Wind energy: Modeling implications and what influences policy decisions

There are many factors that determine the success of a wind energy project. One is accurate wind speed and wind energy production prediction. The first part of this thesis addressed the accuracy of the software WindPRO and WAsP for predicting the annual energy production for a residential-scale wind turbine. Two installed turbines were used as prediction sites, while two meteorological towers were used as reference sites. Wind speed and direction data for the met towers, along with a height contour map and a roughness map, were used as inputs to the prediction calculations for wind speed and direction and from there energy production was predicted. Predictions were consistent and all had errors of less than 17%. The model over-predicts annual energy production, which is preferable to under-prediction because there are about 50 days a year during which the turbines were not producing energy due to maintenance or weather, the most common of which is ice on the blades. The model does not account for these real-life events, so the over-prediction compared to actual energy production seen in the predictions makes sense. The models can be used to accurately predict annul energy production for residential-scale turbines under similar conditions as those in this study. For utility-scale wind, the permitting process can present some challenges, especially for projects that face public opposition, like the Perrin Ranch Wind project in northern Arizona. The Coconino County Planning and Zoning Commission approved the project in December of 2010, but three community groups appealed the approval. The project was then brought before the Board of Supervisors in February 2011. An analysis of the county documents and interviews with the members of the Board of Supervisors investigated what factors were taken into account when deciding on the Perrin Wind Ranch Project and how those factors were weighted. The Board of Supervisors considered information from different sources including the developer, the community members, and their staff. Information from their staff was valued the most, but information from all of the above sources were taken into account and considered as is seen with the additional conditions on the project. Many factors, like view shed and wildlife impacts, were taken into account by all Supervisors while some factors like national energy policy were only considered by one.