基于Copula函数及Rosenblatt变换的含相关性概率潮流计算

随着风电渗透率的增加,在考虑风电出力随机性和间歇性对配电网影响的同时,风电场间风速的相关性应予以考虑。将Rosenblatt变换与Copula函数相结合以处理风电场间复杂的非线性关系,提出一种基于Copula函数及Rosenblatt变换的概率潮流半不变量算法。所提方法能准确捕捉到输入变量间的非线性相关关系,且具备半不变量法计算效率高的优点。以IEEE33节点网络接入风电场对所提方法进行测试。结果证明了所提方法的有效性、准确性和实用性,所提方法计算结果较考虑线性相关性算法的计算结果更接近实际运行情况。

Probabilistic load flow calculation based on Copula function and Rosenblatt transformation considering correlation among input variables

With the increase of wind power penetration, the correlation of wind speed among wind farms should be considered while considering the randomness and intermittency of wind power. In this paper, Rosenblatt transformation and Copula function are combined to deal with the complex nonlinear relationship among wind farms, and a probabilistic load flow cumulant method based on Copula function and Rosenblatt transformation is proposed. The method can capture the nonlinear correlation among input variables accurately and has the advantage of high efficiency of cumulant method. The IEEE33-bus system including wind farms is used for testing the proposed method. The results verify the validity, accuracy and practicability of the proposed method, and the calculation results of the proposed method are closer to the actual operating condition than that of the method which considers linear correlation. This work is supported by National Natural Science Foundation of China (No.51467012).