Understanding the strengths and weaknesses of a new-generation numerical weather prediction model for application to short-term wind energy prediction



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Wind power is a growing economy and science. It has far reaching consequences in all aspects of society and if goals of energy sustainability and security are to be met, wind power will become crucial. Numerous studies, including the U.S. Department of Energy’s report “20% by 2030”, have already shown the incredible growth of wind power in the past 5 years. A compelling problem with this growth is that in spite of all the wind power currently online in the United States, according to the “20% by 2030” report, we have only utilized about 0.3% of the total available land based wind power, and are only 11% of the way to the 2030 goal of 300GW of installed wind. Wind power is entirely at the mercy of the wind, and in essence, the entire atmosphere. These are very dynamic systems and because of this, the energy distribution and market system of the United States are already feeling the strain of integrating wind as a significant resource. One of the main ways in which wind integration is achieved is by producing wind power forecasts. To date, several works have concluded that wind speed and wind power forecasting have economic benefits for wind farms and are necessary for the best integration into the electric grid.
The work of this thesis is simulation conducted with the Weather Research and Forecasting model. The model is run as an ensemble of simulations varying each only by atmospheric boundary parameterizations schemes. Three case studies significant to wind power are assessed and discussed in terms of overall weather prediction, atmospheric boundary layer processes, and their significance to wind energy prediction.