Journal
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 3, Issue 1, Pages 134-141Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2011.2163324
Keywords
Autoregression moving average (ARMA) models; power system impact; regression; wind energy; wind forecasting
Categories
Funding
- U.K. Department of Energy and Climate Change [K/EL/00332/02/00]
- EPSRC [EP/G013616/1]
- TWENTIES
- European Union [249812]
- Engineering and Physical Sciences Research Council [EP/G013616/1] Funding Source: researchfish
Ask authors/readers for more resources
Scientific research to characterize the long-term wind energy resource is plentiful. However, if the impact of wind power on the electric power system is the goal of modeling, consideration must be given to diurnal and seasonal effects, as well as the correlation of wind speed between geographical areas. This paper provides such detail bymodeling these effects explicitly, enabling accurate evaluations of wind power impact on future power systems to be carried out. This is increasingly important in the context of ambitious wind energy targets driven in the U.K., for example, by the requirement for 20% of Europe's energy to bemet from renewable energy sources by 2020. Both univariate and multivariate auto-regressive models are presented here and it is shown how they can be applied to geographically dispersed wind speed data. These models are applied to suitably de-trended data. The accuracy of the models is assessed both by inspection of the residuals and by assessment of the forecasting accuracy of the models. Finally, it is shown how the models can be used to synthesize wind speed and thus wind power time series with the correct seasonal, diurnal, and spatial diversity characteristics.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available