Game Theory Approach on Modeling of Residential Electricity Market by Considering the Uncertainty due to the Battery Electric Vehicles (BEVs)

The rapid progression of sophisticated advance metering infrastructure (AMI), allows us to have a better understanding and data from demand-response (DR) solutions. There are vast amounts of research on the internet of things and its application on the smart grids has been examined to find the most optimized bill for the user; however, we propose a novel approach of house loads, combined with owning a battery electric vehicle (BEV) equipped with the BEV communication controllers and vehicle-to-grid (V2G) technology. In this paper we use the Stackelberg game approach to achieve an efficient and effective optimized algorithm for the users (followers) based on time dependent pricing. We also assumed an electricity retailer company (leader) and a two-way bilateral communication procedure. The usage-based side of the game has been studied together with demand side management (DSM). Real-time pricing (RTP) from time-of-use (TOU) companies has been used for better results, and Monte Carlo simulation (MCS) handles the uncertain behavior of BEV drivers. Numerical results compared to those from the simulation show that with this method we can reshape the customer's demand for the best efficiency.