فهرست نشریات علمی دسترسی آزاد ایران

This paper addresses the critical challenge of managing peak load growth, which places significant financial burdens on governments due to the need for new power plants or upgrades to existing infrastructure. Flattening the demand curve through effective peak shaving and valley filling presents an opportunity to reduce these costs, yet traditional nonlinear optimization models struggle with the complexities introduced by the increasing prevalence of electric vehicles. To overcome these challenges, we propose a linearized approach based on a piecewise linear approximation technique, enhancing computational efficiency while maintaining accuracy. Additionally, we address the inherent uncertainty associated with electric vehicle arrival and departure times using Hong’s two-point estimation method. A microgrid case study utilizing real-world data collected at ten-minute intervals demonstrates the effectiveness of our approach, achieving a notable reduction in peak demand of 25.3% and decreasing computation time by 80% compared to conventional nonlinear models. Furthermore, sensitivity analysis conducted on parking availability, initial energy levels of the vehicles, and energy requirements for subsequent trips indicates the robustness and efficiency of the proposed framework. The findings suggest that this method not only optimizes electric vehicle charging management but also supports the integration of electric vehicles into the power grid, paving the way for sustainable energy management practices.