Control parameter identification method for virtual synchronous generator considering electric vehicles based on dynamic particle swarm optimization

To address the issues of insufficient control parameter identification accuracy and convergence speed during the grid connection of distributed power sources, a control parameter identification method for the Virtual Synchronous Generator (VSG) converter model considering the integration of electric vehicles (EVs) based on the dynamic particle swarm optimization algorithm is proposed. By constructing a VSG inverter control model suitable for distributed power sources and EV charging systems, analyzing the interactions between active and reactive power control loops under EV integration scenarios, selecting parameters and observations to be identified, and improving the Particle Swarm Optimization (PSO) algorithm based on actual conditions, the method ensures enhanced system adaptability. Simulation results demonstrate that the proposed method exhibits higher dynamic response capabilities, system stability, and adaptability under varying load conditions and uncertainties introduced by EV charging behaviors, highlighting its significant engineering application value.