Journal
ENERGY
Volume 224, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.120106
Keywords
Fast-charging station; Location planning; Binary particle swarm optimization; Affinity propagation
Categories
Funding
- China Southern Power Grid: Research on deployment of ebuses charging stations in Yangjiang City considering transportation network [GDKJ031700KK52180001]
- Department of Finance and Education of Guangdong Province [2016 [202]]
- Key Discipline Construction Program, China
- National Natural Science Foundation of China [72071100]
- Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [2016KCXTD022]
- Brunel University London BRIEF Funding, UK
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This paper presents a location planning model for electric bus fast-charging stations, considering the bus operation network and distribution network, to optimize the layout and reduce operational costs for bus charging stations. The model effectively minimizes construction, operation, maintenance, and power loss costs while improving the charging infrastructure for electric buses in a coastal city in South China.
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. (c) 2021 Elsevier Ltd. All rights reserved.
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