Modeling Small-Signal Stability Constraints in Optimal Power Flow Using a Bayesian Optimization Framework

This paper presents a Bayesian optimization-based framework for incorporating small-signal stability constraints into the optimal power flow (OPF) problem. First, a mathematical optimization problem is formulated to identify the critical regions of the small-signal stability boundary. The Bayesian optimization framework is then employed to efficiently locate these critical regions, reducing the need for extensive simulations. In the identified critical region, a surrogate model for the small-signal stability constraint is constructed using support vector machines (SVM), enabling an analytical representation of the constraint. Finally, the OPF is solved with the embedded surrogate model, achieving an economically optimal dispatch that satisfies small-signal stability requirements. Case studies on the modified IEEE-39 bus system validate the effectiveness and computational efficiency of the proposed method.