Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86833
Title: Charging and Discharging of Plug-In Electric Vehicles (PEVs) in Vehicle-to-Grid (V2G) Systems: A Cyber Insurance-Based Model
Authors: Hoang, Dinh Thai
Wang, Ping
Niyato, Dusit
Hossain, Ekram
Keywords: Plug-In Electric Vehicle
Cyber Insurance
Issue Date: 2017
Source: Hoang, D. T., Wang, P., Niyato, D., & Hossain, E. (2017). Charging and Discharging of Plug-In Electric Vehicles (PEVs) in Vehicle-to-Grid (V2G) Systems: A Cyber Insurance-Based Model. IEEE Access, 5, 732-754.
Series/Report no.: IEEE Access
Abstract: In addition to being environment friendly, vehicle-to-grid (V2G) systems can help the plug-in electric vehicle (PEV) users in reducing their energy costs and can also help stabilizing energy demand in the power grid. In V2G systems, since the PEV users need to obtain system information (e.g., locations of charging/discharging stations, current load, and supply of the power grid) to achieve the best charging and discharging performance, data communication plays a crucial role. However, since the PEV users are highly mobile, information from V2G systems is not always available for many reasons, e.g., wireless link failures and cyber attacks. Therefore, in this paper, we introduce a novel concept using cyber insurance to “transfer” cyber risks, e.g., unavailable information, of a PEV user to a third party, e.g., a cyber-insurance company. Under the insurance coverage, even without information about V2G systems, a PEV user is always guaranteed the best price for charging/discharging. In particular, we formulate the optimal energy cost problem for the PEV user by adopting a Markov decision process framework. We then propose a learning algorithm to help the PEV user make optimal decisions, e.g., to charge or discharge and to buy or not to buy insurance, in an online fashion. Through simulations, we show that cyber insurance is an efficient solution not only in dealing with cyber risks, but also in maximizing revenue for the PEV user.
URI: https://hdl.handle.net/10356/86833
http://hdl.handle.net/10220/44256
DOI: 10.1109/ACCESS.2017.2649042
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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