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A Dual-Layer MPC of Coordinated Control of Battery Load Demand and Grid-Side Supply Matching at Electric Vehicle Swapping Stations

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  • Minan Tang

    (College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Chenchen Zhang

    (College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Yaqi Zhang

    (College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Yaguang Yan

    (College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Wenjuan Wang

    (College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Bo An

    (College of Electrical and Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

The uncontrolled charging of electric vehicles may cause damage to the electrical system as the number of electric vehicles continues to rise. This paper aims to construct a new model of the power system and investigates the rational regulation and efficient control of electric vehicle battery charging at electric vehicle exchange battery stations in response to the real-time grid-side supply situation. Firstly, a multi-objective optimization strategy is established to meet the day-ahead forecasted swap demand and grid-side supply with the maximization of day-ahead electric vehicle battery swapping station (BSS) revenue in the core. Secondly, considering the variable tariff strategy, a two-layer Model Predictive Control (MPC) coordinated control system under real-time conditions is constructed with the objective function of maximizing the revenue of BSS and smoothing the load fluctuation of the power system. Then, the day-ahead optimization results are adopted as the reference value for in-day rolling optimization, and the reference value for in-day optimization is dynamically adjusted according to the real-time number of electric car changes and power system demand. Finally, verified by experimental simulation, the results show that the day-ahead-intraday optimization model can increase the economic benefits of BSS and reduce the pressure on the grid to a certain extent, and it can ensure the fast, accurate, and reasonable allocation of batteries in BSS, and realize the flexible, efficient, and reasonable distribution of batteries in BSS.

Suggested Citation

  • Minan Tang & Chenchen Zhang & Yaqi Zhang & Yaguang Yan & Wenjuan Wang & Bo An, 2024. "A Dual-Layer MPC of Coordinated Control of Battery Load Demand and Grid-Side Supply Matching at Electric Vehicle Swapping Stations," Energies, MDPI, vol. 17(4), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:879-:d:1338677
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    References listed on IDEAS

    as
    1. Zhang, Yudi & Wang, Xiaojun & Zhi, Bangdong, 2024. "Strategic investment in electric vehicle charging service: Fast charging or battery swapping," International Journal of Production Economics, Elsevier, vol. 268(C).
    2. Wu, Chuantao & Lin, Xiangning & Sui, Quan & Wang, Zhixun & Feng, Zhongnan & Li, Zhengtian, 2021. "Two-stage self-scheduling of battery swapping station in day-ahead energy and frequency regulation markets," Applied Energy, Elsevier, vol. 283(C).
    3. Tan, Yang & Fukuda, Hiroatsu & Li, Zhang & Wang, Shuai & Gao, Weijun & Liu, Zhonghui, 2022. "Does the public support the construction of battery swapping station for battery electric vehicles? - Data from Hangzhou, China," Energy Policy, Elsevier, vol. 163(C).
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    5. Paolo Scarabaggio & Raffaele Carli & Graziana Cavone & Mariagrazia Dotoli, 2020. "Smart Control Strategies for Primary Frequency Regulation through Electric Vehicles: A Battery Degradation Perspective," Energies, MDPI, vol. 13(17), pages 1-19, September.
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