Data-Boosted Optimization for AC Optimal Power Flow: Interior-Point and Spatial Branching Methods

The AC Optimal Power Flow (AC-OPF) problem is a non-convex, NP-hard optimization task essential for secure and economic power system operation. While interior-point methods are widely used due to their computational efficiency, spatial branching techniques offer global optimality guarantees at significantly higher computational cost. In this work, we propose data-boosted variants of both approaches that leverage historical operating data to enhance performance. Specifically, data are used to guide initialization in interior-point methods and to restrict the search region in spatial branching. This unified perspective enables a systematic assessment of how learning can accelerate both local and global optimization strategies. We conduct an extensive empirical study across networks of varying sizes under both standard conditions and modified configurations designed to induce local optima. Our results show that data-boosted strategies consistently improve convergence and reduce computation times for both approaches. However, spatial branching remains computationally demanding even with data-driven enhancements, while interior-point methods exhibit remarkable robustness, often converging to globally optimal solutions, even in challenging instances with multiple local optima. These findings highlight the practical effectiveness of modern interior-point solvers and suggest that global optimization methods for AC-OPF still face significant scalability challenges, even when augmented with data-driven guidance.