  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 of the PEV user.
