Abstract
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.
Highlights
With the adjustment of global energy strategies, the energy transition and comprehensive social benefits generated by the electric vehicle (EV) industry has attracted significant attention
Based on the distribution of fast charging demands in the target region, combined with different types of user charging selection criteria, this paper employs a fuzzy programming algorithm to achieve the multi-objective optimization of charging balance degree and user satisfaction degree, and the charging service fee at each fast charging station (FCS) is calculated and the changes of the indexes before and after cost adjustment can be obtained
This paper has proposed a price-setting strategy concerning the charging service fee at FCSs by considering both the charging balance degree and user satisfaction degree in the target region
Summary
With the adjustment of global energy strategies, the energy transition and comprehensive social benefits generated by the electric vehicle (EV) industry has attracted significant attention. In [9], a multi-objective control method was proposed, which was based on time-of-use (TOU) pricing and regarded the minimal charging fees and the earliest initial charging time as the objective of two-stage control, and the effect of “peak-load shifting” and users’ economical charging were achieved. Current research on the guidance effects of charging service fees based on “peak-valley price” only considers the perspective of the safe operation of grid, while neglecting different types of users’ response to the charging fees. We first analyze the responses of different types of users toward the selection of charging location and charging service fee, and we construct a multi-objective decision model based on the optimization goal of achieving both a degree of regional charging balance and user satisfaction. Analysis of Charging Location Selection and Charging Service Fee Response Based on
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