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Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm

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  • Jianwei Gao

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Yu Yang

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Fangjie Gao

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Haoyu Wu

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

Abstract

With the implementation of the carbon neutral policy, the number of electric vehicles (EVs) is increasing. Thus, it is urgently needed to manage the charging and discharging behavior of EVs scientifically. In this paper, EVs are regarded as agents, and a multiagent cooperative optimization scheduling model based on Roth–Erev (RE) algorithm is proposed. The charging and discharging behaviors of EVs will influence each other. The charging and discharging strategy of one EV owner will affect the choice of others. Therefore, the RE algorithm is selected to obtain the optimal charging and discharging strategy of the EV group, with the utility function of the prospect theory proposed to describe EV owners’ different risk preferences. The utility function of the prospect theory has superior effectiveness in describing consumers’ utility. Finally, in the case of residential electricity, the effectiveness of the proposed method is verified. Compared with that of random charging, this method reduces the total EV group cost of EVs by 52.4%, with the load variance reduced by 26.4%.

Suggested Citation

  • Jianwei Gao & Yu Yang & Fangjie Gao & Haoyu Wu, 2021. "Collaborative Optimization of Electric Vehicles Based on MultiAgent Variant Roth–Erev Algorithm," Energies, MDPI, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:125-:d:710722
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    References listed on IDEAS

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    1. Kempton, Willett & Tomic, Jasna & Letendre, Steven & Brooks, Alec & Lipman, Timothy, 2001. "Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as Resources for Distributed Electric Power in California," Institute of Transportation Studies, Working Paper Series qt0qp6s4mb, Institute of Transportation Studies, UC Davis.
    2. Kempton, Willett & Tomic, Jasna & Letendre, Steven & Brooks, Alec & Lipman, Timothy, 2001. "Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as Resources for Distributed Electric Power in California," Institute of Transportation Studies, Working Paper Series qt5cc9g0jp, Institute of Transportation Studies, UC Davis.
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