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The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms

arXiv 1908.02788 ↗First posted 7 Aug 2019math.OCcited by 63

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Abstract (arXiv)

In recent years, the power systems research community has seen an explosion of novel methods for formulating the AC power flow equations. Consequently, benchmarking studies using the seminal AC Optimal Power Flow (AC-OPF) problem have emerged as the primary method for evaluating these emerging methods. However, it is often difficult to directly compare these studies due to subtle differences in the AC-OPF problem formulation as well as the network, generation, and loading data that are used for evaluation. To help address these challenges, this IEEE PES Task Force report proposes a standardized AC-OPF mathematical formulation and the PGLib-OPF networks for benchmarking AC-OPF algorithms. A motivating study demonstrates some limitations of the established network datasets in the context of benchmarking AC-OPF algorithms and a validation study demonstrates the efficacy of using the PGLib-OPF networks for this purpose. In the interest of scientific discourse and future additions, the PGLib-OPF benchmark library is open-access and all the of network data is provided under a creative commons license.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Aug 2019.

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Citing papers per year: 2021: 12; 2022: 6; 2023: 11; 2024: 11; 2025: 15; 2026: 8.

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  1. 2022Self-Supervised Primal-Dual Learning for Constrained Optimizationcited by 60
  2. 2021Learning to run a Power Network Challenge: a Retrospective Analysiscited by 52
  3. 2021Physics-Informed Neural Networks for AC Optimal Power Flowcited by 52
  4. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  5. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  6. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  7. 2024Foundation Models for the Electric Power Gridcited by 27
  8. 2021Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flowcited by 23
  9. 2024OPFData: Large-scale datasets for AC optimal power flow with topological perturbationscited by 22
  10. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 21
  11. 2021Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitmentcited by 17
  12. 2021OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasetscited by 17
38 more
  1. 2023Compact Optimization Learning for AC Optimal Power Flowcited by 11
  2. 2023Dual Conic Proxies for AC Optimal Power Flowcited by 11
  3. 2025PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flowcited by 11
  4. 2025Enforcing Hard Linear Constraints in Deep Learning Models with Decision Rulescited by 10
  5. 2021Data-Driven Time Series Reconstruction for Modern Power Systems Researchcited by 7
  6. 2023Learning Optimal Power Flow Value Functions with Input-Convex Neural Networkscited by 7
  7. 2021Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problemscited by 6
  8. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 6
  9. 2021Graph Neural Networks for Learning Real-Time Prices in Electricity Marketcited by 5
  10. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 5
  11. 2022Global Performance Guarantees for Neural Network Models of AC Power Flowcited by 4
  12. 2024Dual Interior Point Optimization Learningcited by 4
  13. 2025Sparser, Better, Faster, Stronger: Sparsity Detection for Efficient Automatic Differentiationcited by 4
  14. 2025PFDelta: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variationscited by 4
  15. 2026GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysiscited by 4
  16. 2022Bucketized Active Sampling for Learning ACOPFcited by 3
  17. 2022Minimizing Worst-Case Violations of Neural Networkscited by 3
  18. 2023Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flowcited by 3
  19. 2024Learning To Solve Differential Equation Constrained Optimization Problemscited by 3
  20. 2021Application-Driven Learning: A Closed-Loop Prediction and Optimization Approach Applied to Dynamic Reserves and Demand Forecastingcited by 2
  21. 2023Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimizationcited by 2
  22. 2024Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flowcited by 2
  23. 2024Compact Optimality Verification for Optimization Proxiescited by 2
  24. 2024Optimal Power Grid Operations with Foundation Modelscited by 2
  25. 2024Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approachcited by 2
  26. 2025Tightening optimality gap with confidence through conformal predictioncited by 2
  27. 2025Sobolev Training of End-to-End Optimization Proxiescited by 2
  28. 2025Constrained Diffusion Models for Synthesizing Representative Power Flow Datasetscited by 2
  29. 2025DiffOPF: Diffusion Solver for Optimal Power Flowcited by 2
  30. 2025gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generationcited by 2
  31. 2023An Efficient Learning-Based Solver for Two-Stage DC Optimal Power Flow with Feasibility Guaranteescited by 1
  32. 2025Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flowcited by 1
  33. 2025Solving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the AC Optimal Power Flowcited by 1
  34. 2026MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Modelcited by 1
  35. 2021Load Encoding for Learning AC-OPFcited by 0
  36. 2021Realistic Differentially-Private Transmission Power Flow Data Releasecited by 0
  37. 2022Sparse Polynomial Optimization: Theory and Practicecited by 0
  38. 2023Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guaranteescited by 0

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