Stochastic Optimal Scheduling Analysis of Electric Vehicle Access to Microgrid Considering Multiple Uncertainties

Abstract

The integration of new energy generation equipment into the microgrid can effectively reduce energy consumption and operational costs. However, due to the inherent spatial and temporal variability of sources such as wind and photovoltaic (PV) power, their integration poses challenges for grid stability. To address these issues, this paper proposes a stochastic optimal scheduling model for microgrids incorporating electric vehicle participation and multiple sources of uncertainty. Historical data on loads, wind power, and PV generation are first collected and modeled to capture their spatiotemporal uncertainties. Electric vehicles (EVs) are then integrated into the grid as bidirectional energy carriers, capable of both charging and discharging, thereby alleviating pressure on conventional energy storage systems. Since the charging and discharging processes contribute to battery degradation, participating EV users are provided with subsidies to compensate for associated costs. This mechanism reduces both the operational costs of the microgrid and the charging expenses for EV users, while enhancing power system stability. Simulation results demonstrate that, compared to the conventional distribution network operating independently, the proposed model significantly improves the overall benefits for both the microgrid and its users. Additionally, it achieves cost and carbon emission reductions, enhances system stability and economic performance, and offers a novel approach for optimal microgrid scheduling.