DeepOPF-V: Solving AC-OPF Problems Efficiently

2021/03/22 by Wanjun Huang, Xiang Pan, Huang, Wanjun +5 · 5 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Computation #Computer science #Control theory (sociology) #Electric Power System Optimization #Electric power system #Electrical engineering #Engineering #Flow (mathematics) #Mathematical optimization #Mathematics #Optimal Power Flow Distribution #Parallel computing #Power (physics) #Power System Optimization and Stability #Power flow #Process (computing) #Speedup #Voltage #cs.AI #cs.LG #cs.SY #eess.SY

paper · pdf · doi:10.48550/arxiv.2103.11793

published in arXiv (Cornell University) (Cornell University) · 4 pages, 1 figure

openalex publication_date 2021/03/22 · arxiv created 2021/07/19 · arxiv updated 2021/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06

Abstract

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.

Cited by

• Learning Optimal Power Flow with Pointwise Constraints
• MPA-DNN: Projection-Aware Unsupervised Learning for Multi-period DC-OPF
• Revisiting Deep AC-OPF
• Dynamic Domain Adaptation-Driven Physics-Informed Graph Representation Learning for AC-OPF
• Optimization Under Uncertainty for Energy Infrastructure Planning: A Synthesis of Methods, Tools, and Open Challenges

Related

• DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
• DeepOPF-U: A Unified Deep Neural Network to Solve AC Optimal Power Flow in Multiple Networks
• DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility
• DeepOPF: Deep Neural Network for DC Optimal Power Flow
• DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow