电力系统自动化Issue(12)：54-61,8.DOI:10.7500/AEPS20130429008
基于输入变量秩相关系数的概率潮流计算方法
Probabilistic Load Flow Calculation Based on Rank Correlation Coefficient of Input Random Variables
摘要
Abstract
With numerous new energy resources integrated into the power system,the influences brought about by random variables have to be properly considered in the operation of power systems.Probabilistic load flow is one of the effective tools. In this paper,the method for probabilistic load flow considering the dependence among variables is studied.The Spearman rank correlation coefficient is used to model the dependence among variables,and the inherent relation between Latin hypercube sampling and rank correlation coefficient is analyzed.Latin hypercube sampling combined with genetic algorithm is proposed to solve probabilistic load flow.Simulation results show that the method has a better performance than others in describing the dependence between wind speeds,and is not influenced by different marginal distributions.Moreover,it can handle positive and non-positive rank correlation coefficient matrices.关键词
概率潮流/风力发电/Spearman秩相关系数/拉丁超立方抽样/概率分布/遗传算法/潮流计算Key words
probabilistic load flow/wind power generation/Spearman rank correlation coefficient/Latin hypercube sampling/probability distribution/genetic algorithm/power flow calculation引用本文复制引用
徐潇源,严正,冯冬涵,王毅,曹路..基于输入变量秩相关系数的概率潮流计算方法[J].电力系统自动化,2014,(12):54-61,8.基金项目
国家电网公司大电网重大专项资助项目(SGCC-MPLG018-2012)。This work is supported by State Grid Corporation of China,Major Projects on Planning and Operation Control of Large Scale Grid(No.SGCC-MPLG018-2012) （SGCC-MPLG018-2012）
