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A Trading Mode Based on the Management of Residual Electric Energy in Electric Vehicles

Author

Listed:
  • Xiuli Wang

    (School of Electric Power and Architecture, Shanxi University, Taiyuan 030006, China)

  • Junkai Wei

    (School of Electric Power and Architecture, Shanxi University, Taiyuan 030006, China)

  • Fushuan Wen

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Kai Wang

    (State Grid Shanxi Electric Power Company, Taiyuan 030021, China)

Abstract

Aiming at the distributed resources of electric vehicles with photovoltaics (PVs) on the user side, a trading mode of surplus energy sharing for electric vehicles based on the user-side PVs is proposed by utilizing the bidirectional mobility of information and energy. Power transfer can be implemented between different electric vehicle users through vehicle-to-grid (V2G) technology with a reasonable distribution of benefits taken into account. First, the operational framework of electric energy trading is presented, and the transmission architecture of each body of interest in the system is analyzed. Second, the portraits of EV users’ charging behaviors are established considering their different charging habits, and electric vehicle users are divided into electricity buyers and sellers in each trading time period. An electricity transaction model based on “multi-seller–multi-buyer” is established, and all electricity transactions are realized through blockchain-based decentralized technology. Finally, the benefit to each interest group is maximized using the improved Northern Goshawk Optimization (NGO) algorithm. Simulation results of a sample system indicate that the new power trading mode proposed in this study could lead to reasonable reuse of the electric energy of private electric vehicles and can achieve a win–win situation for all stakeholders.

Suggested Citation

  • Xiuli Wang & Junkai Wei & Fushuan Wen & Kai Wang, 2023. "A Trading Mode Based on the Management of Residual Electric Energy in Electric Vehicles," Energies, MDPI, vol. 16(17), pages 1-23, August.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6317-:d:1229503
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    References listed on IDEAS

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    1. Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
    2. 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).
    3. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    4. Zhihang Zhang & Languang Lu & Yalun Li & Hewu Wang & Minggao Ouyang, 2023. "Accurate Remaining Available Energy Estimation of LiFePO 4 Battery in Dynamic Frequency Regulation for EVs with Thermal-Electric-Hysteresis Model," Energies, MDPI, vol. 16(13), pages 1-28, July.
    5. Tim Jonas & Noah Daniels & Gretchen Macht, 2023. "Electric Vehicle User Behavior: An Analysis of Charging Station Utilization in Canada," Energies, MDPI, vol. 16(4), pages 1-19, February.
    6. Zheng, Boshen & Wei, Wei & Chen, Yue & Wu, Qiuwei & Mei, Shengwei, 2022. "A peer-to-peer energy trading market embedded with residential shared energy storage units," Applied Energy, Elsevier, vol. 308(C).
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    Cited by:

    1. Sara Khan & Uzma Amin & Ahmed Abu-Siada, 2024. "P2P Energy Trading of EVs Using Blockchain Technology in Centralized and Decentralized Networks: A Review," Energies, MDPI, vol. 17(9), pages 1-17, April.

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