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Quantifying the Shadow Effect between Offshore Wind Farms with Idealized Mesoscale Models and Observed Wind Data
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences. Vattenfall AB. (Wake Research Group)
2016 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

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.

Place, publisher, year, edition, pages
2016. , p. 155
Keywords [en]
Shadow effect, offshore wakes, atmospheric stability, analog ensemble, wake forecasting, idealized mesoscale modeling, Weather Research Forecast (WRF) model, downscaling, North Sea, Princes Amalia, Egmond aan Zee
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:uu:diva-304737OAI: oai:DiVA.org:uu-304737DiVA, id: diva2:1033728
External cooperation
Vattenfall AB
Educational program
Master Programme in Wind Power Project Management
Presentation
2016-09-30, B23, Cramérgatan 3, 621 57, Visby, Sweden, 14:20 (English)
Supervisors
Examiners
Projects
Wake Research GroupAvailable from: 2017-03-22 Created: 2016-10-09 Last updated: 2017-03-22Bibliographically approved

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