基于改进非参数核密度估计和拉丁超立方抽样的电动公共客车负荷模型

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摘要为了建立更精确的电动公共客车充电负荷模型,对起始荷电状态和起始充电时间的概率分布和抽样方法进行研究。首先针对概率分布,提出采用边界核的自适应非参数核密度估计算法,该方法无需概率分布的任何假设,能有效解决边界偏差和缺乏局部适应性的问题,从而提高了起始荷电状态和起始充电时间概率分布的准确度和适应性。其次针对抽样环节,提出结合三次样条插值法的改进拉丁超立方抽样算法,该方法不仅可以解决累积分布函数的反函数较难求解的问题,而且具有准确度高、抽样规模小等优点。最后,以这两种算法为基础,建立电动公共客车充电负荷模型,并将其与实测数据和传统参数估计方法所得结果进行对比。仿真结果证明了所提方法的适应性和有效性。

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	缪鹏彬
	余娟
	史乐峰
	刘国平
	梁明
	李文沅
	任洲洋

关键词 ：电动公共客车, 充电负荷, 自适应非参数核密度估计算法, 边界核, 拉丁超立方抽样

Abstract：To build a more precise charging load model of electric public buses, the probability distribution and sampling method of charging state and charging start time are studied. Firstly, an adaptive kernel density estimation with boundary Kernel algorithm is proposed to build probability distribution models. The proposed algorithm does not require any assumptions about the probability distribution, and can also solve the problems of boundary bias and lacking of local adaptability, which improves the precision and adaptability of probability distribution. Secondly, Latin hypercube sampling with cubic spline interpolation algorithm is proposed to figure out the inverse cumulative distribution function of probability distribution. The proposed algorithm has high precision and sampling efficiency. Finally, based on these two algorithms, electric public bus charging load model is set up. The simulation result demonstrates the effectiveness and adaptability of the proposed method.

Key words： Electric public bus charging load adaptive Kernel density estimation boundary Kernel Latin hypercube sampling

出版日期: 2016-03-03

PACS:	TM743
	U469.72+1

作者简介: 缪鹏彬男,1991年生,硕士研究生,研究方向为电动汽车与电网安全稳定分析。E-mail: miaopengbin@qq.com

引用本文:

缪鹏彬, 余娟, 史乐峰, 刘国平, 梁明, 李文沅, 任洲洋. 基于改进非参数核密度估计和拉丁超立方抽样的电动公共客车负荷模型[J]. 电工技术学报, 2016, 31(4): 187-193. Miao Pengbin, Yu Juan, Shi Lefeng, Liu Guoping, Liang Ming, Li Wenyuan, Ren Zhouyang. Electric Public Bus Load Model Based on Improved Kernel Density Estimation and Latin Hypercube Sampling. Transactions of China Electrotechnical Society, 2016, 31(4): 187-193.

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https://dgjsxb.ces-transaction.com/CN/Y2016/V31/I4/187

[1] 葛文捷, 黄梅, 张维戈. 电动汽车充电站经济运行分析[J]. 电工技术学报, 2013, 28(2): 15-21.
Ge Wenjie, Huang Mei, Zhang Weige. Economic operation analysis of the electric vehicle charging station[J]. Transactions of China Electrotechnical Society, 2013, 28(2): 15-21.
[2] 肖湘宁, 温剑锋, 陶顺,等. 电动汽车充电基础设施规划中若干关键问题的研究与建议[J]. 电工技术学报, 2014, 29(8): 1-10.
Xiao Xiangning, Wen Jianfeng, Tao Shun, et al. Study and recommendations of the key issues in planning of electric vehicles’ charging facilities[J]. Transactions of China Electrotechnical Society, 2014, 29(8): 1-10.
[3] 罗卓伟, 胡泽春, 宋永华, 等. 电动汽车充电负荷计算方法[J]. 电力系统自动化, 2011, 35(14): 36-42.
Luo Zhuowei, Hu Zechun, Song Yonghua,et al. Study on plug-in electric vehicles charging load calculating[J]. Automation of Electric Power Systems, 2011, 35(14): 36-42.
[4] 杨冰, 王丽芳, 廖承林. 大规模电动汽车充电需求及影响因素[J]. 电工技术学报, 2013, 28(2): 22-27.
Yang Bing, Wang Lifang, Liao Chenglin. Research on power-charging demand of large-scale electric vehicles and its impacting factors[J]. Transactions of China Electrotechnical Society, 2013, 28(2): 22-27.
[5] Pashajavid E, Golkar M A. Charging of plug-in electric vehicles: Stochastic modelling of load demand within domestic grids[C]//2012 20th Iranian Con- ference on Electrical Engineering, 2012: 535-539.
[6] 徐国钧, 刘永胜, 李题印, 等. 基于层次分析和概率模拟的电动汽车对配网负荷影响研究[J]. 电力系统保护与控制, 2012, 40(22): 38-45.
Xu Guojun, Liu Yongsheng, Li Tiyin, et al. Study on the impact of electric vehicles charging load integ- rated into distribution network based on analytic hierarchy process and probabilistic simulation[J]. Power System Protection and Control, 2012, 40(22): 38-45.
[7] Leou Rong Ceng, Su Chun Lien, Lu Chan Nan. Stochastic analyses of electric vehicle charging impacts on distribution network[J]. IEEE Transa- ctions on Power Systems, 2014, 29(3): 1055-1063.
[8] Dai Qian, Cai Tao, Duan Shanxu, et al. Stochastic modeling and forecasting of load demand for electric bus battery-swap station[J]. IEEE Transactions on Power Delivery, 2014, 29(4): 1909-1917.
[9] Jeffery S Simonoff. Smoothing methods in stati- stics[M]. New York: Springer-Verlag, 1996.
[10] Silverman B W. Density estimation for statistics and data analysis[M]. Boca Raton: CRC Press, 1986.
[11] 赵渊, 张夏菲, 周家启. 电网可靠性评估的非参数多变量核密度估计负荷模型研究[J]. 中国电机工程学报, 2009, 29(31): 27-33.
Zhao Yuan, Zhang Xiafei, Zhou Jiaqi. Load modeling utilizing nonparametric and multivariate Kernel density estimation in bulk power system reliability evaluation[J]. Proceedings of the CSEE, 2009, 29(31): 27-33.
[12] Mckay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code[J]. Technimetrics, 1979, 21(2): 239- 245.
[13] Young T, Mohlenkamp M J. Introduction to numerical methods and Matlab programming for engineers[M]. Athens: Ohio University, 2012.
[14] 张德丰, 等. MATALAB 数值计算方法[M]. 北京: 机械工业出版社, 2010.
[15] Qin Zhilong, Li Wenyuan, Xiong Xiaofu. Estimating wind speed probability distribution using Kernel density method[J]. Electric Power Systems Research, 2011, 81(12): 2139-2146.