mlsanityData to 7 Oct 2026

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

arXiv 1910.01213 ↗First posted 27 Sep 2019cs.LG cs.SY eess.SPcited by 76

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Abstract (arXiv)

In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e.g., milliseconds), bypassing solving an AC OPF altogether. This is motivated by the fact that as the power grid experiences increasing amounts of renewable power generation, controllable loads, and other inverter-interfaced devices, faster system dynamics and quicker fluctuations in the power supply are likely to occur. Currently, grid operators typically solve AC OPF every 15 minutes to determine economic generator settings while ensuring grid constraints are satisfied. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or approximating the problem in order to solve the AC OPF on faster timescales. However, many of these approximations can be fairly poor representations of the actual system state and still require solving an optimization problem, which can be time consuming for large networks. In this work, we leverage historical data to learn a mapping between the system loading and optimal generation values, enabling us to find near-optimal and feasible AC OPF solutions on extremely fast timescales without actually solving an optimization problem.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Sep 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 18; 2022: 13; 2023: 10; 2024: 10; 2025: 17; 2026: 8.

76 papers.

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  1. 2021DC3: A learning method for optimization with hard constraintscited by 181
  2. 2022Tutorial on amortized optimizationcited by 83
  3. 2022Self-Supervised Primal-Dual Learning for Constrained Optimizationcited by 60
  4. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  5. 2023Learning to Warm-Start Fixed-Point Optimization Algorithmscited by 32
  6. 2021Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimizationcited by 31
  7. 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
  8. 2022Topology-aware Graph Neural Networks for Learning Feasible and Adaptive ac-OPF Solutionscited by 28
  9. 2024CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbationscited by 27
  10. 2021Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networkscited by 26
  11. 2021Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flowcited by 23
  12. 2024OPFData: Large-scale datasets for AC optimal power flow with topological perturbationscited by 22
38 more
  1. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 21
  2. 2021Power Flow Balancing with Decentralized Graph Neural Networkscited by 19
  3. 2025FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guaranteescited by 19
  4. 2021DeepOPF-V: Solving AC-OPF Problems Efficientlycited by 18
  5. 2021OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasetscited by 17
  6. 2025Pinet: Optimizing hard-constrained neural networks with orthogonal projection layerscited by 17
  7. 2021Spatial Network Decomposition for Fast and Scalable AC-OPF Learningcited by 15
  8. 2022Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitmentscited by 12
  9. 2023Compact Optimization Learning for AC Optimal Power Flowcited by 11
  10. 2023Dual Conic Proxies for AC Optimal Power Flowcited by 11
  11. 2021Data-Driven Time Series Reconstruction for Modern Power Systems Researchcited by 7
  12. 2021DNN-based Policies for Stochastic AC OPFcited by 7
  13. 2022Learning Regionally Decentralized AC Optimal Power Flows with ADMMcited by 7
  14. 2021Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problemscited by 6
  15. 2022Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Supportcited by 6
  16. 2021Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approachcited by 5
  17. 2021Graph Neural Networks for Learning Real-Time Prices in Electricity Marketcited by 5
  18. 2021Learning to Solve the AC Optimal Power Flow via a Lagrangian Approachcited by 5
  19. 2024Learning the Optimal Power Flow: Environment Design Matterscited by 5
  20. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 5
  21. 2022Global Performance Guarantees for Neural Network Models of AC Power Flowcited by 4
  22. 2023Data-driven decision-focused surrogate modelingcited by 4
  23. 2023An Expandable Machine Learning-Optimization Framework to Sequential Decision-Makingcited by 4
  24. 2024Calabi-Yau metrics through Grassmannian learning and Donaldson's algorithmcited by 4
  25. 2022Bucketized Active Sampling for Learning ACOPFcited by 3
  26. 2024Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Modelscited by 3
  27. 2024Learning To Solve Differential Equation Constrained Optimization Problemscited by 3
  28. 2022Data-Driven Stochastic AC-OPF using Gaussian Processescited by 2
  29. 2023Self-supervised Equality Embedded Deep Lagrange Dual for Approximate Constrained Optimizationcited by 2
  30. 2024Neural Network Approximators for Marginal MAP in Probabilistic Circuitscited by 2
  31. 2024Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approachcited by 2
  32. 2025Constrained Diffusion Models for Synthesizing Representative Power Flow Datasetscited by 2
  33. 2025Flow-Aware GNN for Transmission Network Reconfiguration via Substation Breaker Optimizationcited by 2
  34. 2025DiffOPF: Diffusion Solver for Optimal Power Flowcited by 2
  35. 2026Improving Feasibility via Fast Autoencoder-Based Projectionscited by 2
  36. 2022Sampling Strategies for Static Powergrid Modelscited by 1
  37. 2022DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappingscited by 1
  38. 2022Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systemscited by 1

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