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A New Method to Plan the Capacity and Location of Battery Swapping Station for Electric Vehicle Considering Demand Side Management

Author

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  • Wenxia Liu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Shuya Niu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Huiting Xu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Xiaoying Li

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

Compared to electric vehicle (EV) charging mode, battery swapping mode can realize concentrated and orderly charging. Therefore battery swapping stations (BSS) can participate in the demand side management (DSM) as an integrated form. In this context, a new method to plan the capacity and location of BSS for EV, considering DSM, is proposed in this paper. Firstly, based on the original charging power of BSS with the rule of “First-In First-Out”, a bi-level optimal configuration model of BSS, in which net profit of BSS is maximized in the upper model and operating cost of Distribution Company is minimized in the lower model, is developed to decide the rated power, number of batteries, contract pricing and dispatched power of BSS for DSM. Then, the optimal locating model of BSS with the objective of minimizing network loss is built. A mesh adaptive direct search algorithm with YALMIP toolbox is applied to optimize the bi-level model. Simulation calculation was carried on IEEE-33 nodes distribution system and the results show that participating in DSM can improve the economic benefits of both BSS and distribution network and promote the consumption of distributed generation, verifying the feasibility and effectiveness of the proposed method.

Suggested Citation

  • Wenxia Liu & Shuya Niu & Huiting Xu & Xiaoying Li, 2016. "A New Method to Plan the Capacity and Location of Battery Swapping Station for Electric Vehicle Considering Demand Side Management," Sustainability, MDPI, vol. 8(6), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:6:p:557-:d:72015
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    References listed on IDEAS

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    1. Yang, Shengjie & Yao, Jiangang & Kang, Tong & Zhu, Xiangqian, 2014. "Dynamic operation model of the battery swapping station for EV (electric vehicle) in electricity market," Energy, Elsevier, vol. 65(C), pages 544-549.
    2. Liu, Nian & Chen, Zheng & Liu, Jie & Tang, Xiao & Xiao, Xiangning & Zhang, Jianhua, 2014. "Multi-objective optimization for component capacity of the photovoltaic-based battery switch stations: Towards benefits of economy and environment," Energy, Elsevier, vol. 64(C), pages 779-792.
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    Cited by:

    1. Suganya, S. & Raja, S. Charles & Venkatesh, P., 2017. "Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified Particle Swarm Optimization approach," Energy, Elsevier, vol. 138(C), pages 92-102.
    2. Quddus, Md Abdul & Kabli, Mohannad & Marufuzzaman, Mohammad, 2019. "Modeling electric vehicle charging station expansion with an integration of renewable energy and Vehicle-to-Grid sources," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 251-279.
    3. Yang, Xiong & Peng, Zhenhan & Wang, Pinxi & Zhuge, Chengxiang, 2023. "Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach," Energy, Elsevier, vol. 280(C).
    4. Zhan, Weipeng & Wang, Zhenpo & Zhang, Lei & Liu, Peng & Cui, Dingsong & Dorrell, David G., 2022. "A review of siting, sizing, optimal scheduling, and cost-benefit analysis for battery swapping stations," Energy, Elsevier, vol. 258(C).
    5. Stojiljković, Mirko M., 2017. "Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics," Energy, Elsevier, vol. 137(C), pages 1231-1251.
    6. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    7. Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
    8. Liang, Yanni & Cai, Hua & Zou, Guilin, 2021. "Configuration and system operation for battery swapping stations in Beijing," Energy, Elsevier, vol. 214(C).
    9. Changhong Deng & Ning Liang & Jin Tan & Gongchen Wang, 2016. "Multi-Objective Scheduling of Electric Vehicles in Smart Distribution Network," Sustainability, MDPI, vol. 8(12), pages 1-15, November.
    10. Quddus, Md Abdul & Shahvari, Omid & Marufuzzaman, Mohammad & Usher, John M. & Jaradat, Raed, 2018. "A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid," Applied Energy, Elsevier, vol. 229(C), pages 841-857.
    11. Weimin Ma & Jiakai Chen & Hua Ke, 2021. "Electric Vehicle Assignment Considering Users’ Waiting Time," Sustainability, MDPI, vol. 13(23), pages 1-14, December.

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