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Ancillary Services Bidding for Uncertain Bidirectional V2G Using Fuzzy Linear Programming

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  • Faddel, Samy
  • Aldeek, A.
  • Al-Awami, Ali T.
  • Sortomme, Eric
  • Al-Hamouz, Zakariya

Abstract

The operation of bidirectional V2G participating in ancillary services markets is particularly challenging due to various uncertainties. This work proposes an algorithm to optimize the uncertain operation of bidirectional V2G for an electric vehicle (EV) aggregator. The proposed algorithm maximizes the profits of the aggregator while providing additional system flexibility and low charging costs to the EV owners. The proposed algorithm considers electricity market and EV mobility uncertainties using fuzzy linear programming. These uncertainties include those of the regulation and responsive reserve prices, deployment signals, and the energy used for EV trips. Unlike other works in the literature, the proposed algorithm is capable of considering a large number of uncertain parameters without greatly impacting the problem's complexity or simulation time. Also, the algorithm does not result in conservative solutions. The simulation results show the superiority of the proposed fuzzy algorithm over its deterministic counterpart in terms of higher realized profits. Also, the proposed approach results in lower real-time penalties, which ensures its superiority to provide the EVs with the required trip energy at the required time. In addition, it results in lower battery degradation costs, which helps increase the life time of the EVs.

Suggested Citation

  • Faddel, Samy & Aldeek, A. & Al-Awami, Ali T. & Sortomme, Eric & Al-Hamouz, Zakariya, 2018. "Ancillary Services Bidding for Uncertain Bidirectional V2G Using Fuzzy Linear Programming," Energy, Elsevier, vol. 160(C), pages 986-995.
    • Handle: RePEc:eee:energy:v:160:y:2018:i:c:p:986-995
    DOI: 10.1016/j.energy.2018.07.091
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    References listed on IDEAS

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    Cited by:

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    5. 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).
    6. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    7. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.
    8. Zhang, Qian & Wu, Xiaohan & Deng, Xiaosong & Huang, Yaoyu & Li, Chunyan & Wu, Jiaqi, 2023. "Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market," Applied Energy, Elsevier, vol. 341(C).
    9. Krzysztof Zagrajek, 2021. "A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision," Energies, MDPI, vol. 14(21), pages 1-38, November.
    10. Gržanić, M. & Capuder, T. & Zhang, N. & Huang, W., 2022. "Prosumers as active market participants: A systematic review of evolution of opportunities, models and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    11. Xiangchu Xu & Zewei Zhan & Zengqiang Mi & Ling Ji, 2023. "An Optimized Decision Model for Electric Vehicle Aggregator Participation in the Electricity Market Based on the Stackelberg Game," Sustainability, MDPI, vol. 15(20), pages 1-26, October.
    12. Mohamed El-Hendawi & Zhanle Wang & Xiaoyue Liu, 2022. "Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation," Energies, MDPI, vol. 15(12), pages 1-22, June.

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