Multi-objective optimization for improving EV users’ adhesion with hybrid demand response strategy

Abstract

With the popularization of electric vehicles (EVs), the charging behavior of users will inevitably affect the power grid in the process of vehicle-to-grid (V2G). It is particularly important to consider the behavior characteristics and charging habits of EV users to guide their charging behavior. This paper presents a hybrid demand response (HDR) strategy based on price-sensitive demand response (PSDR), as well as incentive-based demand response (IBDR) strategy. PSDR strategy based on time-of-use (TOU) price is to guide EV users charging behavior by implementing dynamic TOU price. IBDR strategy is to put forward a charging point accumulation mechanism based on incentive subsidies and set charging reward and punishment points for different types of users according to the type of users. Then converting the TOU price into the form of points and setting a limited value for incentive points in combination with peak and valley periods to obtain the HDR strategy. Considering the uncertainty of users’ response, the model of users’ participation response is established. The multi-objective optimization model of reward and punishment coefficient and unit integral value is established by comprehensively considering various benefits. Finally, simulation results show that this strategy can effectively improve the adhesion of EV users and reduce the impact of their charging on the power grid.