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Smart Carbon Emission Scheduling for Electric Vehicles via Reinforcement Learning under Carbon Peak Target

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  • Yongsheng Cao

    (Department of Intelligent Science and Information Law, East China University of Political Science and Law, Shanghai 200042, China
    School of Electronic Information and Electrical Engineering, China Institute for Smart Court, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yongquan Wang

    (Department of Intelligent Science and Information Law, East China University of Political Science and Law, Shanghai 200042, China)

Abstract

Electric vehicles (EVs) have become popular in daily life, which influences carbon dioxide emissions and reshapes the curves of community loads. It is crucial to study efficient carbon emission scheduling algorithms to lessen the influence of EVs’ charging demand on carbon dioxide emissions and reduce the carbon emission cost for EVs coming to the community. We study an electric vehicle (EV) carbon emission scheduling problem to shave the peak community load and reduce the carbon emission cost when we do not know future EV data. First, we investigate an offline carbon emission scheduling problem to minimize the carbon emission cost of the community by predicting future data with regard to incoming EVs. Then, we study the online carbon emission problem and propose an online carbon emission algorithm based on a heuristic rolling algorithm. Furthermore, we propose a reinforcement learning smart carbon emission algorithm (RLSCA) to achieve the dispatching plan of the charging carbon emission of EVs. Last but not least, simulation results show that our proposed algorithm can reduce the carbon emission cost by 21.26 % , 16.60 % , and 8.72 % compared with three benchmark algorithms.

Suggested Citation

  • Yongsheng Cao & Yongquan Wang, 2022. "Smart Carbon Emission Scheduling for Electric Vehicles via Reinforcement Learning under Carbon Peak Target," Sustainability, MDPI, vol. 14(19), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12608-:d:933090
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    References listed on IDEAS

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    1. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    2. Luo, Lizi & Gu, Wei & Zhou, Suyang & Huang, He & Gao, Song & Han, Jun & Wu, Zhi & Dou, Xiaobo, 2018. "Optimal planning of electric vehicle charging stations comprising multi-types of charging facilities," Applied Energy, Elsevier, vol. 226(C), pages 1087-1099.
    3. Jieshuang Dong & Yiming Li & Wenxiang Li & Songze Liu, 2022. "CO 2 Emission Reduction Potential of Road Transport to Achieve Carbon Neutrality in China," Sustainability, MDPI, vol. 14(9), pages 1-24, May.
    4. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
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    Cited by:

    1. Junchi Ma & Yuan Zhang & Zongtao Duan & Lei Tang, 2023. "PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging," Sustainability, MDPI, vol. 15(18), pages 1-22, September.

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