Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training
This work proposes a neural network framework to solve AC Optimal Power Flow (AC-OPF) problems by training models to explicitly minimize worst-case constraint violations during learning. By incorporating formal verification techniques like $\alpha$-CROWN, the method produces accurate and provably safer approximations of large power systems, significantly reducing constraint breaches compared to traditional solvers.