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Correlative coherence modelling of South Australian wind farms

Author

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  • Ward, K.
  • Korolkiewicz, M.
  • Boland, J.

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.

Suggested Citation

  • Ward, K. & Korolkiewicz, M. & Boland, J., 2013. "Correlative coherence modelling of South Australian wind farms," Renewable Energy, Elsevier, vol. 52(C), pages 154-159.
    • Handle: RePEc:eee:renene:v:52:y:2013:i:c:p:154-159
    DOI: 10.1016/j.renene.2012.09.036
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    References listed on IDEAS

    as
    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Magnano, L. & Boland, J.W., 2007. "Generation of synthetic sequences of electricity demand: Application in South Australia," Energy, Elsevier, vol. 32(11), pages 2230-2243.
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