Hierarchical Optimal Dispatching of Electric Vehicles Based on Photovoltaic-Storage Charging Stations

Ziyuan Liu (), Junjing Tan, Wei Guo, Chong Fan (), Wenhe Peng, Zhijian Fang and Jingke Gao
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Ziyuan Liu: School of Innovation and Entrepreneurship, Wuhan Institute of Technology, No.206, Guanggu 1st Road, Wuhan 430205, China
Junjing Tan: School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, No.206, Guanggu 1st Road, Wuhan 430205, China
Wei Guo: School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, No.206, Guanggu 1st Road, Wuhan 430205, China
Chong Fan: Jinguan Electric Co., Ltd., Neixiang County Industrial Park, Nanyang 474350, China
Wenhe Peng: School of Economics and Management, China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, China
Zhijian Fang: School of Automation, China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, China
Jingke Gao: Institute of Advanced Study, China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, China

Mathematics, 2024, vol. 12, issue 21, 1-13

Abstract: Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

Keywords: optimal dispatching; microgrid; photovoltaic-storage charging station; dynamic electricity pricing model; adaptive particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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