Collaborative optimization of vehicle and charging scheduling for mixed bus systems considering charging load balance

Abstract

With the widespread development of electric buses, the impact of charging scheduling on peak grid loads and fluctuations has become increasingly significant. Existing studies primarily focus on dynamically adjustable charging power to mitigate charging load peak or fluctuations. However, these strategies gradually adjust the charging power too frequently and are based on predetermined vehicle scheduling, resulting in poor applicability. To address this issue, a segmented adjustable charging power strategy (SACP) in charging scheduling is proposed that simultaneously considers reducing the fluctuations and the peak of the charging load. Meanwhile, this study proposes a collaborative optimization model for both vehicle and charging scheduling of a mixed bus system that comprising electric human-driven buses and electric autonomous modular buses. The objective is to minimize peak loads and fluctuations on the grid, while also reducing operating costs for bus enterprises. An improved NSGA-II algorithm is developed to solve the collaborative optimization model, incorporating an objective-oriented strategy in the initial solution to enhance search efficiency and solution quality. Case studies demonstrate that the SACP strategy significantly reduces peak grid loads and fluctuation costs compared with a fixed charging power scenario, thereby achieving balanced charging loads. Furthermore, compared to the charging scheduling strategy alone, the SACP strategy exhibits a significant reduction in fluctuation cost of charging load by 50% and the peak cost of charging load by 21.6%, thereby ensuring the stability of charging load for both the system and charging events.