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Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers

Author

Listed:
  • Jian-Tang Liao

    (Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan)

  • Hao-Wei Huang

    (Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan)

  • Hong-Tzer Yang

    (Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan)

  • Desheng Li

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
    National Innovation Center of Energy and Information for N.E.V. Ltd., Nanjing 210000, China)

Abstract

With a rapid increase in the awareness of carbon reduction worldwide, the industry of electric vehicles (EVs) has started to flourish. However, the large number of EVs connected to a power grid with a large power demand and uncertainty may result in significant challenges for a power system. In this study, the optimal charging and discharging scheduling strategies of G2V/V2G and battery energy storage system (BESS) were proposed for EV charging stations. A distributed computation architecture was employed to streamline the complexity of an optimization problem. By considering EV charging/discharging conversion efficiencies for different load conditions, the proposed method was used to maximize the operational profits of each EV and BESS based on the related electricity tariff and demand response programs. Moreover, the behavior model of drivers and cost of BESS degradation caused by charging and discharging cycles were considered to improve the overall practical applicability. An EV charging station with 100 charging piles was simulated as an example to verify the feasibility of the proposed method. The developed algorithms can be used for EV charging stations, load aggregators, and service companies integrated with distributed energy resources in a smart grid.

Suggested Citation

  • Jian-Tang Liao & Hao-Wei Huang & Hong-Tzer Yang & Desheng Li, 2021. "Decentralized V2G/G2V Scheduling of EV Charging Stations by Considering the Conversion Efficiency of Bidirectional Chargers," Energies, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:962-:d:497974
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    Citations

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    Cited by:

    1. Oussama Ouramdane & Elhoussin Elbouchikhi & Yassine Amirat & Ehsan Sedgh Gooya, 2021. "Optimal Sizing and Energy Management of Microgrids with Vehicle-to-Grid Technology: A Critical Review and Future Trends," Energies, MDPI, vol. 14(14), pages 1-45, July.
    2. Hussain, Shahid & Irshad, Reyazur Rashid & Pallonetto, Fabiano & Hussain, Ihtisham & Hussain, Zakir & Tahir, Muhammad & Abimannan, Satheesh & Shukla, Saurabh & Yousif, Adil & Kim, Yun-Su & El-Sayed, H, 2023. "Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles," Applied Energy, Elsevier, vol. 352(C).
    3. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.
    4. Jiwen Qi & Li Li, 2023. "Economic Operation Strategy of an EV Parking Lot with Vehicle-to-Grid and Renewable Energy Integration," Energies, MDPI, vol. 16(4), pages 1-16, February.

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