Combination of moment-matching, Cholesky and clustering methods to approximate discrete probability distribution of multiple wind farms

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 gener...

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Vydané v:	IET renewable power generation Ročník 10; číslo 9; s. 1450 - 1458
Hlavní autori:	Li, Jinghua, Zhu, Dunlin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	The Institution of Engineering and Technology 01.10.2016
Predmet:	Approximation Approximation methods approximation theory Cholesky decomposition method Cholesky methods Clustering clustering methods Computer simulation importance sampling Latin hypercube sampling Mathematical analysis Metal matrix composites moment matching moment‐matching‐clustering methods observed original scenarios original discrete probability distribution probability Research Article stochastic programs Stochasticity wind farms Wind power wind power generation wind turbines clustering methods approximation theory probability Cholesky methods Cholesky decomposition method moment-matching-clustering methods observed original scenarios importance sampling original discrete probability distribution wind farms Latin hypercube sampling stochastic programs wind power generation wind turbines moment matching
ISSN:	1752-1416, 1752-1424
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