Hybrid Data-Driven Modeling for an AC/DC Power System Considering Renewable Energy Uncertainty

Abstract

The development of grid-connected renewable energy sources and the widespread use of power electronic devices have exacerbated the uncertain, time-varying, and non-linear characteristics of power systems, making accurate and real-time model design challenging. Modeling for unmodeled dynamics and random characteristics has inherent disadvantages in power system simulation. Conventional converter valve modeling ignores the high-frequency switching condition. This study aims to provide an effective modeling strategy that can accurately characterize the unmodeled dynamics and uncertainty of AC/DC hybrid interconnection systems with significant grid-connected renewable energy capacity. The model-data hybrid-driven modeling concept based on digital twin (DT) enhances the technique’s effectiveness. It models the proportional-integral control link of a voltage source converter (VSC). The time convolution neural network (TCN) algorithm can describe accurately the high-frequency switching state of the switching device and the operation state of renewable energy units that changes dynamically with weather conditions and other variables. The simulation experiments on a real-world power grid demonstrate the proposed modeling method’s efficiency and the hybrid-driven model’s performance.