基于用户行为和分时电价的充电定价策略优化

Optimization of charging pricing strategy based on user behavior and time-of-use tariffs

考虑用户充电行为和竞争对手定价等因素,研究公共充电站的充电定价问题.建立用户价格响应模型,量化分析充电价格与用户需求的内在关系.基于用户价格响应和分时电价,提出一种两阶段充电定价策略:第1阶段基于用户的充电频率分布,采用K-means聚类算法将一天划分为峰时段、平时段和谷时段;第2阶段,分析各时段的充电供需,峰时段以充电站利润最大化为优化目标,平谷时段以充电站利润和利用率最大化为目标,构建分段式定价优化模型,并分别采用遗传算法和NSGA-Ⅱ算法求解模型.基于某市充电站的运营数据,完成案例分析.结果表明:峰时段总体利润提升约13.4%,充电桩利用率提高约26.8%;平谷时段共获得17组Pareto最优解,均优于固定电价机制.所提出的充电定价策略可以为充电站运营与规划提供决策参考.

Considering the user's charging behavior and competitor's pricing,the charging pricing of public charging stations was studied.A user price response model was established to quantify the internal relationship between the charging price and user demand.Based on the user price response and time-of-use tariffs,a two-stage charging pricing strategy was proposed.In the first stage,based on the distribution of the user's charging frequency,the K-means clustering algorithm was used to divide a day into three periods,i.e.peak,flat and valley periods,respectively.In the second stage,the characteristics of charging supply and demand in each period were analyzed,and the price optimization models were constructed,with the aim to maximize the profit of the charging station in the peak period,and maximize the profit as well as the utilization rate of the charging station in the other periods,respectively.Moreover,the genetic algorithm and NSGA-Ⅱalgorithm were adopted to solve the optimization models,respectively.A case study with the operation data of charging stations in a city was implemented.The results show that the total profit is increased by 13.4%,the utilization rate of the charging piles is increased by 26.8%in the peak period,and 17 sets of Pareto optimal solutions are obtained in the flat and valley periods,which are better than the fixed-pricing mechanism.The proposed charging pricing strategy can provide a reference for the operation and planning of charging station.