Empirical analysis of electric vehicle charging load forecasting based on Monte Carlo simulation model

With the rapid proliferation of electric vehicles, their charging loads pose new challenges to power grid stability and operational efficiency. To address this, this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province, leveraging multi-source probabilistic distribution data under typical operational scenarios. The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods, with significant load fluctuations exerting substantial pressure on the grid. In response, this paper proposes strategic interventions including optimized charging infrastructure planning, time-of-use electricity pricing mechanisms, and smart charging technologies to balance grid loads. The results provide a theoretical foundation for electric vehicle load forecasting, smart grid dispatching, and vehicle-grid integration, thereby enhancing grid operational efficiency and sustainability.