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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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

Published: 03 March 2016

PACS:	TM743
	U469.72+1

Fund:国家自然科学基金（51477017）,重庆市基础与前沿研究计划一般项目（cstc2014jcyjA90017）和国家电网公司科技项目（SGCQDK00DJJS1500056）资助

Corresponding Authors: 余娟女,1980年生,博士,副教授,研究方向为无功优化、风险评估、电压稳定与电力系统静态等值。E-mail: cqu.juan.yu@gmail.com

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	Miao Pengbin
	Yu Juan
	Shi Lefeng
	Liu Guoping
	Liang Ming
	Li Wenyuan
	Ren Zhouyang

Cite this article:

Miao Pengbin,Yu Juan,Shi Lefeng等. Electric Public Bus Load Model Based on Improved Kernel Density Estimation and Latin Hypercube Sampling[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 187-193.

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

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