AI Model Optimizes Power Flow for Renewable Energy Grids

The electricity sector faces increasing complexity with the integration of renewable energy, requiring thousands of daily AC Optimal Power Flow (OPF) calculations to determine minimum-cost generation. While traditional methods provide point predictions, operators increasingly need a distribution of feasible near-optimal solutions for risk assessment and trade-off analysis. Existing generative models struggle to provide diverse, high-quality solutions and scale to larger systems. This research introduces FMOPF, a novel framework that addresses these issues by separating the high-dimensional OPF solution manifold compression from the conditional mapping of loads to that manifold. It achieves this through latent flow matching and a Constraint-Aware Interaction Prior Network. Experiments on various IEEE test systems demonstrate that FMOPF significantly improves warm starts for Newton-Raphson solvers, reduces tail risk among generative methods, and is the first to scale effectively to systems with hundreds of buses while maintaining full physical feasibility. This advancement is critical for managing modern power grids with deep renewable penetration.