NDLI: Charging and Discharging of Plug-In Electric Vehicles (PEVs) in Vehicle-to-Grid (V2G) Systems: A Cyber Insurance-Based Model

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Dinh Thai Hoang Ping Wang Dusit Niyato Ekram Hossain
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.
e-ISSN	21693536
DOI	10.1109/ACCESS.2017.2649042
Journal	IEEE Access
Volume Number	5
Language	English
Publisher	IEEE
Publisher Date	2017-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Cyber Insurance Plug-in Electric Vehicle Vehicle Charging Vehicle-to-grid Markov Decision Process
Content Type	Text
Resource Type	Article

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