mlsanityData to 14 Oct 2026

Compact Optimization Learning for AC Optimal Power Flow

arXiv 2301.08840 ↗First posted 21 Jan 2023cs.LGcited by 11

Not on the map. Why?

Abstract (arXiv)

This paper reconsiders end-to-end learning approaches to the Optimal Power Flow (OPF). Existing methods, which learn the input/output mapping of the OPF, suffer from scalability issues due to the high dimensionality of the output space. This paper first shows that the space of optimal solutions can be significantly compressed using principal component analysis (PCA). It then proposes Compact Learning, a new method that learns in a subspace of the principal components before translating the vectors into the original output space. This compression reduces the number of trainable parameters substantially, improving scalability and effectiveness. Compact Learning is evaluated on a variety of test cases from the PGLib with up to 30,000 buses. The paper also shows that the output of Compact Learning can be used to warm-start an exact AC solver to restore feasibility, while bringing significant speed-ups.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 21 Jan 2023.

Builds on

25 of 48 references.

References · 25

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2021DC3: A learning method for optimization with hard constraintscited by 184
  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. 2019Optimal Power Flow Using Graph Neural Networkscited by 63
  7. 2022Self-Supervised Primal-Dual Learning for Constrained Optimizationcited by 61
  8. 2021Physics-Informed Neural Networks for AC Optimal Power Flowcited by 54
  9. 2020DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problemscited by 47
  10. 2019Learning to Solve Large-Scale Security-Constrained Unit Commitment Problemscited by 40
  11. 2017PowerModels.jl: An Open-Source Framework for Exploring Power Flow Formulationscited by 33
  12. 2019Learning Warm-Start Points for AC Optimal Power Flowcited by 32
13 more
  1. 2019Learning for DC-OPF: Classifying active sets using neural netscited by 31
  2. 2021Learning Optimization Proxies for Large-Scale Security-Constrained Economic Dispatchcited by 29
  3. 2020Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulationcited by 19
  4. 2020Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approachcited by 18
  5. 2021Spatial Network Decomposition for Fast and Scalable AC-OPF Learningcited by 15
  6. 2022Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitmentscited by 12
  7. 2021Data-Driven Time Series Reconstruction for Modern Power Systems Researchcited by 7
  8. 2022Learning Regionally Decentralized AC Optimal Power Flows with ADMMcited by 7
  9. 2022Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operationscited by 6
  10. 2018Optimization-Based Bound Tightening using a Strengthened QC-Relaxation of the Optimal Power Flow Problemcited by 3
  11. 2019Inexact Convex Relaxations for AC Optimal Power Flow: Towards AC Feasibilitycited by 3
  12. 2022Benchmarking Large-Scale ACOPF Solutions and Optimality Boundscited by 2
  13. 2022Restoring AC Power Flow Feasibility from Relaxed and Approximated Optimal Power Flow Modelscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 1; 2025: 5; 2026: 1.

11 papers.

  1. 2023End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatchcited by 35
  2. 2025FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guaranteescited by 21
  3. 2023Dual Conic Proxies for AC Optimal Power Flowcited by 11
  4. 2023AI4OPT: AI Institute for Advances in Optimizationcited by 6
  5. 2025Towards Generalization of Graph Neural Networks for AC Optimal Power Flowcited by 6
  6. 2023Self-Supervised Learning for Large-Scale Preventive Security Constrained DC Optimal Power Flowcited by 3
  7. 2024Compact Optimality Verification for Optimization Proxiescited by 2
  8. 2025DiffOPF: Diffusion Solver for Optimal Power Flowcited by 2
  9. 2026Learning to accelerate Krasnosel'skii-Mann fixed-point iterations with guaranteescited by 2
  10. 2025Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatchcited by 0
  11. 2025Homotopy-Guided Self-Supervised Learning of Parametric Solutions for AC Optimal Power Flowcited by 0