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Service-quality based pricing approach for charging electric vehicles in smart energy communities.

Authors :
Wang, Yanliang
Xiang, Yue
Hu, Haifeng
Lao, Keng Weng
Tong, Jun
Jiang, Yi
Source :
Journal of Cleaner Production. Sep2023, Vol. 420, pN.PAG-N.PAG. 1p.
Publication Year :
2023

Abstract

Guiding electric vehicles (EVs) in smart energy communities to participate in demand response can significantly benefit users and the load aggregator. However, when the user's travel plans are uncertain, the EV may end up charging before the planned time, leaving the user with an insufficient power supply. This can occur because the control program chooses to charge the EV during periods of low electricity prices to reduce user costs. To address this issue, a new strategy for charging pricing based on service-quality is proposed to help users deal with the uncertainty of travel plans and promote user participation in demand response. In this paper, the proposed charging pricing of EVs consists of three parts. Firstly, the model of charging packages with different service qualities is designed for users. The differences in charging packages are reflected in the percentage of power supply and price within each time slot. Charging packages provide users with at least a certain amount of power in each time slot, allowing them to better cope with the uncertainty of their travel plans. Secondly, a subscription model for charging packages is built to match the user's travel plans. Finally, the pricing model of the charging package is established, representing the master-slave game between the load aggregator and the user. The model is linearized by the KKT condition. The results show that the proposed strategy helps users cope with the uncertainty of their travel plans, reducing charging cost for users by 16.39% while increasing revenue for the load aggregator by 14.8%. • A novel charging package for electric vehicles based on service-quality is proposed. • A pricing model is established as a master-slave game formulation. • The uncertainty of the EV users' travel plan is effectively handled. • The user 's charging cost is reduced by 16.39% in the case. • The revenue of the load aggregator is increased by 14.8% in the case. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09596526
Volume :
420
Database :
Academic Search Index
Journal :
Journal of Cleaner Production
Publication Type :
Academic Journal
Accession number :
170043907
Full Text :
https://doi.org/10.1016/j.jclepro.2023.138416