Stability-Constrained AC Optimal Power Flow--A Gaussian Process-Based Approach

TL;DR

This paper introduces a Gaussian Process-based method to incorporate generator dynamic stability into AC Optimal Power Flow, enabling more reliable and robust power system operation decisions.

Contribution

It presents a novel data-driven framework that models generator stability as a probabilistic function within ACOPF, integrating dynamic stability assessment directly into the optimization.

Findings

The method effectively captures generator dynamics with limited training data.

Numerical experiments show improved stability and robustness over existing approaches.

The approach is validated on IEEE benchmark systems with promising results.

Abstract

The Alternating Current Optimal Power Flow (ACOPF) problem is a core task in power system operations, aimed at determining cost-effective generation dispatch while satisfying physical and operational constraints. However, conventional ACOPF formulations rely on steady-state models and neglect generator dynamics, which can result in operating points that are economically optimal but dynamically unstable. This paper proposes a novel, data-driven approach to incorporate generator dynamics into the ACOPF using Gaussian Process (GP) models. Specifically, it introduces an exponential surrogate function to characterize the stability of solutions to the differential equations governing synchronous generator dynamics. The exponent, which indicates whether system trajectories decay (stable) or grow (unstable), is learned as a function of the bus voltage using GP regression. Crucially, the framework enables probabilistic stability assessment to be integrated directly into the optimization process. The resulting dynamics-aware ACOPF formulation identifies operating points that satisfy both operational safety and dynamic stability criteria. Numerical experiments on the IEEE 39-bus, 57-bus, and 118-bus systems demonstrate that, compared with existing data-driven approaches, the proposed method efficiently captures generator dynamics with limited training data, yielding more reliable and robust decisions across a wide range of operating conditions.