An application of probabilistic collocation method in wind farms modelling and power system simulation

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

5 Citations (Scopus)

Abstract

In this paper Probabilistic Collocation Method (PCM) is introduced to solve a stochastic model representing wind farms in South Australia (SA). The model is based upon historical acquisition of wind source data, and considering the spatial correlation of wind speeds at neighboring wind farms. This correlation is used to reduce the number of uncertain parameters of the model, and then reducing the cost of PCM computation. In addition, fuzzy logic optimization is applied to PCM to improve the accuracy of the model output. The paper concludes with presentation of an aggregated DC load flow model of SA that is used as an example to compare the computation efficiency of the PCM and traditional Monte Carlo (MC) simulation method.

Original languageEnglish
Title of host publication2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
PublisherIEEE Computer Society
Pages681-686
Number of pages6
ISBN (Electronic)9781509043033
DOIs
Publication statusPublished - 22 Dec 2016
Externally publishedYes
Event2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016 - Melbourne, Australia
Duration: 28 Nov 20161 Dec 2016

Publication series

NameIEEE PES Innovative Smart Grid Technologies Conference Europe

Conference

Conference2016 IEEE Innovative Smart Grid Technologies - Asia, ISGT-Asia 2016
CountryAustralia
CityMelbourne
Period28.11.201601.12.2016

Keywords

  • fuzzy logic
  • power system simulation
  • Probabilistic Collocation Method
  • uncertainty
  • Wind power generation

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