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Wednesday, 12 March 2014
16:30 - 18:00 Wakes: Do we need different models for onshore and offshore wind farms?
Resource Assessment  

Room: Ponent
Session description

The accurate prediction of the wake effects within and between wind farms is vital to wind farm design and to provide a prediction of the energy output. To date, the vast majority of wind farms have been designed using engineering models which have been tuned and validated using experimental data. As wind farms become larger, empirical correction upon empirical correction are being developed upon the basis of scarce and perhaps erroneous experimental data. Perhaps this is the appropriate time to question if this is the right and only approach.

In the session advanced models and observations will be described and discussed.

Learning objectives:

  • Describe what wakes are, how they can be seen in observations and how they are modelled
  • Identify different models, state-of-the-art and more classical ones
  • See wakes in observations and understand the related issues
Lead Session Chair:
Lars Landberg, DNV GL – Energy (Garrad Hassan), Denmark
Charlotte Hasager DTU Wind Energy, Denmark
Charlotte Hasager (1) F P
(1) DTU Wind Energy, Roskilde, Denmark (2) CRES, Athens, Greece (3) Indiana University, Indiana, United States of America (4) NTUA, Athens, Greece (5) CLS, Brest, France (6) CENER, Pamplona, Spain (7) ECN, Amsterdam, The Netherlands

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Dr. Charlotte Hasager received the M.Sc. in 1992 and PhD. in 1996. She has worked in research on wind energy at DTU Wind Energy (former Risø DTU) since 1993 with wind energy, boundary layer meterology and satellite remote sensing. Much focus on offshore wind energy, wind farms and wakes.


EERA DTOC wake results offshore


In the FP7 project European Research Alliance - Design Tool for Offshore wind farm Cluster (EERA DTOC) one of the key project aims was to investigate a wide variety of wake models for application in the offshore environment. A team with members from relevant EERA partners together with some industry partners has addressed this challenge.


The results of one and a half years joint work has given comparison results from two large offshore wind farms using several wake models and comparing to wind farm production data. The aim of the EERA DTOC project is to develop ‘A robust, efficient, easy to use and flexible tool created to facilitate the optimised design of individual and clusters of offshore wind farms’. It is based on previous knowledge on wind farm wake modeling among the partners.

Main body of abstract

So the accurate prediction of the wake effects within and between wind farms is important in the project. This knowledge is needed in order to make optimal wind farm lay out designs and to provide a prediction of the energy output. In the EERA DTOC project we investigate wake modeling with engineering wake models. We also investigate wake with several other types of wake models such as linearized Computational Fluid Dynamic (CFD) models (e.g. Fuga), CFD models, coupled mesoscale-micro-scale wake modeling (e.g. WRF and WAsP) and very high resolution wake modeling (Skiron). The results from the various models are compared to wind turbine production observations at two different large offshore wind farms, Horns Rev 1 in the North Sea and Lillgrund in the Baltic Sea. Furthermore, we use observations from high-resolution satellite remote sensing to capture the far field wind farm wake. This analysis has shown wakes as long as 50 km on occasions in the North Sea.


The presentation highlights the unique new insight in respect to wake modeling for offshore wind farm clusters based on many types of wake model results compared to wind farm production data. Also the new far field wind farm wake observations using satellite data are state-of-the-art and these results are of importance for further advancement of understanding the wake influence and effect at the wind farm cluster scale.

Learning objectives
Learning objectives include knowledge on wind farm wake within and between wind farm clusters offshore from a broad variety of types of wake models and new insight to satellite remote sensing for far field wind farm wake observation.