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An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid

Author

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  • Muhammad Ahsan Khan

    (Department of Electrical Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea)

  • Akhtar Hussain

    (Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Woon-Gyu Lee

    (Department of Electrical Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea)

  • Hak-Man Kim

    (Department of Electrical Engineering, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea
    Research Institute for Northeast Asian Super Grid, Incheon National University, 119 Academy-ro, Yeonsu-gu, Incheon 22012, Republic of Korea)

Abstract

The growing penetration of electric vehicles (EVs) introduces both opportunities and challenges for power grid operators. Incentivization is considered a viable option to tempt EV owners to participate in supporting the grid during peak load intervals while receiving compensation for their services. Therefore, this study proposes a two-step incentive mechanism to reduce the peak load of the grid by enabling power trading among the microgrid, EVs and the utility grid. In the first step, an incentive price is determined for EVs considering the grid-loading conditions during different hours of the day. In the second step, a multi-objective optimization problem is formulated to optimize trading among different entities, such as EVs, the microgrid and the utility grid. The two objectives considered in this study are the operation cost of the microgrid and the revenue of EVs. Monte Carlo simulations are used to deal with uncertainties associated with EVs. Simulations are conducted to analyze the impact of different weight parameters on the energy-trading amount and operation cost of EVs and MG. In addition, a sensitivity analysis is conducted to analyze the impact of changes in the EV fleet size on the energy-trading amount and operation cost.

Suggested Citation

  • Muhammad Ahsan Khan & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2023. "An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid," Energies, MDPI, vol. 16(17), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6359-:d:1231381
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    References listed on IDEAS

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    1. Yang, Qingqing & Li, Jianwei & Cao, Wanke & Li, Shuangqi & Lin, Jie & Huo, Da & He, Hongwen, 2020. "An improved vehicle to the grid method with battery longevity management in a microgrid application," Energy, Elsevier, vol. 198(C).
    2. Huang, Bing & Meijssen, Aart Gerard & Annema, Jan Anne & Lukszo, Zofia, 2021. "Are electric vehicle drivers willing to participate in vehicle-to-grid contracts? A context-dependent stated choice experiment," Energy Policy, Elsevier, vol. 156(C).
    3. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    4. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    5. Thomas Steffen & Ashley Fly & William Mitchell, 2020. "Optimal Electric Vehicle Charging Considering the Effects of a Financial Incentive on Battery Ageing," Energies, MDPI, vol. 13(18), pages 1-15, September.
    6. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
    7. Aghajani, Saemeh & Kalantar, Mohsen, 2017. "A cooperative game theoretic analysis of electric vehicles parking lot in smart grid," Energy, Elsevier, vol. 137(C), pages 129-139.
    8. Asfand Yar Ali & Akhtar Hussain & Ju-Won Baek & Hak-Man Kim, 2020. "Optimal Operation of Networked Microgrids for Enhancing Resilience Using Mobile Electric Vehicles," Energies, MDPI, vol. 14(1), pages 1-20, December.
    9. Pearre, Nathaniel S. & Ribberink, Hajo, 2019. "Review of research on V2X technologies, strategies, and operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 61-70.
    10. Ma, Shao-Chao & Xu, Jin-Hua & Fan, Ying, 2019. "Willingness to pay and preferences for alternative incentives to EV purchase subsidies: An empirical study in China," Energy Economics, Elsevier, vol. 81(C), pages 197-215.
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