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.
Highlights
This study focuses on the wind power system of Alberta, Canada
Gneiting et al [5] describe the first use of a second order spatio-temporal process to model the wind speed, incorporating a non-separable covariance function to capture the effect of prevalent wind directions in wind speed
We find that with the new wind farms the total power generation will increase by 40.8%, whereas the variability as measured by the standard deviation, of the aggregate generation will increase by 31.5%
Summary
This study focuses on the wind power system of Alberta, Canada. Wind plays an increasingly important role in the energy system of Alberta. The wind energy capacity in Alberta is 1479 MW, ranking third in Canada, but the government expects to at least triple the current wind power capacity by 2030 [1] This motivates us to find an efficient model to forecast the future performance of the current wind energy grid and the potential generation of new wind farms in Alberta. We apply the covariance function models used in [5] to a new data set, namely the wind power generation data of Alberta, Canada. We enhance their methodology by obtaining kriging prediction intervals, in addition to kriging point predictions. We derive the distribution of the future aggregate power production using the planned site and capacity information of the future wind farms in Alberta. A new farm that reduces the variability of aggregate wind power may well be financially attractive even if its capacity factor is relatively low
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