Game Theory Based Optimal Pricing Strategy for V2G Participating in Demand Response

Abstract

Aggregators, as the interface of electric vehicles (EVs) and distribution system, are the market agents of EVs to participate in demand response (DR) and other balancing services. Aggregators can enlarge its benefit room from vehicle-to-grid (V2G) by providing flexible V2G price to EV users as long as they can secure a certain amount of V2G energy. There is a need to study Aggregator's V2G pricing strategy due to the fact that distribution companies, aggregators, and EV users have their own independent benefit maximization objectives as independent entities, and the decision variables of different entities interact and restrict each other. In this study, an optimal V2G pricing strategy for each aggregator is obtained by using Stackelberg game during the gaming of the involved multiple entities. The benefits of aggregator and EV users are set as the game factors, and benefit models of EV user's and aggregator's are established with consideration of the historical charging cost, and users' inconvenience cost. A simulation of V2G price for 750 EVs and 3 aggregators is carried out, and the impact of the market volume of EVs on each aggregator under this pricing strategy is studied. Simulation results show the feasibility of the proposed model and the interaction among the randomness of driving behavior and the market volume of EV groups, loading level of the distribution system, V2G strategies, as well as the DR capabilities.