  This paper introduces a novel data-driven constraint screening approach aimed
at accelerating the solution of convexified AC optimal power flow (C-OPF) by
eliminating non-binding constraints. Our constraint screening process leverages
a novel mixture-of-experts architecture called MoGE (Mixture of Gradient
Experts), which is trained to predict optimal dual variables based on problem
parameters. The results demonstrate that subject to certain mild conditions on
the C-OPF model, our proposed method guarantees an identical solution to the
full problem but significantly reduces computational time. The solution's
accuracy is shown through C-OPF properties. Even with incorrectly screened
constraints, a recovery process is possible. This results in a problem with
fewer constraints than the original. Extensive simulations conducted on the
quadratic-convex model show that our method outperforms other constraint
screening techniques.
