Stackelberg Game Approach on Modeling of Supply Demand Behavior Considering BEV Uncertainty

Abstract

Based on sophisticated metering infrastructure (AMI), one can use big data to provide demand-response (DR) solutions. There is a need to develop optimized cost structures for consumers. In this paper, Stackelberg game approaches are utilized, and residential loads are considered including battery electric vehicles (BEVs) equipped with BEV communication controllers and vehicle-to-grid (V2G) technologies. Efficient and effective optimized algorithms are developed for users (followers) based on time dependent pricing schemes. In the “games,” besides the followers, other participant is an electricity retailer company (leader), with a two-way bilateral communication procedure accepted and established by all participants. The user side of the games is related to the demand side management (DSM). Real-time pricing (RTP) from time-of-use (TOU) companies is used to achieve better results. Monte Carlo simulations (MCS) represent uncertain behaviors of BEV drivers. Results indicate that customers' demands can be met while reaching the best efficiency.