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Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization

Author

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  • Lingling Hu

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Junming Zhou

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Feng Jiang

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China
    Guangxi Huihuang Langjie Environmental Protection Technology Co., Ltd., Beihai 536000, China
    Institute for Artificial Intelligence, Peking University, Beijing 100871, China)

  • Guangming Xie

    (Institute for Artificial Intelligence, Peking University, Beijing 100871, China)

  • Jie Hu

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

  • Qinglie Mo

    (School of Mechanical and Automotive, Guangxi University of Science and Technology, Liuzhou 545006, China)

Abstract

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.

Suggested Citation

  • Lingling Hu & Junming Zhou & Feng Jiang & Guangming Xie & Jie Hu & Qinglie Mo, 2023. "Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization," Sustainability, MDPI, vol. 16(1), pages 1-25, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:57-:d:1303916
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    References listed on IDEAS

    as
    1. Surender Reddy Salkuti, 2023. "Advanced Technologies for Energy Storage and Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-7, February.
    2. Welzel, Fynn & Klinck, Carl-Friedrich & Pohlmann, Yannick & Bednarczyk, Mats, 2021. "Grid and user-optimized planning of charging processes of an electric vehicle fleet using a quantitative optimization model," Applied Energy, Elsevier, vol. 290(C).
    3. Yang, Zhichun & Yang, Fan & Min, Huaidong & Tian, Hao & Hu, Wei & Liu, Jian & Eghbalian, Nasrin, 2023. "Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources," Energy, Elsevier, vol. 263(PA).
    4. Mortaz, Ebrahim & Vinel, Alexander & Dvorkin, Yury, 2019. "An optimization model for siting and sizing of vehicle-to-grid facilities in a microgrid," Applied Energy, Elsevier, vol. 242(C), pages 1649-1660.
    5. Dusan Medved & Lubomir Bena & Maksym Oliinyk & Jaroslav Dzmura & Damian Mazur & David Martinko, 2023. "Assessing the Effects of Smart Parking Infrastructure on the Electrical Power System," Energies, MDPI, vol. 16(14), pages 1-16, July.
    6. Ziad M. Ali & Martin Calasan & Shady H. E. Abdel Aleem & Francisco Jurado & Foad H. Gandoman, 2023. "Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review," Energies, MDPI, vol. 16(16), pages 1-41, August.
    7. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    8. Kris Scicluna & Brian Azzopardi & Kurt Spiteri, 2023. "Power Quality Analysis for Light-Duty Electric Vehicles: A Case Study in Malta," Energies, MDPI, vol. 16(15), pages 1-13, July.
    9. Jian, Linni & Zheng, Yanchong & Shao, Ziyun, 2017. "High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles," Applied Energy, Elsevier, vol. 186(P1), pages 46-55.
    10. Mladen Bošnjaković & Robert Santa & Zoran Crnac & Tomislav Bošnjaković, 2023. "Environmental Impact of PV Power Systems," Sustainability, MDPI, vol. 15(15), pages 1-26, August.
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