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Machine Learning for AC Optimal Power Flow

arXiv 1910.08842 ↗First posted 19 Oct 2019cs.LG eess.SP stat.MLcited by 24

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Abstract (arXiv)

We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Oct 2019.

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Citing papers per year: 2021: 8; 2022: 3; 2023: 3; 2024: 4; 2025: 4; 2026: 2.

24 papers.

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  1. 2024Neural Network Verification with Branch-and-Bound for General Nonlinearitiescited by 47
  2. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  3. 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
  4. 2021Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networkscited by 26
  5. 2021Leveraging power grid topology in machine learning assisted optimal power flowcited by 21
  6. 2023Large Foundation Models for Power Systemscited by 16
  7. 2022Unsupervised Optimal Power Flow Using Graph Neural Networkscited by 13
  8. 2021Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problemscited by 6
  9. 2021Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approachcited by 5
  10. 2021Graph Neural Networks for Learning Real-Time Prices in Electricity Marketcited by 5
  11. 2021Learning to Solve the AC Optimal Power Flow via a Lagrangian Approachcited by 5
  12. 2024QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraintscited by 5
12 more
  1. 2024SoundnessBench: A Soundness Benchmark for Neural Network Verifierscited by 5
  2. 2021Scalable Learning for Optimal Load Shedding Under Power Grid Emergency Operationscited by 3
  3. 2022Bucketized Active Sampling for Learning ACOPFcited by 3
  4. 2024Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Gridscited by 3
  5. 2025DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Networkcited by 3
  6. 2025From Natural Language to Solver-Ready Power System Optimization: An LLM-Assisted, Validation-in-the-Loop Frameworkcited by 2
  7. 2022Sampling Strategies for Static Powergrid Modelscited by 1
  8. 2023Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networkscited by 1
  9. 2025HoP: Homeomorphic Polar Learning for Hard Constrained Optimizationcited by 1
  10. 2025Constraint-Informed Active Learning for End-to-End ACOPF Optimization Proxiescited by 1
  11. 2026Rethinking Neural Width for Alternating Current Optimal Power Flow Proxiescited by 0
  12. 2026Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learningcited by 0