CAJ | 학술논문

随着大量新能源接入，电力系统运行必须考虑其随机性带来的影响。概率潮流是有效工具之一。针对考虑输入变量相关性的概率潮流计算，文中采用 Spearman 秩相关系数表示输入随机变量间的相关性，分析了拉丁超立方抽样方法与秩相关系数的内在关联，提出结合遗传算法的改进拉丁超立方抽样方法进行概率潮流计算。算例结果表明，所提出的方法能较好地刻画风速间的相关性，不受输入随机变量边缘分布的影响，并且能处理秩相关系数矩阵正定和非正定的情况。
수착대량신능원접입，전력계통운행필수고필기수궤성대래적영향。개솔조류시유효공구지일。침대고필수입변량상관성적개솔조류계산，문중채용 Spearman 질상관계수표시수입수궤변량간적상관성，분석료랍정초립방추양방법여질상관계수적내재관련，제출결합유전산법적개진랍정초립방추양방법진행개솔조류계산。산례결과표명，소제출적방법능교호지각화풍속간적상관성，불수수입수궤변량변연분포적영향，병차능처리질상관계수구진정정화비정정적정황。
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.