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Research on ISFLA-Based Optimal Control Strategy for the Coordinated Charging of EV Battery Swap Station

机译:基于ISFLA的电动汽车交换站协同充电最优控制策略研究

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摘要

As an important component of the smart grid, electric vehicles (EVs) could be a good measure against energy shortages and environmental pollution. A main way of energy supply to EVs is to swap battery from the swap station. Based on the characteristics of EV battery swap station, the coordinated charging optimal control strategy is investigated to smooth the load fluctuation. Shuffled frog leaping algorithm (SFLA) is an optimization method inspired by the memetic evolution of a group of frogs when seeking food. An improved shuffled frog leaping algorithm (ISFLA) with the reflecting method to deal with the boundary constraint is proposed to obtain the solution of the optimal control strategy for coordinated charging. Based on the daily load of a certain area, the numerical simulations including the comparison of PSO and ISFLA are carried out and the results show that the presented ISFLA can effectively lower the peak-valley difference and smooth the load profile with the faster convergence rate and higher convergence precision.
机译:作为智能电网的重要组成部分,电动汽车(EV)可以很好地应对能源短缺和环境污染。电动汽车的主要能源供应方式是从交换站交换电池。根据电动汽车换乘站的特点,研究了协调充电最优控制策略,以减轻负载波动。改组蛙跳算法(SFLA)是一种优化方法,其灵感来自寻找食物时一组青蛙的模因进化。提出了一种改进的带反射蛙跳蛙跳算法(ISFLA),采用反射法来处理边界约束,以获得协调充电最优控制策略的解决方案。基于某区域的日负荷,进行了PSO和ISFLA比较的数值模拟,结果表明,所提出的ISFLA可以有效地减小峰谷差,并以更快的收敛速度和更快的速度使负荷曲线平滑。更高的收敛精度。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|613404.1-613404.7|共7页
  • 作者单位

    School of Electrical Engineering, Southeast University, Nanjing 210096, China,Jiangsu Key Lab of Smart Grid Technology and Equipment, Zhenjiang 212009, China;

    School of Electrical Engineering, Southeast University, Nanjing 210096, China,School of Information Science and Engineering, Changzhou University, Changzhou 213164, China;

    School of Electrical Engineering, Southeast University, Nanjing 210096, China;

    School of Electrical Engineering, Southeast University, Nanjing 210096, China;

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