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The reserve trading model considering V2G Reverse

Author

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  • Lefeng, Shi
  • Qian, Zhang
  • Yongjian, Pu
Abstract
With the popularity of plug-in electric vehicles, V2G reserve will inevitably impact on the traditional reserve trading model. By analyzing the differences and similarities between V2G reserve and generation side reserve in terms of reliability and economy, this paper pointed out the trading features of the reserve market including V2G reserve, and analyzed the interactive relationships of the relevant reserve need, reserve supply and reserve price under the requirement of certain reliability. To achieve the minimal total cost of the purchase cost and risk cost of reserve trade, and taking reliability electricity price as measurement index, a new reserve trading model of electric companies concerning the V2G reserve was proposed. Furthermore, a specific solution method was put forward according to the sequential bidding. This new model, taking default probability of plug-in electric vehicle users and accident probability of generation side reserve into consideration, realized the optimal scheduling of the V2G reserve and generation side reserve, which provide a new way for future reserve trading model considering V2G reverse. Finally, the effectiveness and validity of the proposed trading model were illustrated by a numerical example.

Suggested Citation

  • Lefeng, Shi & Qian, Zhang & Yongjian, Pu, 2013. "The reserve trading model considering V2G Reverse," Energy, Elsevier, vol. 59(C), pages 50-55.
    • Handle: RePEc:eee:energy:v:59:y:2013:i:c:p:50-55
    DOI: 10.1016/j.energy.2013.07.030
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    References listed on IDEAS

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    1. Delucchi, Mark A. & Jacobson, Mark Z., 2011. "Providing all global energy with wind, water, and solar power, Part II: Reliability, system and transmission costs, and policies," Energy Policy, Elsevier, vol. 39(3), pages 1170-1190, March.
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    5. Sovacool, Benjamin K. & Hirsh, Richard F., 2009. "Beyond batteries: An examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition," Energy Policy, Elsevier, vol. 37(3), pages 1095-1103, March.
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    Cited by:

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    3. Amirioun, Mohammad Hassan & Kazemi, Ahad, 2014. "A new model based on optimal scheduling of combined energy exchange modes for aggregation of electric vehicles in a residential complex," Energy, Elsevier, vol. 69(C), pages 186-198.
    4. 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).
    5. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    6. Chen, Yang & Hu, Mengqi & Zhou, Zhi, 2017. "A data-driven analytical approach to enable optimal emerging technologies integration in the co-optimized electricity and ancillary service markets," Energy, Elsevier, vol. 122(C), pages 613-626.
    7. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 720-732.
    8. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    9. Hidrue, Michael K. & Parsons, George R., 2015. "Is there a near-term market for vehicle-to-grid electric vehicles?," Applied Energy, Elsevier, vol. 151(C), pages 67-76.
    10. Weiller, C. & Neely, A., 2014. "Using electric vehicles for energy services: Industry perspectives," Energy, Elsevier, vol. 77(C), pages 194-200.

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