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A unified configurational optimization framework for battery swapping and charging stations considering electric vehicle uncertainty

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  • Zhang, Mingze
  • Li, Weidong
  • Yu, Samson Shenglong
  • Wen, Kerui
  • Zhou, Chen
  • Shi, Peng

Abstract

Used batteries from electric vehicles (EVs) can be utilized as retired battery energy storage systems (RBESSs) at battery swapping and charging stations (BSCSs) to enhance their economic profitability and operational flexibility, by responding to the market incentive mechanism and interacting with EV batteries. In order to maximize the annual income of a BSCS, in this paper, we establish a double-stage coordinative decision-making (DCD) framework for the BSCS configuration, using the distributed robust optimization (DRO) approach for multi-timescale battery inventories. More specifically, in the DRO approach, the probability of each discrete EV battery swapping demand is carefully modeled to address the uncertainty in BSCS operations. The proposed DCD framework is able to enhance the flexibility of BSCS scheduling through systematically and optimally incorporating RBESSs; at the same time, it can also significantly improve regional load characteristics to accommodate the needs of the main electric grid. The effectiveness and superiority of the proposed DCD framework for BSCS is tested and verified through extensive simulation and comparison studies. The proposed integral optimization approach will be able to facilitate safe, reliable and economic operations of the next-generation power grid, whilst enhancing economics and utilization of retired EV batteries.

Suggested Citation

  • Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Zhou, Chen & Shi, Peng, 2021. "A unified configurational optimization framework for battery swapping and charging stations considering electric vehicle uncertainty," Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:energy:v:218:y:2021:i:c:s0360544220326438
    DOI: 10.1016/j.energy.2020.119536
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    2. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
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    4. Forero-Quintero, Jose-Fernando & Villafáfila-Robles, Roberto & Barja-Martinez, Sara & Munné-Collado, Ingrid & Olivella-Rosell, Pol & Montesinos-Miracle, Daniel, 2022. "Profitability analysis on demand-side flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    5. Wang, Ziqi & Hou, Sizu, 2023. "A real-time strategy for vehicle-to-station recommendation in battery swapping mode," Energy, Elsevier, vol. 272(C).
    6. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Wang, Shubin & Li, Jiabao & Liu, Xinni & Zhao, Erlong & Eghbalian, Nasrin, 2022. "Multi-level charging stations for electric vehicles by considering ancillary generating and storage units," Energy, Elsevier, vol. 247(C).
    8. Najafi, Arsalan & Jasiński, Michał & Leonowicz, Zbigniew, 2022. "A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem," Energy, Elsevier, vol. 249(C).

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