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首页> 外文期刊>Renewable Power Generation, IET >Combination of moment-matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms
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Combination of moment-matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms

机译：矩匹配，Cholesky和聚类方法相结合来近似估计多个风电场的离散概率分布

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摘要

This study focuses on approximating a reduced discrete probability distribution (RDPD) of wind power from the original discrete probability distribution (ODPD), consisting of a large number of observed original scenarios (OSs), to relieve the burden of solving stochastic programs of wind power generation. The proposed method, namely, the MMCC method, aims to achieve high approximation accuracy and computational efficiency by combining an improved moment-matching (MM) method with the clustering (C) method and the Cholesky decomposition (CD) method. First, the C method is used to reduce the number of OSs by minimising the space distance between the reduced scenarios (RSs) and the OSs. Next, the CD method is used to rectify the correlation of the RSs to satisfy that of the ODPD. Finally, the RS probabilities are optimally determined by the MM method in order to minimise the stochastic features (first four moments and correlation matrix) between the RDPD and the ODPD. Simulations of RDPD approximation for three wind farms with 10, 20, 40, 60, 80, and 100 scenarios were carried out using the Latin hypercube sampling, importance sampling, C, moment-matching-clustering (MMC), and MMCC methods. The results showed that the MMCC method exhibits the best performance in terms of capturing the features of the ODPD.
机译：这项研究的重点是从包括大量观测到的原始情景（OS）的原始离散概率分布（ODPD）近似降低风电的离散概率分布（RDPD），以减轻解决风电随机程序的负担代。提出的方法，即MMCC方法，旨在通过将改进的矩匹配（MM）方法与聚类（C）方法和Cholesky分解（CD）方法相结合来实现较高的逼近精度和计算效率。首先，使用C方法通过最小化简化方案（RS）和OS之间的空间距离来减少OS的数量。接下来，CD方法用于校正RS的相关性以满足ODPD的相关性。最后，通过MM方法最优地确定RS概率，以最小化RDPD和ODPD之间的随机特征（前四个矩和相关矩阵）。使用拉丁文超立方体采样，重要性采样，C，矩量匹配聚类（MMC）和MMCC方法，对具有10、20、40、60、80和100个场景的三个风电场的RDPD逼近进行了模拟。结果表明，MMCC方法在捕获ODPD的特征方面表现出最好的性能。

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