Characterizing future large, rapid changes in aggregated wind power using Numerical Weather Prediction spatial fields
Article first published online: 9 DEC 2008
Copyright © 2008 John Wiley & Sons, Ltd.
Volume 12, Issue 6, pages 542–555, September 2009
How to Cite
Cutler, N. J., Outhred, H. R., MacGill, I. F., Kay, M. J. and Kepert, J. D. (2009), Characterizing future large, rapid changes in aggregated wind power using Numerical Weather Prediction spatial fields. Wind Energ., 12: 542–555. doi: 10.1002/we.312
- Issue published online: 15 SEP 2009
- Article first published online: 9 DEC 2008
- Manuscript Accepted: 27 OCT 2008
- Manuscript Revised: 23 OCT 2008
- Manuscript Received: 10 JUL 2008
- Large, rapid changes;
- Wind speed
A critical limiting factor to the successful deployment of a large proportion of wind power in power systems is its predictability. Power system operators play a vital role in maintaining system security, and this task is greatly aided by useful characterizations of future system operations. A wind farm power forecast generally relies on the forecast output from a Numerical Weather Prediction (NWP) model, typically at a single grid point in the model to represent the wind farm's physical location. A key limitation of this approach is the spatial misplacement of weather features often found in NWP forecasts. This paper presents a methodology to display wind forecast information from multiple grid points at hub height around the wind farm location. If the raw forecast wind speeds at hub height at multiple grid points were to be displayed directly, they would be misleading as the NWP outputs take account of the estimated local surface roughness and terrain at each grid point. Hence, the methodology includes a transformation of the wind speed at each grid point to an equivalent value that represents the surface roughness and terrain at the chosen single grid point for the wind farm site. The chosen-grid-point-equivalent wind speeds for the wind farm can then be transformed to available wind farm power. The result is a visually-based decision support tool which can help the forecast user to assess the possibilities of large, rapid changes in available wind power from wind farms. A number of methods for displaying the field for multiple wind farms are discussed. The chosen-grid-point-equivalent transformation also has other potential applications in wind power forecasting such as assessing deterministic forecast uncertainty and improving downscaling results. Copyright © 2008 John Wiley & Sons, Ltd.