基于实测离散数据的含风电场电力系统概率潮流计算

Abstract

随着风电接入容量的增加，利用常见的概率密度函数拟合其分布特征进行概率潮流(Probabilistic Load Flow, PLF)计算将产生较大误差，提出一种考虑离散分布输入变量的PLF计算方法，以期计算结果更符合实际情况。该方法仅需风电出力离散采样数据，结合离散变量函数统计矩点估计法和Gram-Charlier展开级数，便可估计出输出随机变量的统计特征，如期望、方差、累积分布等信息。采用IEEE 39节点系统验证了所提方法的有效性和实用性，结果表明：该方法不仅计算速度快，而且具有较高的精度，工程应用前景较好。 With the increasing penetration of wind sources, using common probability density function to fit the probability distribution to compute the probabilistic load flow (PLF) will lead to considerable error. We propose PLF calculation method considering discrete distribution of input variables to calculate more realistic results. This method only needs wind power discrete sampling data combined with point estimate method of discrete variable function statistical moments and Gram- Charlier expand series, to estimate the statistical characteristics of the output of the random variable, such as the information of expectation, variance, cumulative distribution and so on. The effectiveness and practicality of the proposed method is verified using IEEE 39-bus system, and the results show that: the calculation method is not only faster, but also has high accuracy, and it has better engineering application prospects.