Alternative Learning Architecture for Solving AC-OPF via Supervised Relaxation and Cross Encoder

As power systems evolve, efficient AC optimal power flow solutions are increasingly critical, yet traditional methods face challenges in speed and scalability. This paper introduces ExpressOPF, an ML-based framework that alternates between the original AC-OPF problem and its relaxed form. By integrating Lagrange multipliers into a compact neural network, ExpressOPF enforces constraints while improving accuracy and inference speed. Tested on 162-, 300-, 1354-, and the real-world 4492-bus system operated by Korea Power Exchange (KPX), ExpressOPF achieves under 1\% cost deviation from MATPOWER, 100,000$\times$ speedup, 75\% model compression, and 40\% lower GPU memory use—while maintaining over 99\% constraint satisfaction. These results highlight its potential for real-time, resource-efficient AC-OPF at scale, with future work aimed at grid reliability and multi-area operations.