Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

Abstract

This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

Wind power scenario generation with non-separable spatio-temporal covariance function and fluctuation-based clustering

“…Since positive definiteness is a necessary and sufficient condition for valid covariance functions, it can be difficult to build new classes of valid non-separable covariance functions preserving this property. An effective strategy is to adopt the parametric family known to be positive definite [33,34]. The non-separable spatio-temporal covariance function proposed in [23], which can directly construct the covariance function in spatio-temporal domain, is adopted to characterize the spatio-temporal interactions of wind power output.…”