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

Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK
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Author to whom correspondence should be addressed.
Academic Editor: Pierluigi Siano
Energies 2021, 14(21), 7015; https://doi.org/10.3390/en14217015
Received: 26 September 2021 / Revised: 22 October 2021 / Accepted: 23 October 2021 / Published: 26 October 2021
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. View Full-Text
Keywords: electrical vehicle (EV); vehicle-to-grid (V2G); bi-level; ancillary service; demand response; optimisation; risk-aversion; aggregator; conditional value-at-risk 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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MDPI and ACS Style

Wang, Y.; Jia, Z.; Li, J.; Zhang, X.; Zhang, R. Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk. Energies 2021, 14, 7015. https://doi.org/10.3390/en14217015

AMA Style

Wang Y, Jia Z, Li J, Zhang X, Zhang R. Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk. Energies. 2021; 14(21):7015. https://doi.org/10.3390/en14217015

Chicago/Turabian Style

Wang, Yilu, Zixuan Jia, Jianing Li, Xiaoping Zhang, and Ray Zhang. 2021. "Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk" Energies 14, no. 21: 7015. https://doi.org/10.3390/en14217015

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