Abstract: Guiding the charging behavior of electric vehicles （EVs） through price means helps to reduce the adverse effects of a large number of EVs access to the power system. This paper proposes an optimization model for floating charging service fee to guide EV users to charge more reasonably. Firstly， EV users are classified according to user preference. And on this basis， users charging utility model based on prospect theory is established. Secondly， the transfer probability matrix is used to establish the price response model of EV users. Then comprehensively considering the interests of the grid， charging stations and users， a multi-objective optimization model is established for floating service fee. Finally， the multi-objective particle swarm optimization algorithm is improved on the basis of adaptive grid archiving by non-uniform mutation operation and the model is solved by using the proposed algorithm. Taking a typical urban area as an example， the optimization results of floating service fee under different base loads， the user price response results under different service fee mechanisms and the user response behavior under different user compositions are compared and analyzed. The correctness and effectiveness of the mechanism and model described in this paper are verified. The results of these examples show that the floating service fee mechanism and its optimization model mentioned in this paper can further guide the charging behavior based on the time-of-use electricity price mechanism and play the role of cutting peak and filling the valley as well as ensuring the interests of all aspects.

Key words: floating service fee, prospect theory, charging utility, price response, non-uniform mutation