PowerModels-ACOPF-AI: On-the-Fly Machine Learning Approach for Solving AC Optimal Power Flow Integrating Renewable Energy Sources

Abstract

The increasing complexity of modern power systems, driven by high renewable penetration, load variability, and operational uncertainty, demands fast and reliable solutions to the AC optimal power flow problem (AC-OPF). Traditional optimization methods, though accurate, often struggle with scalability and high computational burdens, making them impractical for real-time use in large networks. This paper introduces PowerModels-ACOPF-AI, a two-stage Bayesian Neural Network (BNN) surrogate designed to predict generator set points, bus voltages, and phase angles with uncertainty. The framework integrates a performance-sensitive on-the-fly learning mechanism that identifies regions of degraded prediction accuracy and dynamically retrains with additional AC-OPF solutions generated by PowerModels.jl. This self-adaptive loop ensures robust performance, enabling the model to maintain accuracy under novel or highly variable operating conditions. Validation on benchmark test systems of different sizes, namely the 30-bus, 200-bus, and 500-bus networks, demonstrates strong generalization capability, efficient handling of stochastic renewable injections, and the ability to provide rapid, uncertainty-aware predictions. Beyond predictive accuracy, the proposed approach offers practical value as both a real-time advisory tool for system operators and a fast initializer for conventional solvers, thus supporting resilient and efficient grid operation in future power systems.