Cited By
View all- Paganini FEspindola EMarvid DFerragut A(2022)Optimization of spatial infrastructure for EV charging2022 IEEE 61st Conference on Decision and Control (CDC)10.1109/CDC51059.2022.9993162(5035-5041)Online publication date: 6-Dec-2022
EDF vehicle charging under deadline uncertainty
Pages 27 - 29
Abstract
In this paper, we analyze the performance of the EDF scheduling policy for charging electrical vehicles when the exact deadlines are not known by the scheduler. Instead, they are declared by users. We quantify the effect of this uncertainty in a mean field regime, and show that incentives appear for users to under-report their sojourn time. We characterize the average gain for a given uncertainty model and devise a policy to curtail strategic users.
References
[1]
EVQueues.jl, a Julia language library for simulating Electrical Vehicle charging policies. https://github.com/Grupo-MATE/EVQueues.jl.
[2]
Z. Lee, T. Li, and S. H. Low. ACN-Data: Analysis and applications of an open EV charging dataset. In Proceedings of the Tenth International Conference on Future Energy Systems, ACM e-Energy '19, June 2019.
[3]
W. Tang, S. Bi, and Y. J. Zhang. Online charging scheduling algorithms of electric vehicles in smart grid: An overview. IEEE communications Magazine, 54(12):76--83, 2016.
[4]
M. P. Wand and M. C. Jones. Kernel smoothing. CRC press, 1994.
[5]
M. Zeballos, A. Ferragut, and F. Paganini. Proportional fairness for EV charging in overload. IEEE Transactions on Smart Grid, 10(6):6792--6801, 2019.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 January 2022
Published in SIGMETRICS Volume 49, Issue 2
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View all- Paganini FEspindola EMarvid DFerragut A(2022)Optimization of spatial infrastructure for EV charging2022 IEEE 61st Conference on Decision and Control (CDC)10.1109/CDC51059.2022.9993162(5035-5041)Online publication date: 6-Dec-2022
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