摘要
电动汽车(electric vehicle,EV)无序充电行为导致用电负荷峰值激增,对电网的安全稳定运行造成不利影响,因此,本文提出了一种基于分时电价的电动汽车充电负荷优化策略,首先,建立电动汽车充电负荷模型,采用蒙特卡洛法模拟电动汽车无序充电情况,其次,在峰、谷、平时段划分上建立以系统总负荷均方差最小和用户充电成本最低为目标的多目标模型,并引入价格弹性矩阵反映用户对电价变化的响应行为,最后,用多目标优化遗传算法(NSGA-II)进行求解,对比电动汽车无序充电与多目标优化策略下的仿真结果,仿真结果表明,本文所提出的基于分时电价的电动汽车充电负荷优化策略,不仅可以减少系统总负荷均方差,还可以降低用户的充电费用,验证了该策略的有效性与合理性。
The Uncoordinated charging behavior of electric vehicles(EVs)leads to a sharp surge in power load peaks,which exerts an adverse impact on the safe and stable operation of power grid.This paper proposes an EV charging load optimization strategy based on time-of-use pricing.First,an EV charging load model is established,and Monte Carlo simulation is used to simulate the uncoordinated charging scenario of EVs.Second,a multi-objective model is developed based on the division of peak,valley,and flat periods,aiming to minimize the mean square deviation of total system load and users'charging costs.The price elasticity matrix is introduced to reflect users’response behavior to electricity price changes.Finally,a multi-objective optimization genetic algorithm(NSGA-II)is applied for solution.By comparing simulation results of uncoordinated charging with the proposed multi-objective optimization strategy,the results demonstrate that the proposed TOU-based EV charging load optimization strategy can not only reduce the mean square deviation of total system load but also cut down users’charging costs,which verifies the effectiveness and rationality of the strategy.
作者
廖宇聪
Liao Yucong(Hunan University of Technology,Zhuzhou 412007,Hunan,China)
出处
《船电技术》
2026年第2期75-79,共5页
Marine Electric & Electronic Engineering
关键词
电动汽车
分时电价
蒙特卡洛
有序充电
electric vehicle
time-sharing tariff
Monte Carlo
orderly charging
