A Bayesian Real-Time Electric Vehicle Charging Strategy for Mitigating Renewable Energy Fluctuations

Abstract

A novel pricing and scheduling mechanism is proposed in this paper 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 for the peak shaving and frequency regulation responding, respectively. We design a fully distributed stochastic optimization mechanism using a 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 this 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, it is agent-based, and the agents share the information only with their optional neighbors, it is scale free, robust, and secure.