Cooperative Optimization of Bus Service and Charging Schedules for a Fast-Charging Battery Electric Bus Network

Owing to the development of fast-charging technologies, the problems of limited driving range and long charging time for battery electric buses (BEBs) can be alleviated. However, fast charging may lead to a high energy cost because of charging during periods of high electricity rates and a high-power peak and consequently enhanced electricity demand cost. The reason is that the bus service and charging schedules can both affect the charging behavior and there is strong interaction between them. However, there is lack of researches to consider these two aspects jointly to optimise the charging behavior of large-scale BEB network. In this study, a BEB network charging optimisation (BEB-NCO) model is proposed to cooperatively optimise the bus service and charging schedules to minimise the charging cost for a fast-charging BEB network. To enhance the computing efficiency for large-scale networks, a heuristic algorithm, an integration of adaptive large neighbourhood searching and branch & bound (ALNS-BB), has been developed. The developed model and the heuristic algorithm were applied to a real-world BEB network in Shenzhen, China. The results show that the ALNS-BB) algorithm can reduce the computational time by at least 82.56% and the bus service and charging schedule generated by BEB-NCO model can reduce the charging cost by up to 53.35% compared with the existing strategy. Furthermore, sensitivity analyses were conducted to investigate the impact of the charging power on the charging cost. It is suggested that a charging power of 115 kW is recommended in this case.