Supervised Learning for Optimal Power Flow as a Real-Time Proxy

2016/12/20 by Raphael Canyasse, Canyasse, Raphael, Gal Dalal +3 · 1 citation
Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Optimal Power Flow Distribution #Power System Optimization and Stability #Power System Reliability and Maintenance

paper · pdf · doi:10.48550/arxiv.1612.06623

openalex publication_date 2016/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.

Citations

• The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee
• MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education
• Hierarchical Decision Making In Electricity Grid Management
• Unit Commitment using Nearest Neighbor as a Short-Term Proxy

Cited by

• Gaussian Processes in Power Systems: Techniques, Applications, and Future Works

Related

• Machine Learning for AC Optimal Power Flow
• Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks
• Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
• Beyond the Neural Fog: Interpretable Learning for AC Optimal Power Flow
• Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF