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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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    References listed on IDEAS

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    4. Lamnatou, Chr. & Chemisana, D. & Cristofari, C., 2022. "Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment," Renewable Energy, Elsevier, vol. 185(C), pages 1376-1391.
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    13. Kenji Araki & Yasuyuki Ota & Anju Maeda & Minoru Kumano & Kensuke Nishioka, 2023. "Solar Electric Vehicles as Energy Sources in Disaster Zones: Physical and Social Factors," Energies, MDPI, vol. 16(8), pages 1-25, April.
    14. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    15. Mohammad K. Najjar & Eduardo Linhares Qualharini & Ahmed W. A. Hammad & Dieter Boer & Assed Haddad, 2019. "Framework for a Systematic Parametric Analysis to Maximize Energy Output of PV Modules Using an Experimental Design," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    16. Xinghan Xu & Weijie Ren, 2019. "Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM 2.5 Concentration Forecasting: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(11), pages 1-19, May.
    17. Abdullah Al Shereiqi & Amer Al-Hinai & Mohammed Albadi & Rashid Al-Abri, 2020. "Optimal Sizing of a Hybrid Wind-Photovoltaic-Battery Plant to Mitigate Output Fluctuations in a Grid-Connected System," Energies, MDPI, vol. 13(11), pages 1-21, June.
    18. 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).
    19. Kameswara Satya Prakash Oruganti & Chockalingam Aravind Vaithilingam & Gowthamraj Rajendran & Ramasamy A, 2019. "Design and Sizing of Mobile Solar Photovoltaic Power Plant to Support Rapid Charging for Electric Vehicles," Energies, MDPI, vol. 12(18), pages 1-22, September.
    20. Dorian O. Sidea & Andrei M. Tudose & Irina I. Picioroaga & Constantin Bulac, 2022. "Two-Stage Optimal Active-Reactive Power Coordination for Microgrids with High Renewable Sources Penetration and Electrical Vehicles Based on Improved Sine−Cosine Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    21. Xihao Wang & Xiaojun Wang & Yuqing Liu & Chun Xiao & Rongsheng Zhao & Ye Yang & Zhao Liu, 2022. "A Sustainability Improvement Strategy of Interconnected Data Centers Based on Dispatching Potential of Electric Vehicle Charging Stations," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
    22. 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.
    23. 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.
    24. Mahyar Alinejad & Omid Rezaei & Reza Habibifar & Mahdi Azimian, 2022. "A Charge/Discharge Plan for Electric Vehicles in an Intelligent Parking Lot Considering Destructive Random Decisions, and V2G and V2V Energy Transfer Modes," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    25. Alaa Farah & Hamdy Hassan & Alaaeldin M. Abdelshafy & Abdelfatah M. Mohamed, 2020. "Optimal Scheduling of Hybrid Multi-Carrier System Feeding Electrical/Thermal Load Based on Particle Swarm Algorithm," Sustainability, MDPI, vol. 12(11), pages 1-21, June.

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