Study on Optimal V2G Pricing Strategy under Multi-Aggregator Competition Based on Game Theory

Abstract

Aggregators act as interfaces between distribution system and multiple EV users. Under the real time tariff systems, EVs, especially non-commercial commuters tend to charge during the low-price period at night, will participate in V2G during peak-load hour in the day. In order to maintain aggregator's V2G capability and be competitive among fellow aggregators, V2G pricing mechanism needs to be studied for aggregators. V2G service price is affected by distribution network's purchase price from aggregator, aggregator's V2G capability, the features of committed EV users driving behavior and the level and dynamics of SOH of their EV batteries, and the V2G price of aggregator's competitors. In this study, we firstly built EV user's profit model based on EV batteries loss model and EV users' inconvenience cost. Secondly, by using game theory, we study the aggregator's optimal V2G pricing mechanism against its competitors. Distribution companies, EV aggregators and EV users have their own independent profit maximization objectives as different entities. At the same time, the decision variables of different entities interact and restrict each other. By using the method of game theory, interests of different parties are taken into account in order to achieve a win-win-win situation among all participants. The pricing mechanism is verified for 3 aggregators and 2000 EVs. Simulation results verify the effectiveness of the V2G pricing mechanism.