Improving the feasibility of wind energy through improved wind resource characterization and use of remote sensing technologies

Date

2014-05

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Abstract

While installed wind generating capacity has grown rapidly in the United States over the last several years, its primary competition in the electricity market, natural gas-fired generation, has benefited from low natural gas prices and efficiency improvements. Wind energy’s growth has been spurred on by the Federal Renewable Energy Tax Credit (PTC), which has helped make wind very cost competitive with natural gas generation. However, wind projects not currently under construction will not be eligible for the PTC due to its expiration and it is unclear when or if Congress will reinstate it. If wind energy is to continue to be cost competitive and the industry is to continue its rapid growth, its efficiency must be improved to the point where success in the U.S. is no longer dependent upon Congress reauthorizing or extending the PTC.

Advancements in Lidar technology as well as a decline in its cost offer the potential for improving the efficiency of wind generation through anticipatory wind data collection integrated with real-time turbine control. Furthermore, Lidar and a similar technology, Sodar, can be used to gather wind data at heights well above the that of most traditional meteorological towers and may provide an answer to achieving the level of accuracy in wind resource assessments and energy production estimates demanded by the lending and investment communities. The financial community has relied heavily on traditional meteorological towers and 3-cup anemometers to provide the wind data used in their due diligence process, but with turbine hub heights now frequently reaching or exceeding 100 meters, such towers may no longer be practical for this use.

The goal of this research is to determine the potential for remote wind sensing technologies such as Lidar and Sodar to improve the competitive position of wind energy relative to the more traditional sources of electric power generation.

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Keywords

Wind energy, Wind generation, Yaw error, Lidar, Sodar, Efficiency, Inefficiency, Uncertainty

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