Learning to Optimize meets Neural-ODE: Real-Time, Stability-Constrained AC OPF

TL;DR

This paper introduces DynOPF-Net, a novel machine learning model that combines learning to optimize with Neural ODEs to produce real-time, stability-aware solutions for AC optimal power flow problems, enhancing both accuracy and stability.

Contribution

It develops a stability-constrained AC OPF model integrating learning to optimize with Neural ODEs, addressing a key gap in real-time power system operations.

Findings

Achieves high-accuracy AC-OPF solutions on benchmark systems.

Ensures system stability, outperforming existing LtO methods.

Demonstrates practical applicability for real-time grid management.

Abstract

Recent developments in applying machine learning to address Alternating Current Optimal Power Flow (AC OPF) problems have demonstrated significant potential in providing close to optimal solutions for generator dispatch in near real-time. While these learning to optimize methods have demonstrated remarkable performance on steady-state operations, practical applications often demand compliance with dynamic constraints when used for fast-timescale optimization. This paper addresses this gap and develops a real-time stability-constrained OPF model (DynOPF-Net) that simultaneously addresses both optimality and dynamical stability within learning-assisted grid operations. The model is a unique integration of learning to optimize that learns a mapping from load conditions to OPF solutions, capturing the OPF's physical and engineering constraints, with Neural Ordinary Differential Equations, capturing generator dynamics, enabling the inclusion of a subset of stability constraints. Numerical results on the WSCC 9-bus and IEEE 57-bus benchmark systems demonstrate that DynOPF-Net can produce highly accurate AC-OPF solutions while also ensuring system stability, contrasting the unstable results obtained by state-of-the-art LtO methods.