Preventive Control of Power System With Analytic Data-driven Stability Constraints

Abstract

Effective and efficient preventive control methods are important to ensure the safe and stable operation of power grid. Preventive control problem is generally formulated as the stability-constrained optimal power flow model. It is necessary to satisfy multiple stability constraints at the same time in practical use. In this paper, the nonparametric regression method was selected to construct various data-driven models between different stability indexes and system operation modes respectively. Based on the analytical representation method of data-driven stability constraint, this paper proposed the definition and selection method of joint dominant samples, and constructed a preventive control model with multiple analytic data-driven stability constraints. The preventive control problem was thus formulated as a mixed integer programming problem, which could be solved directly. Numerical examples show that the proposed method can quickly obtain effective preventive control strategies in various scenarios such as different stability problems and different scales of power systems.