Research on optimal charging and discharging strategies for electric vehicles based on PSO algorithm

The rapid increase in electric vehicles (EVs) has significantly impacted power systems, necessitating the implementation of coordinated charging and discharging strategies to address challenges such as increased peak-to-valley load differences and reduced grid stability. In this study, a multi-objective optimization model for vehicle-to-grid (V2G) scenarios was developed by analyzing the load characteristics of EVs, with the dual objectives of minimizing charging costs and peak-to-valley load differences. The proposed model aims to reduce grid load variability and user charging costs. By employing a particle swarm optimization algorithm enhanced with dynamic adaptive inertia weight coefficients, the method effectively mitigates grid load fluctuations, reduces the peak-to-valley load ratio, and lowers charging costs. This approach achieves "peak shaving and valley filling," thereby enhancing grid stability and improving user economic benefits.