mlsanityData to 14 Oct 2026

OPFData: Large-scale datasets for AC optimal power flow with topological perturbations

arXiv 2406.07234 ↗First posted 11 Jun 2024cs.LGcited by 24

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Abstract (arXiv)

Solving the AC optimal power flow problem (AC-OPF) is critical to the efficient and safe planning and operation of power grids. Small efficiency improvements in this domain have the potential to lead to billions of dollars of cost savings, and significant reductions in emissions from fossil fuel generators. Recent work on data-driven solution methods for AC-OPF shows the potential for large speed improvements compared to traditional solvers; however, no large-scale open datasets for this problem exist. We present the largest readily-available collection of solved AC-OPF problems to date. This collection is orders of magnitude larger than existing readily-available datasets, allowing training of high-capacity data-driven models. Uniquely, it includes topological perturbations - a critical requirement for usage in realistic power grid operations. We hope this resource will spur the community to scale research to larger grid sizes with variable topology.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 15 of its 42 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

15 of 42 references.

References · 15

  1. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  2. 2019Tackling Climate Change with Machine Learningcited by 265
  3. 2019Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methodscited by 94
  4. 2019Learning Optimal Solutions for Extremely Fast AC Optimal Power Flowcited by 77
  5. 2019The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithmscited by 65
  6. 2017PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulationscited by 33
  7. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  8. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  9. 2021OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasetscited by 18
  10. 2016AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASEcited by 17
  11. 2019Solutions of DC OPF are Never AC Feasiblecited by 16
  12. 2023Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generationcited by 3
3 more
  1. 2023Graph Neural Networks for Fast Contingency Analysis of Power Systemscited by 3
  2. 2023Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasetscited by 2
  3. 2022Learning-based AC-OPF Solvers on Realistic Network and Realistic Loadscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 6; 2026: 13.

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  1. 2024Foundation Models for the Electric Power Gridcited by 28
  2. 2025PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flowcited by 11
  3. 2026LUMINA: Foundation Models for Topology Transferable ACOPFcited by 8
  4. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 6
  5. 2026GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysiscited by 6
  6. 2024Improving Graph Neural Networks by Learning Continuous Edge Directionscited by 3
  7. 2025gridfm-datakit-v1: A Python Library for Scalable and Realistic Power Flow and Optimal Power Flow Data Generationcited by 3
  8. 2024Optimal Power Grid Operations with Foundation Modelscited by 2
  9. 2024Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approachcited by 2
  10. 2025Constrained Diffusion Models for Synthesizing Representative Power Flow Datasetscited by 2
  11. 2025Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operatorcited by 2
  12. 2026LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learningcited by 2
12 more
  1. 2026Parameter-Efficient Domain Adaptation of Physics-Informed Self-Attention based GNNs for AC Power Flow Predictioncited by 1
  2. 2026Cheap Thrills: Effective Amortized Optimization Using Inexpensive Labelscited by 1
  3. 2026Towards Systematic Generalization for Power Grid Optimization Problemscited by 1
  4. 2024Accelerating Quasi-Static Time Series Simulations with Foundation Modelscited by 0
  5. 2025Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutionscited by 0
  6. 2026Generating adversarial inputs for a graph neural network model of AC power flowcited by 0
  7. 2026Can Large Language Models Reason and Optimize Under Constraints?cited by 0
  8. 2026Rethinking Neural Width for Alternating Current Optimal Power Flow Proxiescited by 0
  9. 2026Navigating the Safety-Fidelity Trade-off: Massive-Variate Time Series Forecasting for Power Systems via Probabilistic Scenarioscited by 0
  10. 2026Scaling Laws for Physics-Aware ACOPF Surrogate Learningcited by 0
  11. 2026GridSFM: A Foundation Model for Solving AC Optimal Power Flowcited by 0
  12. 2026Training Numerical Intelligence via Auto-Diagnosis and Skill Discoverycited by 0