Evaluation of a wind farm parameterization in an operational mesoscale model
Pooja Ramakrishnan (TU Delft - Civil Engineering & Geosciences)
AP Siebesma – Mentor (TU Delft - Atmospheric Remote Sensing)
Sukanta Basu – Graduation committee member (TU Delft - Atmospheric Remote Sensing)
Carlos Simao Ferreira – Coach (TU Delft - Wind Energy)
Remco Verzijlbergh – Coach (Whiffle)
Bart van Stratum – Coach (Royal Netherlands Meteorological Institute (KNMI))
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Abstract
In the coming decade, the Dutch government plans to broadly expand its wind farm real estate over the North Sea region. This is a significant undertaking because, from literature, it is understood that wind turbines can affect the local environment. These impacts include, but are not limited to, changes in variables such as wind speed and turbulent kinetic energy. To accurately capture these effects and improve power forecast accuracy, weather prediction models numerically represent the physical effects of a wind farm (i.e., a wind farm's behaviour as a momentum sink and a source of turbulent kinetic energy) by incorporating a wind farm parameterization in the model. This study looked at two simulations (with and without a wind farm parameterization) from HARMONIE-AROME, an operational weather model, and assessed the performance of the wind farm parameterization by using the simulation results for the Belgian wind farm zone in the North Sea, and comparing it with observational data from Sentinel-1 SAR, floating LiDARs, measured power as well as results from a large-eddy simulation. The data was also composited based on wind direction and atmospheric stability and analysed. Most importantly, it was found, after comprehensive analysis, that the wind farm parameterization caused a marked improvement in the results of the model especially in the region downwind of a wind farm where the effects are most severely experienced. Specifically, the simulation with the wind farm parameterization reduced the overall wind bias to -0.028 m/s from 0.602 m/s when compared with observational data for the selected region. The overall power bias was also found to be approximately 1.92\%. Several hypotheses based on existing literature were also tested, and along with the composite analysis, indicated that the simulation with the wind farm parameterization performed well in most stability regimes with the exception of stable conditions. It was recommended that this be further explored to determine the cause. Recommendations for expanding the study to look at wind farm effects on surface fluxes in offshore regions have also been made.