High dimensional dependence in power systems: A review

Edgar Nuño Martinez, Nicolaos Cutululis and Poul Sørensen

Renewable and Sustainable Energy Reviews, 2018, vol. 94, issue C, 197-213

Abstract: Weather-driven renewable generation is characterized by being uncertain and geographically dependent. In this regard, the recent deployment of wind and solar power has had a significant impact on the operation and planning of modern electricity grids; justifying the need to model high dimensional dependence. It is a relevant topic which is starting to have a significant importance in power systems. This paper presents a general overview on different multivariate dependence modeling techniques, namely parametric, non-parametric and copula functions. In addition, approximated methods based on limited information e.g. some statistical measures or a predefined dependence structure are presented. Autoregressive moving average (ARMA) and Markov models are discussed as general frameworks to reproduce spatio-temporal processes. Moreover, different applications in power systems are discussed in detail, along with a case study exemplifying the importance of a correct dependence modeling of wind generation.

Keywords: Power systems; Multivariate dependence; Wind energy; Solar energy (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:rensus:v:94:y:2018:i:c:p:197-213

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DOI: 10.1016/j.rser.2018.05.056

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