Advanced probabilistic load flow technique in consideration of non-Gaussianity and nodal correlation of input variables

Hiroyuki Mori, Wenjung Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper proposes a new probabilistic load flow (PLF) method that considers the non-Gaussianity and the correlation of nodal input variables. The load flow calculation plays an important role to evaluate power system conditions. In recent years, power systems become more complicated due to renewable energy such as wind power generators and PV systems. As a result, the impotence of PLF has been reevaluated as one of the promising techniques for handling the network uncertainties. This paper focuses on the applications of Monte Carlo Simulation (MCS) to PLF so that the effect of the correlations of input variables and the network uncertainties is evaluated through the nonlinear equation. In this paper, new techniques are introduced into MCS-PLF to evaluate more accurate solutions. The maximum likelihood estimation for a probability density function (PDF) of input variables is carried out to construct the non-Gaussian distribution model of input variables with the correlation by the Deterministic Annealing Expectation Maximization (DAEM) algorithm. Also, the Metropolis-Hastings sampling is used to generate random numbers that correspond to complicated multivariate probability density functions. The proposed method is successively applied to a system with wind power generators.

Original languageEnglish
Title of host publicationProceedings of the 18th IFAC World Congress
PublisherIFAC Secretariat
Pages1665-1671
Number of pages7
Edition1 PART 1
ISBN (Print)9783902661937
DOIs
Publication statusPublished - 1 Jan 2011

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume44
ISSN (Print)1474-6670

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Mori, H., & Jiang, W. (2011). Advanced probabilistic load flow technique in consideration of non-Gaussianity and nodal correlation of input variables. In Proceedings of the 18th IFAC World Congress (1 PART 1 ed., pp. 1665-1671). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 44, No. 1 PART 1). IFAC Secretariat. https://doi.org/10.3182/20110828-6-IT-1002.02061