Towards Real-Time Machine Learning Approximations of Ac Optimal Power Flow

Optimal Power Flow (OPF) is central to the efficient and secure operation of electric power systems. The Alternating Current OPF (ACOPF) variant accounts for nonlinear Kirchhoff laws and is known to be both non-convex and, in many instances, NP-hard. Despite longstanding research, achieving fast, accurate, and robust ACOPF solutions at scale remains challenging-especially under evolving grid conditions such as high renewables. Recent progress in machine learning (ML) offers a promising paradigm for approximating solutions with near-real-time speed after offline training. In this paper, we propose a detailed methodological framework spanning data extraction, neural network training, and model comparisons (Gaussian Mixture Models and Linear Regression). We integrate these ML pipelines with classical OPF theory, referencing comprehensive overviews as well as novel ML-based OPF strategies. Experimental results on five standard IEEE power system networks demonstrate that neural networks are generally competitive or superior to simpler statistical methods in complex cases, while Linear Regression can be surprisingly strong for smaller networks. We further analyze feasibility issues, constraint enforcement, and out-of-distribution conditions. Our findings highlight both the promise of data-driven approaches and the nuances of ensuring reliable, real-time dispatch solutions.