Wind Energy | Vol.19, Issue.5 | | Pages 996-979
An evaluation of the predictive accuracy of wake effects models for offshore wind farms
Wake losses are perceived as one of the largest uncertainties in energy production estimates (EPEs) for new offshore wind projects. In recent years, significant effort has been invested to improve the accuracy of wake models. However, it is still common for a standard wake loss uncertainty of 50% to be assumed in EPEs for new offshore wind farms. This paper presents a body of evidence to support reducing that assumed uncertainty. It benchmarks the performance of four commonly used wake models against production data from five offshore wind farms. Three levels of evidence are presented to substantiate the performance of the models: Case studies, i.e. efficiencies of specific turbines under specific wind conditions; Array efficiencies for the wind farm as a whole for relatively large bins of wind speed and direction; and Validation wake loss, which corresponds to the overall wake loss within the proportion of the annual energy production where validation is possible. The most important result for predicting annual energy production is the validation wake loss. The other levels of evidence demonstrate that this result is not unduly reliant on cancellation of errors between wind speed and/or wind direction bins. All of the root-mean-squared errors in validation wake loss are substantially lower than the 50% uncertainty commonly assumed in EPEs; indeed, even the maximum errors are below 25%. It is therefore concluded that there is a good body of evidence to support reducing this assumed uncertainty substantially, to a proposed level of 25%. Copyright © 2015 John Wiley & Sons, Ltd.
Original Text (This is the original text for your reference.)
An evaluation of the predictive accuracy of wake effects models for offshore wind farms
Wake losses are perceived as one of the largest uncertainties in energy production estimates (EPEs) for new offshore wind projects. In recent years, significant effort has been invested to improve the accuracy of wake models. However, it is still common for a standard wake loss uncertainty of 50% to be assumed in EPEs for new offshore wind farms. This paper presents a body of evidence to support reducing that assumed uncertainty. It benchmarks the performance of four commonly used wake models against production data from five offshore wind farms. Three levels of evidence are presented to substantiate the performance of the models: Case studies, i.e. efficiencies of specific turbines under specific wind conditions; Array efficiencies for the wind farm as a whole for relatively large bins of wind speed and direction; and Validation wake loss, which corresponds to the overall wake loss within the proportion of the annual energy production where validation is possible. The most important result for predicting annual energy production is the validation wake loss. The other levels of evidence demonstrate that this result is not unduly reliant on cancellation of errors between wind speed and/or wind direction bins. All of the root-mean-squared errors in validation wake loss are substantially lower than the 50% uncertainty commonly assumed in EPEs; indeed, even the maximum errors are below 25%. It is therefore concluded that there is a good body of evidence to support reducing this assumed uncertainty substantially, to a proposed level of 25%. Copyright © 2015 John Wiley & Sons, Ltd.
+More
epes case losses predicting annual energy production cancellation of errors wind direction validation wake loss array ie efficiencies
APA
MLA
Chicago
Keith Walker, Miriam Marchante Jimémez, Robert Cussons, Brian Gribben, Oliver Peronne, Paul Housley, Andrew Henderson, Niall Connell, Sarah Ruth Schmidt, Andreas Knauer, Miguel Cordoba, Breanne Gellatly, Javier Rodriguez Ruiz, Måns Håkansson, Daniel Paredes, Gemma Harrington, Eoghan Maguire, Nicolai Gayle Nygaard, Neil Adams,.An evaluation of the predictive accuracy of wake effects models for offshore wind farms. 19 (5),996-979.
Select your report category*
Reason*
New sign-in location:
Last sign-in location:
Last sign-in date: