Advances in wind turbine technology and reassessments of wind power potential have resulted in a rapid buildup of wind energy capacity in the United States and worldwide. Already several nations have mandated that a substantial portion of their electricity needs be generated by wind turbines. Numerous wind farm projects are under development in regions where the wind power resource has traditionally been considered too low for economical extraction. Wind energy is now, in many areas, less costly than conventional energy production methods. In choosing wind farm sites, planners rely on a regional wind climatology (averages and standard deviations) derived from the statistical interpolation of surface wind speed observations that are not at the height of the turbine, are sparsely distributed, and may be of questionable quality. Such interpolations may contain a great deal of error when no nearby observations exist, especially over water or complex terrain (often some of the windiest regions). As a result, wind farm siting methodologies are largely post hoc and the best sites climatologically may be overlooked. An alternative to traditional statistical methods is the use of a regional numerical climate model (RCM) to provide estimates of wind speeds at a fine temporal (hourly) and spatial (< 12 km) scale, and do so at the height of the wind turbine, rather than the surface. Thus, the primary goal of this dissertation research is to develop a regional climate model (RCM) based geographic information system (GIS) for the purposes of wind farm siting. The key question this research will address is whether the RCM/GIS approach represents a significant improvement over currently employed statistical techniques (e.g., spatial interpolation, measure-correlate-predict [MCP], probability density functions [pdf]). The research will make use of the wind speed output of the MM5 RCM operated by the U.S. Forest Service for fire weather analysis over the Great Lakes region of North America. The model output will be validated against wind speed observation records at several surface stations throughout the region. It is expected that the RCM will provide a significant improvement over traditional statistical methods in the fine-resolution estimation of the regional wind resource. Upon validation, the output of the RCM will be included in a GIS and coupled with additional wind farm siting criteria to create a siting tool that will identify the optimal wind farm sites in a region based upon the considerations determined by a wind farm developer. After the initial model development and validation, this grant facilitates the evaluation of the RCM/GIS model over a different region (the UK) where wind energy development also is growing rapidly and a great deal of wind climate research already has been conducted. This evaluation, to take place at the University of East Anglia will also provide an opportunity for leading European wind energy researchers to provide input on the suitability of such a model to varied and diverse regions.
This research endeavors to provide a new method for the optimal siting of wind farms such that they can operate most economically and with a minimum of disruption to society and the environment. It also represents a new and practical use for a RCM outside the realm of atmospheric science. Such research would highlight the strengths and limitations of utilizing regional climate models in practical applications. Additionally, through the use of a regional climate model the wind resource of regions where surface wind observations are sparse or lacking may be effectively estimated. Such ability would facilitate the development of wind energy projects in offshore locations as well as in less developed countries that would greatly benefit from the development of an independent energy infrastructure. Furthermore, as a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.
