Probabilistic Load Flow Method Based on Modified Latin Hypercube-Important Sampling

Quan Li, Xin Wang and Shuaiang Rong
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Quan Li: Huanggang Power Supply Company, Hubei Electric Power Co., Ltd., State Grid Corporation of China, Huanggang 438000, China
Xin Wang: Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China
Shuaiang Rong: College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200082, China

Energies, 2018, vol. 11, issue 11, 1-14

Abstract: The growing amount of distributed generation has brought great uncertainty to power grids. Traditional probabilistic load flow (PLF) algorithms, such as the Monte-Carlo method (MCM), can no longer meet the needs of efficiency and accuracy in large-scale power grids. Latin Hypercube Sampling (LHS) develops a sampling efficiency and solves the correlation problem of distributed generation (DG) access nodes for accuracy analyses. In this paper, a modified Latin Hypercube-Important Sampling method is proposed for higher efficiency and precision by using the importance sampling method before LHS and the Cholesky decomposition method in correlation calculations. The simulation results are presented using a modified IEEE 30-bus system and are compared with traditional MCM and LHS.

Keywords: probabilistic load flow; important sampling; non-parametric estimation construction; Latin hypercube sampling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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