An improved vehicle to the grid method with battery longevity management in a microgrid application

Qingqing Yang, Jianwei Li, Wanke Cao, Shuangqi Li, Jie Lin, Da Huo and Hongwen He

Energy, 2020, vol. 198, issue C

Abstract: This paper proposed an improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service. The proposed methodology is improved in two ways. Firstly, to give a prediction of the available electric vehicle (EV) battery capacity in the control time-step for the V2G service, a deep learning based prediction is developed. Secondly, this study advances the previous V2G method by adding the quantitative analysis of the battery cycle life into the V2G optimization. The accurate prediction of the schedulable battery capacity based on the LSTM algorithm is shown very effective in the power system frequency control. Also, compared with the previous method that without battery lifetime control, the proposed method benefits in the reduction of charge/discharge cycles.

Keywords: Electric vehicles; Deep learning; Frequency control; Microgrid; Vehicle to the grid (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:198:y:2020:i:c:s0360544220304813

DOI: 10.1016/j.energy.2020.117374

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