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Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm

Author

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  • Qiongjie Dai

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China
    School of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos 017000, Inner Mongolia, China)

  • Jicheng Liu

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China)

  • Qiushuang Wei

    (School of Economics and Management, North China Electric Power University, Changping, Beijing 102206, China)

Abstract

In order to effectively improve the utilization rate of solar energy resources and to develop sustainable urban efficiency, an integrated system of electric vehicle charging station (EVCS), small-scale photovoltaic (PV) system, and battery energy storage system (BESS) has been proposed and implemented in many cities around the world. This paper proposes an optimization model for grid-connected photovoltaic/battery energy storage/electric vehicle charging station (PBES) to size PV, BESS, and determine the charging/discharging pattern of BESS. The multi-agent particle swarm optimization (MAPSO) algorithm solves this model is solved, which combines multi-agent system (MAS) and the mechanism of particle swarm optimization (PSO). In this model, a load simulation model is presented to simulate EV charging patterns and to calculate the EV charging demand at each time interval. Finally, a case in Shanghai, China is conducted and three scenarios are analyzed to prove the effectiveness of the proposed model. A comparative analysis is also performed to show the superiority of MAPSO algorithm.

Suggested Citation

  • Qiongjie Dai & Jicheng Liu & Qiushuang Wei, 2019. "Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:1973-:d:219517
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    16. Xiang Liao & Beibei Qian & Zhiqiang Jiang & Bo Fu & Hui He, 2023. "Integrated Energy Station Optimal Dispatching Using a Novel Many-Objective Optimization Algorithm Based on Multiple Update Strategies," Energies, MDPI, vol. 16(13), pages 1-26, July.
    17. Huang, Jiangfan & An, Qing & Zhou, Mingyu & Tang, Ruoli & Dong, Zhengcheng & Lai, Jingang & Li, Xin & Yang, Xiangguo, 2024. "A self-adaptive joint optimization framework for marine hybrid energy storage system design considering load fluctuation characteristics," Applied Energy, Elsevier, vol. 361(C).
    18. Mohd Bilal & Ibrahim Alsaidan & Muhannad Alaraj & Fahad M. Almasoudi & Mohammad Rizwan, 2022. "Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-40, March.
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    20. Phillip K. Agbesi & Rico Ruffino & Marko Hakovirta, 2023. "The development of sustainable electric vehicle business ecosystems," SN Business & Economics, Springer, vol. 3(8), pages 1-59, August.
    21. Iliana Ilieva & Bernt Bremdal, 2021. "Utilizing Local Flexibility Resources to Mitigate Grid Challenges at Electric Vehicle Charging Stations," Energies, MDPI, vol. 14(12), pages 1-15, June.
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