Abstract
In this study, the performance of the mesoscale meteorological Weather Research and Forecast (WRF) model coupled with the microscale computational fluid dynamics based model WindSim is investigated and compared to the performance of WRF alone. The two model set-ups, WRF and WRF-WindSim, have been tested on three high-wind events in February, June and October, over a complex terrain at the Nygårdsfjell wind park in Norway. The wind speeds and wind directions are compared to measurements and the results are evaluated based on root mean square error, bias and standard deviation error. Both model set-ups are able to reproduce the high wind events. For the winter month February the WRF-WindSim performed better than WRF alone, with the root mean square error (RMSE) decreasing from 2.86 to 2.38 and standard deviation error (STDE) decreasing from 2.69 to 2.37. For the two other months no such improvements were found. The best model performance was found in October where the WRF had a RMSE of 1.76 and STDE of 1.68. For June, both model set-ups underestimate the wind speed. Overall, the adopted coupling method of using WRF outputs as virtual climatology for coupling WRF and WindSim did not offer a significant improvement over the complex terrain of Nygårdsfjell. However, the proposed coupling method offers high degree of simplicity when it comes to its application. Further testing is recommended over larger number of test cases to make a significant conclusion.
Highlights
Renewable energy resource assessment is an important research field due to increasing energy demand as well as the need to reduce the dependency on fossil fuel
Both the wind speed and the wind direction are compared to the measurements by root mean square error (RMSE), bias and standard deviation error (STDE)
The results imply that the performance of the microscale model is mainly dependent upon the quality of input mesoscale winds used as virtual climatology
Summary
Renewable energy resource assessment is an important research field due to increasing energy demand as well as the need to reduce the dependency on fossil fuel. According to [1] successful development of wind energy requires high accuracy of the predicted available wind resource to assure a lower investment risk. The classical methods for wind energy resource assessment is per date local wind measurement campaigns, extrapolation of free atmospheric wind provided by global data bases or by use of wind atlases [2]. Data provided from these different methods are used either alone or in combination with micro-scale models.
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