EconPapers    
Economics at your fingertips  
 

Correlative coherence modelling of South Australian wind farms

K. Ward, M. Korolkiewicz and J. Boland

Renewable Energy, 2013, vol. 52, issue C, 154-159

Abstract: In this paper, a new method is presented for quantifying dependence in output among wind farms connected to an electricity grid. Overall stability of a grid is an important consideration. If output of wind farms is highly correlated, serious stress can be placed on the local infrastructure and as such it needs to be carefully managed. The correlative coherence analysis (CCA), first introduced by Getz [4] to capture variability of competing biological populations, is adapted to study the degree of movement in unison of wind farms. This method is applied to analyse stability of a system consisting of electricity output of five wind farms situated in South Australia. The results of this analysis are verified using principal component analysis. Distributional aspects of a correlative coherence measure on which CCA is based are discussed as well.

Keywords: Correlative coherence; Wind farms; Clustering; Principal component analysis (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148112006076
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:52:y:2013:i:c:p:154-159

DOI: 10.1016/j.renene.2012.09.036

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:renene:v:52:y:2013:i:c:p:154-159