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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.
Author(s): Yang Q, Li J, Cao W, Li S, Lin J, Huo D, He H
Publication type: Article
Publication status: Published
Online publication date: 12/03/2020
Acceptance date: 11/03/2020
Date deposited: 24/03/2020
ISSN (print): 0360-5442
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