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Robust model of electric vehicle charging station location considering renewable energy and storage equipment

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  • Li, Chengzhe
  • Zhang, Libo
  • Ou, Zihan
  • Wang, Qunwei
  • Zhou, Dequn
  • Ma, Jiayu

Abstract

The location of electric vehicle charging station (EVCS) is one of the critical problems that restricts the popularization of electric vehicle (EV), and the combination of EVCS and distributed renewable energy can stabilize the fluctuation of renewable energy output. This article takes a micro-grid composed of the power distribution such as wind power and photovoltaic (PV), EVCSs and energy storage systems (ESS) as the research object. The uncertainties of EVs’ charging demand and distributed renewable energy output are considered. A robust optimization model for the location of charging stations with distributed energy is proposed based on the combination of the road network and the grid. Load fluctuation rate is used to evaluate the degree of fit between the renewable energy uncertain output curve and the charging demand curve to determine the appropriate capacity of the wind and PV generation system and ESS. The method of kernel density estimation is used to improve the issue of over-conservatism of the robust optimization. Finally, a simulation is carried out on a network which consists of an IEEE 33-node power distribution network and a 25-node transportation system. The robustness and economy of the model are demonstrated by the results of simulation given.

Suggested Citation

  • Li, Chengzhe & Zhang, Libo & Ou, Zihan & Wang, Qunwei & Zhou, Dequn & Ma, Jiayu, 2022. "Robust model of electric vehicle charging station location considering renewable energy and storage equipment," Energy, Elsevier, vol. 238(PA).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019617
    DOI: 10.1016/j.energy.2021.121713
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    6. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    7. Lai, Chun Sing & Chen, Dashen & Zhang, Jinning & Zhang, Xin & Xu, Xu & Taylor, Gareth A. & Lai, Loi Lei, 2022. "Profit maximization for large-scale energy storage systems to enable fast EV charging infrastructure in distribution networks," Energy, Elsevier, vol. 259(C).
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    14. Shi, Lei & Wu, Rongxin & Lin, Boqiang, 2023. "Where will go for electric vehicles in China after the government subsidy incentives are abolished? A controversial consumer perspective," Energy, Elsevier, vol. 262(PA).
    15. Zhao, Zhonghao & Lee, Carman K.M. & Yan, Xiaoyuan & Wang, Haonan, 2024. "Reinforcement learning for electric vehicle charging scheduling: A systematic review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 190(C).
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    17. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).

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