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首页> 外文期刊>Renewable Power Generation, IET >Numerical method for probabilistic load flow computation with multiple correlated random variables
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Numerical method for probabilistic load flow computation with multiple correlated random variables

机译：多个相关随机变量的概率潮流计算的数值方法

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摘要

Grid complexity is increasing progressively as the deepening penetration of renewable power generation and unpredictable demand, which necessitates an exhaustive assessment of system parameters in a probabilistic manner. In this study, the authors employ a dimensional reduction integral method to tackle the above problems challenged by dimensionality. Their approach transforms the multivariate raw moments into a linear sum of several one-dimensional integrals, which could be solved by Gauss quadrature. To handle the correlation between non-Gaussian input variables, Nataf transformation is used to map the inputs into the independent normal domain. Instead of commonly used series expansion such as A-type Gram-Charlier, Edgeworth or Cornish-Fisher, the probability distributions of output variables can be better approximated by C-type Gram-Charlier series with the calculated moments. The salient feature of the proposed method is demonstrated in a modified IEEE 118-bus test system with respect to both accuracy and run times.
机译：随着可再生能源发电的不断深入和不可预测的需求，电网的复杂性正在逐步增加，这需要以概率的方式对系统参数进行详尽的评估。在这项研究中，作者采用了降维积分方法来解决上述受维数挑战的问题。他们的方法将多元原始矩转换为几个一维积分的线性和，可以通过高斯求积法求解。为了处理非高斯输入变量之间的相关性，Nataf变换用于将输入映射到独立的法域。代替常用的级数展开（例如A型Gram-Charlier，Edgeworth或Cornish-Fisher），输出变量的概率分布可以通过C型Gram-Charlier级数和计算出的矩更好地近似。改进的IEEE 118总线测试系统在准确性和运行时间方面都证明了该方法的显着特征。

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