An Elastic Charging Service Fee-Based Load Guiding Strategy for Fast Charging Stations

Compared with the traditional slow charging loads, random integration of large scale fast charging loads will exert more serious impacts on the security of power network operation. Besides, to maximize social benefits, effective scheduling strategies guiding fast charging behaviors should be formulated rather than simply increasing infrastructure construction investments on the power grid. This paper first analyzes the charging users’ various responses to an elastic charging service fee, and introduces the index of charging balance degree to a target region by considering the influence of fast charging loads on the power grid. Then, a multi-objective optimization model of the fast charging service fee is constructed, whose service fee can be further optimized by employing a fuzzy programming method. Therefore, both users’ satisfaction degree and the equilibrium of charging loads can be maintained simultaneously by reasonably guiding electric vehicles (EVs) to different fast charging stations. The simulation results demonstrate the effectiveness of the proposed dynamic charging service pricing and the corresponding fast charging load guidance strategy.