基于Q-Learning算法用户最优充电站运营研究

充电站运营系统是为电动汽车提供充电服务,并与电网、充电站与电力网、交通网数据中心相连接,通过数据库的信息共享、互动和数据挖掘,达到充电站高效运营管理。本文就智能充电站的运营系统的整体结构与数据分析进行探讨。运用Q-Learning算法从用户实现总成本的最优(小),作出最佳的充电服务选择角度分析用户充电决策,可在运营数据挖掘中预测用户的充电行为规律,进而预测运营的充电站设备利用状况,能够对不同充电设施规划方案下的充电负荷进行更加真实准确的计算,从而获得更加准确的充电站运行成本,最终为充电站制定的充电服务运营策略提供依据帮助,进而帮助充电站提高设备利用,提升充电站运营效益。也为日后扩建、新建充电站设备数量、类型、地点等信息提供决策依据。

Research on Operation of User Optimal Charging Station Based on Q-Learning Algorithms

Charging station operation system provides charging service for electric vehicles, and connects with power grid, charging station, power network and traffic network data center. It achieves efficient operation and management of charging station through information sharing, interaction and data mining of database. In this paper, the overall structure and data analysis of the operation system of intelligent charging station are discussed. Using Q-Learning algorithm to analyze user charging decision from the point of view of realizing the optimum (small) total cost and making the optimum charging service selection can predict user charging behavior rule in operation data mining, and then predict the utilization status of operating charging station equipment, which can charge under different charging facility planning schemes. Load calculation is more real and accurate, so as to obtain more accurate operation cost of charging station, and ultimately provide basis for charging service operation strategy formulated by charging station, and then help charging station to improve equipment utilization and operational efficiency of charging station. It also provides decision-making basis for future expansion and new charging station equipment quantity, type, location and other information.