Optimal Scheduling Method of Electric Vehicle Charging and Discharging Considering Peak Cutting and Valley Filling

In recent years, the domestic and international automotive industry has undergone rapid development, witnessing a continuous surge in the sales of electric vehicles (EVs). The substantial integration of EVs into the power grid poses formidable challenges, underscoring the necessity of research on optimized charging and discharging schedules for EVs. This imperative arises from the potential for extensive EV charging to induce peak loads on the grid, demanding strategies that effectively mitigate such load fluctuations. Additionally, the unpredictability of EV users' charging demand introduces complexity to scheduling models. This paper begins with a succinct overview of vehicle-to-grid (V2G) optimization scheduling research advancements. It proceeds to develop a model for optimizing EV charging and discharging schedules, considering peak shaving and valley filling. This model simulates EV load data using Monte Carlo methods based on probability density functions representing travel behavior. Subsequently, a scheduling strategy is formulated employing the improved barebones particle swarm optimization (IBBPSO) algorithm, and the parameters for IBBPSO are determined through orthogonal experiments. Finally, the superior performance of the IBBPSO algorithm in addressing this issue is demonstrated through a comparative analysis with BBPSO algorithm, PSO algorithm and GA algorithm, utilizing multifactor variance analysis.