A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations

Latifi, M., Khalili, A., Rastegarnia, A. and Sanei, S. ORCID: 0000-0002-3437-2801, 2018. A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations. IEEE Transactions on Industrial Informatics. ISSN 1551-3203

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Abstract

A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure.

Item Type: Journal article
Publication Title: IEEE Transactions on Industrial Informatics
Creators: Latifi, M., Khalili, A., Rastegarnia, A. and Sanei, S.
Publisher: Institute of Electrical and Electronics Engineers
Date: 6 September 2018
ISSN: 1551-3203
Identifiers:
NumberType
10.1109/tii.2018.2866267DOI
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 13 Sep 2018 11:14
Last Modified: 13 Sep 2018 11:14
URI: http://irep.ntu.ac.uk/id/eprint/34485

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