridscited by 5
  6. 2022Global Performance Guarantees for Neural Network Models of AC Power Flowcited by 4
  7. 2021Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operationscited by 3
  8. 2022Bucketized Active Sampling for Learning ACOPFcited by 3
  9. 2025gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generationcited by 3
  10. 2024Optimal Power Grid Operations with Foundation Modelscited by 2
  11. 2024Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approachcited by 2
  12. 2025Sobolev Training of End-to-End Optimization Proxiescited by 2
  13. 2025Decoupled-Value Attention for Prior-Data Fitted Networks: GP Inference for Physical Equationscited by 1
  14. 2025Nonlinear Optimization with GPU-Accelerated Neural Network Constraintscited by 1
  15. 2021Realistic Differentially-Private Transmission Power Flow Data Releasecited by 0
  16. 2023Price-Aware Deep Learning for Electricity Marketscited by 0
  17. 2025Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutionscited by 0
  18. 2026Generating adversarial inputs for a graph neural network model of AC power flowcited by 0
  19. 2026Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineeringcited by 0
  20. 2026Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learningcited by 0
  21. 2026ML-OPF-Bench: Benchmarking Machine Learning for Optimal Power Flowcited by 0