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Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research

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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)

  • Pengcheng Liang

    (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

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles ( EV ) combined with wind and photovoltaic power based on the Frank-Copula- GlueCVaR . First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula- GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind-photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EV s was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth–Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal strategy of the EV group through continuous learning. Finally, case study results are shown to effectively absorb more renewable energy, reduce the consumption cost of the EV group, and suppress the load fluctuation of the whole EV group, which has a practical significance and theoretical value.

Suggested Citation

  • Jianwei Gao & Yu Yang & Fangjie Gao & Pengcheng Liang, 2021. "Optimization of Electric Vehicles Based on Frank-Copula- GlueCVaR Combined Wind and Photovoltaic Output Scheduling Research," Energies, MDPI, vol. 14(19), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6080-:d:642013
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    References listed on IDEAS

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    Cited by:

    1. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    2. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.

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