Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk

Yilu Wang, Zixuan Jia, Jianing Li, Xiaoping Zhang, Ray Zhang

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With the global net-zero strategy implementation, decarbonisation of transport by massive deployment of electric vehicles (EVs) has been considered to be an essential solution. However, charging EVs and integration into electricity grids is going to be a fundamental challenge to future electricity systems. Hence, in this situation, how to effectively deploy massive numbers of EVs, and in the meantime what can be developed to deliver vehicle-to-grid (V2G) services, become a fundamental yet interesting tech-economical issues. Furthermore, uncertainty in lack of vehicle availability and EV battery degradation could lead to revenue loss when using EVs as ancillary services aggregators. With such considerations, this paper presents a new optimised V2G aggregator scheduling service that has taken into consideration of a number of risks, including EV availability and battery degradation through conditional value-at-risk. The proposed method for V2G scheduling service, as an independent aggregator, is formulated as a bi-level optimisation problem. The performance of the proposed method is to be evaluated through case studies on the Birmingham International Airport parking lot with onsite renewable generation. Uncertainties of EVs and the differences in weekdays and weekends are also compared.
Original languageEnglish
Article number7015
Number of pages16
Issue number21
Early online date26 Oct 2021
Publication statusE-pub ahead of print - 26 Oct 2021


  • electrical vehicle (EV)
  • vehicle-to-grid (V2G)
  • bi-level
  • ancillary service
  • demand response
  • optimisation
  • risk-aversion
  • aggregator
  • conditional value-at-risk


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