mlsanityData to 7 Oct 2026

DeepOPF-V: Solving AC-OPF Problems Efficiently

arXiv 2103.11793 ↗First posted 22 Mar 2021eess.SY cs.AI cs.LGcited by 18

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Abstract (arXiv)

AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation. To tackle this challenge, a deep neural network-based voltage-constrained approach (DeepOPF-V) is proposed to solve AC-OPF problems with high computational efficiency. Its unique design predicts voltages of all buses and then uses them to reconstruct the remaining variables without solving non-linear AC power flow equations. A fast post-processing process is developed to enforce the box constraints. The effectiveness of DeepOPF-V is validated by simulations on IEEE 118/300-bus systems and a 2000-bus test system. Compared with existing studies, DeepOPF-V achieves decent computation speedup up to four orders of magnitude and comparable performance in optimality gap and preserving the feasibility of the solution.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 22 Mar 2021.

Builds on

9 of 16 references.

References · 9

  1. 2019Learning Optimal Solutions for Extremely Fast AC Optimal Power Flowcited by 76
  2. 2019DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flowcited by 50
  3. 2020DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problemscited by 45
  4. 2019DeepOPF: Deep Neural Network for DC Optimal Power Flowcited by 32
  5. 2020Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulationcited by 19
  6. 2020Learning to Solve Network Flow Problems via Neural Decodingcited by 9
  7. 2020Hybrid Learning Aided Inactive Constraints Filtering Algorithm to Enhance AC OPF Solution Timecited by 6
  8. 2020Emulating AC OPF solvers for Obtaining Sub-second Feasible, Near-Optimal Solutionscited by 5
  9. 2014Sparsity-Exploiting Moment-Based Relaxations of the Optimal Power Flow Problemcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 4; 2024: 0; 2025: 4; 2026: 6.

18 papers.

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  1. 2021OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasetscited by 17
  2. 2023Dual Conic Proxies for AC Optimal Power Flowcited by 11
  3. 2026LUMINA: Foundation Models for Topology Transferable ACOPFcited by 7
  4. 2021Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problemscited by 6
  5. 2022Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Dualitycited by 3
  6. 2023Optimal Power Flow Based on Physical-Model-Integrated Neural Network with Worth-Learning Data Generationcited by 3
  7. 2023DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networkscited by 3
  8. 2026LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learningcited by 2
  9. 2022DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappingscited by 1
  10. 2025HoP: Homeomorphic Polar Learning for Hard Constrained Optimizationcited by 1
  11. 2025Dual Conic Proxy for Semidefinite Relaxation of AC Optimal Power Flowcited by 1
  12. 2023DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilitiescited by 0
6 more
  1. 2025Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPFcited by 0
  2. 2025Revisiting Deep AC-OPFcited by 0
  3. 2026Rethinking Neural Width for Alternating Current Optimal Power Flow Proxiescited by 0
  4. 2026Scalable Self-Supervised Learning for Multiphase AC-OPF in Distribution Systems with Topology Reconfigurationcited by 0
  5. 2026Scaling Laws for Physics-Aware ACOPF Surrogate Learningcited by 0
  6. 2026Learning Samples Importance: Parameterizing Dual Variables in Everywhere Learningcited by 0