A novel fast-charging stations locational planning model for electric bus transit system. (1st June 2021)

• Record Type: Journal Article
• Authors: Wu, Xiaomei; Feng, Qijin; Bai, Chenchen; Lai, Chun Sing; Jia, Youwei; Lai, Loi Lei
• Is Part Of: Energy. Volume 224(2021)
• Publication Date: 2021-06-01
• Subjects: Fast-charging station -- Location planning -- Affinity propagation -- Binary particle swarm optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120106
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: With more electric buses, the optimal location of charging station plays an important role for bus electrification. This paper proposes a location planning model of electric bus fast-charging stations for the electric bus transit system, that takes the bus operation network and the distribution network into account. The model 1) simulates the operation network of electric buses thoroughly to obtain the charging demand of electric buses and 2) takes into account of the absorption capacity of distribution network and other constraints in the siting and capacity determination stage. The objective of the model is to minimize the sum of the construction cost, operation and maintenance costs, travel cost to charging stations, and the cost of power loss for charging stations at established bus terminus. The Affinity Propagation method is adopted to cluster the bus terminuses in order to obtain a preliminary number of charging stations. Subsequently, the Binary Particle Swarm Optimization algorithm is used to optimize the site selection and capacity. Finally, the model is applied to simulate and analyze the bus operation network of a coastal city in South China. The case study shows that the model can effectively optimize the layout of bus charging stations for the city. Highlights: Proposed a charging demand model for electric buses with bus operation schedule. Bus terminuses clustering with Affinity Propagation (AP) reduces charging stations. Binary Particle Swarm Optimization (BPSO) for discrete charging site selection. Examined various departure intervals, on-board battery quantity, and charger power. Proposed AP-BPSO identifies the lowest investment cost and reduces modelling time.