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.
Original language | English |
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Journal | Renewable and Sustainable Energy Reviews |
Volume | 94 |
Pages (from-to) | 197-213 |
Number of pages | 17 |
ISSN | 1364-0321 |
DOIs | |
Publication status | Published - 2018 |
Keywords
- Power systems
- Multivariate dependence
- Wind energy
- Solar energy