摘要
为缓解电动汽车集中充电对电网的冲击,提出了一种动态电价驱动的电动汽车优化调度策略,以降低用户综合充电成本并实现电网削峰填谷。首先,构建基于朴素贝叶斯的行程链模型,利用用户出行数据生成目标区域用户未来行程信息。然后,将用户行程信息与能耗模型、充电决策模型结合,得到区域充电负荷的时空分布。随后,建立动态电价模型,描述电价与电网负荷的关系,并以最小化充电成本和电池老化成本为目标,采用遗传算法求解并实时更新电动汽车全天充放电策略。仿真结果表明,所提出模型生成的行程链数据具有可靠性,其优化策略能够有效降低用户成本、实现电网削峰填谷,为电动汽车与电网高效协调提供了理论基础和控制方法。
To mitigate the impact of centralized Electric Vehicle(EV)charging on the power grid,this study proposes an optimal EV scheduling strategy driven by dynamic electricity pricing,aiming to reduce users’comprehensive charging costs and achieve peak shaving and valley filling of the grid.First,a travel chain model based on naive Bayes is constructed to generate detailed information on future trips of users in the target area using historical travel data.Then,user trip information is combined with an established energy consumption model and a charging decision model to obtain the spatiotemporal distribution of regional charging load.Later,a dynamic electricity pricing model is established to describe the relationship between electricity prices and grid load.Subsequently,with the objective of minimizing charging costs and battery aging costs,a genetic algorithm is adopted to solve and update the EV charging and discharging strategies in real time throughout the day.Simulation results show that the travel chain data generated Its optimization strategy can effectively reduce user costs,achivev grid peak shaving and valley filling and provide a theoretical basis and control method for efficient coordination between electric vehicles and the power grid.
作者
梁士福
姜瑞
李金洺
孟祥怡
刘芷彤
荆海信
赵一鸣
Liang Shifu;Jiang Rui;Li Jinming;Meng Xiangyi;Liu Zhitong;Jing Haixin;Zhao Yiming(Global R&D Center,China FAW Corporation Limited,Changchun 130013)
出处
《汽车文摘》
2026年第3期23-31,共9页
Automotive Digest
关键词
电动汽车
车网互动
行程链
动态分时电价
实时优化
充放电控制
Electric Vehicle(EV)
Vehicle to Grid(V2G)
Journey chains
Dynamic time-ofuse pricing
Real-time optimization
Charge and discharge control
