Optimal Scheduling Strategies for EV Charging and Discharging in a Coupled Power–Transportation Network with V2G Scheduling and Dynamic Pricing

Yunzheng Ran, Honghua Liao (), Huijun Liang, Luoping Lu and Jianwei Zhong
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Yunzheng Ran: College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
Honghua Liao: College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
Huijun Liang: College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
Luoping Lu: College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China
Jianwei Zhong: College of Intelligent Systems Science and Engineering, Hubei Minzu University, Enshi 445000, China

Energies, 2024, vol. 17, issue 23, 1-17

Abstract: With the increasing penetration of electric vehicles (EVs), the spatial–temporal coupling between the transportation network (TN) and the power distribution network (PDN) has intensified greatly. Large-scale uncoordinated charging of EVs significantly impacts both the PDN and TN. In this paper, an optimal scheduling strategy for EV charging and discharging in a coupled power–transportation network (CPTN) with Vehicle-to-Grid (V2G) scheduling and dynamic pricing is proposed. The strategy considers the influence of dynamic transportation road network (DTRN) information on EV driving patterns, as well as the unique vehicle characteristics and mobile energy storage capabilities of EVs. Firstly, a DTRN model is established. Subsequently, the dynamic Dijkstra algorithm is utilized to accurately simulate the EV driving paths and predict the spatial–temporal distribution of the EV charging load. Secondly, optimal scheduling for EV charging and discharging within the CPTN is performed, guided by a V2G model coupled with a multi-time dynamic electricity price (MTDEP) strategy to optimize the grid load curve while accommodating the charging requirements of EVs. Finally, the effectiveness and superiority of the proposed optimization scheduling model are validated by the IEEE 33-node PDN test system.

Keywords: electric vehicle charging and discharging; power distribution network; V2G; transportation network; dynamic pricing; coupled power–transportation network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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