A probabilistic load flow calculation method with latin hypercube sampling

Han Yu, Chiyong Chung, Kitpo Wong, Jianhua Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

112 Citations (Scopus)

Abstract

Monte Carlo simulation method combined with simple random sampling is easy to use; and higher accuracy can be obtained only with a large enough sample size. Consequently, in order to achieve lowers errors, the computational speed will have to be reduced and the computational costs remain very high. An effective sampling method, Latin hypercube sampling, is integrated into the probabilistic load flow calculation to increase the sampling efficiency of Monte Carlo simulation by improving the sample values coverage of random variables input spaces. The effectiveness and efficiency of the proposed method is proven by the comparative tests in the IEEE 14-bus system and IEEE 118-bus system. Compared with simple random sampling, it needs a much smaller sampling number to get a specified accuracy, and can effectively evaluate the statistical parameters and probability distribution of output stochastic variables. At the same time, the advantages of Monte Carlo simulation are preserved, and computational cost is dramatically reduced when dealing the stochastic problems of the same stochastic variables.

Original languageEnglish
Pages (from-to)32-35+81
JournalDianli Xitong Zidonghua/Automation of Electric Power Systems
Volume33
Issue number21
Publication statusPublished - 10 Nov 2009

Keywords

  • Latin hypercube sampling
  • Monte Carlo simulation
  • Probabilistic load flow calculation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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