Probabilistic load flow calculation based on sparse polynomial chaos expansion

Author(s): Xin Sun¹ ; Qingrui Tu² ; Jinfu Chen¹ ; Chengwen Zhang¹ ; Xianzhong Duan¹
DOI: 10.1049/iet-gtd.2017.0859

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Author(s): Xin Sun¹ ; Qingrui Tu² ; Jinfu Chen¹ ; Chengwen Zhang¹ ; Xianzhong Duan¹
- Affiliations: 1: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology , Wuhan , People's Republic of China ;
  2: Electric Power Dispatching and Control Centre of Guangdong Power Grid , Guangzhou , People's Republic of China
Source: Volume 12, Issue 11, 19 June 2018, p. 2735 – 2744
DOI: 10.1049/iet-gtd.2017.0859 , Print ISSN 1751-8687, Online ISSN 1751-8695

A probabilistic load flow (PLF) method based on the sparse polynomial chaos expansion (PCE) is presented here. Previous studies have shown that the generalised polynomial chaos expansion (gPCE) is promising for estimating the probability statistics and distributions of load flow outputs. However, it suffers the problem of curse-of-dimensionality in high-dimensional applications. Here, the compressive sensing technique is applied into the gPCE-based scheme, from which the sparse PCE is built as the surrogate model to perform the PLF in an accurate and efficient manner. The dependence among random input variables is also taken into consideration by making use of the Copula theory. Consequently, the proposed method is able to handle the correlated uncertainties of high-dimensionality and alleviate the computational effort as of popular methods. Finally, the feasibility and the effectiveness of the proposed method are validated by the case studies of two standard test systems.

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Probabilistic load flow calculation based on sparse polynomial chaos expansion

Probabilistic load flow calculation based on sparse polynomial chaos expansion

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