Hybrid model-data-driven dynamic VAR planning for wind-penetrated power system using spectral surrogate techniques

Abstract

The increasing wind power penetration and proliferation of induction motor loads, of which dynamic impact cannot be revealed through steady-state analysis, bring challenges to the short-term voltage stability of modern power systems. This study proposes a hybrid model-data-driven approach for dynamic VAR source planning to enhance the short-term voltage stability of wind-penetrated power systems to reduce the computation burden of electro-mechanical transient models. Firstly, the theoretical background of Stochastic Spectral Embedding (SSE) is introduced. Then, a surrogate model for the electromechanical transient model is established using SSE following efficient expansion coefficient calculation and a designed partition strategy for the studied problem. Furthermore, a hybrid model-data-driven VAR deployment optimization model is established with 3 objectives, which is solved by a dual-population-based evolutionary algorithm (DPEA). The accuracy and effectiveness of the model are verified on a modified New England 39-bus system. Simulation results prove that the computational cost is reduced significantly in comparison with conventional model-based method without a compromise in accuracy and the proposed method is also more accurate than methods based on other surrogate models. The proposed SSE-based model can be applied to other power system analysis with electro-mechanical transient models to alleviate the computational cost.