Physics-Data Fusion Enhanced Virtual Synchronous Generator Control Strategy for Multiple Charging Stations Active Frequency Response

Abstract

In regions where electric vehicles (EVs) are widely adopted and charging stations (CSs) are being built in large numbers, CSs are becoming a critical load-side resource for low-inertia power systems. In this paper, a physics-data fusion enhanced frequency control strategy for multiple CSs is proposed. Firstly, the power grid frequency control architecture is improved, where CSs as multi-agent (MA) can participate in frequency response (FR). Besides, a physics-driven adaptive inertia for CS virtual synchronous generators (VSGs) is proposed to improve system dynamic FR characteristics. Building upon this, the physics-data fusion concept is introduced, wherein the MA-soft-actor-critic (MA-SAC) algorithm dynamically adjusts coordination coefficients with the consideration of CSs’ FR capabilities. To validate the proposed strategy, comparative case studies are conducted on the IEEE 39-node system. The simulation results demonstrate that compared to a single physics-driven method, the proposed control strategy exhibits enhanced adaptability and improved FR characteristics across various scenarios. Under intact MA communication conditions, the proposed strategy reduces the frequency disturbance index to 49.872% and the CS response power oscillation index to 79.542%; Even with MA communication impairments, the strategy maintains significant improvements, reducing these indexes to 48.897% and 86.733% respectively.

Keywords

• multiple charging stations
• electric vehicle
• inertia control
• frequency regulation
• reinforcement learning
• soft actor-critic