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Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation

Author

Listed:
  • Yongyi Huang

    (Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

  • Atsushi Yona

    (Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

  • Hiroshi Takahashi

    (Fuji Electric Co., Ltd., Tokyo 191-0064, Japan)

  • Ashraf Mohamed Hemeida

    (Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

  • Paras Mandal

    (Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA)

  • Alexey Mikhaylov

    (The Russian Federation, Financial University, Moscow 124167, Russia)

  • Tomonobu Senjyu

    (Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

  • Mohammed Elsayed Lotfy

    (Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
    Electrical Power and Machines Department, Zagazig University, Zagazig 44519, Egypt)

Abstract

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

Suggested Citation

  • Yongyi Huang & Atsushi Yona & Hiroshi Takahashi & Ashraf Mohamed Hemeida & Paras Mandal & Alexey Mikhaylov & Tomonobu Senjyu & Mohammed Elsayed Lotfy, 2021. "Energy Management System Optimization of Drug Store Electric Vehicles Charging Station Operation," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6163-:d:565609
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    References listed on IDEAS

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    Cited by:

    1. Alessandro Di Giorgio & Emanuele De Santis & Lucia Frettoni & Stefano Felli & Francesco Liberati, 2023. "Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference," Energies, MDPI, vol. 16(3), pages 1-16, January.
    2. He, Hongjing & Huang, Yongyi & Nakadomari, Akito & Masrur, Hasan & Krishnan, Narayanan & Hemeida, Ashraf M. & Mikhaylov, Alexey & Senjyu, Tomonobu, 2023. "Potential and economic viability of green hydrogen production from seawater electrolysis using renewable energy in remote Japanese islands," Renewable Energy, Elsevier, vol. 202(C), pages 1436-1447.
    3. Lorenzo Ricciardi Celsi & Anna Valli, 2023. "Applied Control and Artificial Intelligence for Energy Management: An Overview of Trends in EV Charging, Cyber-Physical Security and Predictive Maintenance," Energies, MDPI, vol. 16(12), pages 1-23, June.
    4. 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.

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    Keywords

    V2H; MILP; energy management; optimization; energy economics;
    All these keywords.

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