LoPrO: Location Privacy-preserving Online auction scheme for electric vehicles joint bidding and charging

Abstract

Developments in the Internet of Things and cyber–physical systems have enabled intelligent solutions to alleviate peak loads, and transmission and storage concerns in MicroGrids (MGs) through the deployment of Electrical Vehicles (EVs), as well as provide a platform for EV energy exchange. Nonetheless, EV participation depends upon the willingness of the owners, who can enter and leave the market in a random fashion with multiple demands. Clever participants may be tempted to misrepresent their information to achieve a greater personal reward. Also, utilizing location information from users will make them susceptible to privacy leakage, but is necessary for the appropriate allocation of services. To resolve these challenges, we propose a Location Privacy-preserving Online (LoPrO) scheme, which can allocate electricity and charging stations in MicroGrids to EVs when the energy supply is limited. In our scheme, the optimal decision of winner determination and the electricity and charging station allocations are made by the auctioneer, without prior knowledge of EV arrival and departure, and EV location information privacy is differentially guaranteed by leveraging the Laplace mechanism. Through theoretical analysis, we prove that LoPrO scheme achieves the properties of incentive compatibility, individual rationality, and ϵ-differential privacy. The results of our experimental evaluation also demonstrate that LoPrO achieves better performance with respect to EV utility, buyer satisfaction ratio, electricity allocation efficiency and EV State-of-Charge (SoC), in comparison with existing schemes. In terms of privacy preservation, LoPrO is capable of protecting EV location information with low probability of leakage, and computation overhead is reasonable.