Two post processing methods for quantifying the shadow effect of the offshore wind farm Princes Amalia (PA) onto Egmond aan Zee (OWEZ) wind farm are analyzed and benchmarked. The first method is the author’s proposed shadow effect determination method (SEDM), which quantifies an offshore wind farm’s shadow effect based on mesoscale WRF (Weather Research Forecast) idealized modeling and the observed frequency of the analyzed site’s wind conditions. The Fitch turbine parametrization scheme and Mellor-Yamada-Nakanishi-Niino (MYNN) surface layer and planetary boundary layer (PBL) schemes were used to simulate the wind farm’s interactions, based on site conditions. The proposed physical downscaling method (SEDM) uses filtered simulated atmospheric stability and wind speed conditions, in order to calculate the percent wind speed deficit downstream of PA, with regard, first, to observed wind speed frequency and, secondly, to wind speed and corresponding atmospheric stability regimes. Then a statistical downscaling method, based on the established Analog Ensemble (AnEn) technique, developed by Luca Delle Monache et al. (2011) was employed to verify the results from the first method. This method runs a post processing algorithm using the weighted average of the observations that were verified when the 15 best analogs were valid. Observed wind speed data at 10 m and 50 m height was used as Numerical Weather Prediction (NWP) input data and fit to observed time series data. From this, wind speeds at 116 m were extrapolated, in order to estimate the reconstructed atmospheric stability. The two methods were benchmarked and shadow effects were quantified in the range of 7.53% - 22.92% for the SEDM and within an 80% confidence interval of 0.23% -1.83% for the statistical downscaling method. Given the physical method’s exceedance of this confidence interval, WRF idealized modeling proves itself as a consistent means of quantifying an offshore wind farm’s wake, as demonstrated by comparable studies, however inaccurate when benchmarked to statistical modelling methods that use observed wind speed data to recreate atmospheric conditions. / Wake Research Group
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-304737 |
| Date | January 2016 |
| Creators | Werner, David |
| Publisher | Uppsala universitet, Institutionen för geovetenskaper, Vattenfall AB |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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