A bi‐level optimization model for operating shared electric bus charging stations with dual‐user queuing systems

Abstract This article investigates how a bus firm can best operate its electric bus (EB) charging stations to improve efficiency while serving dual users: EBs and private electric vehicles (PEVs). We model each station as a stochastic service system and formulate a bi‐level optimization model to capture the interactions between the bus firm and the PEVs. A multiple‐population genetic algorithm is proposed to solve the model. We find that: (a) our sharing strategy can promote station efficiency and ease PEV users’ charging anxiety, achieving over 80% utilization of charging resources while ensuring PEV users wait no longer than 0.5 h; (b) reducing the maximum EB service time increases the number of open stations but raises costs and decreases charging stations’ profit. Reducing the maximum waiting time for PEVs alleviates the congestion of stations at the expense of revenue. (c) Compared to a benchmark model where a strategy of fixed opening hours and number of shared chargers is used, our model's dynamic optimization of opening hours and charger allocation per time period improves operational efficiency (especially when the transition cost is not high). The proposed algorithm performs better than the traditional genetic algorithm in 50 experiments. Our proposed methods can provide decision support for the operation of shared EB charging stations and promote the development of a green and low‐carbon transportation system.