Improving Maryland’s Offshore Wind Energy Resource Estimate Using Doppler Wind Lidar Technology to Assess Microtmeteorology Controls
1 University of Maryland, Baltimore County, Baltimore, MD 21250,
2 University of Maryland, College Park, MD 20742, USA
3 Naval Research Laboratory, Washington D.C., 20375, USA
4 Joint Center for Earth Systems Technology, Baltimore, MD, 21250, USA
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Published online: 7 June 2016
There is enormous potential to harness the kinetic energy of offshore wind and produce power. However significant uncertainties are introduced in the offshore wind resource assessment process, due in part to limited observational networks and a poor understanding of the marine atmosphere’s complexity. Given the cubic relationship between a turbine’s power output and wind speed, a relatively small error in the wind speed estimate translates to a significant error in expected power production. The University of Maryland Baltimore County (UMBC) collected in-situ measurements offshore, within Maryland’s Wind Energy Area (WEA) from July-August 2013. This research demonstrates the ability of Doppler wind lidar technology to reduce uncertainty in estimating an offshore wind resource, compared to traditional resource assessment techniques, by providing a more accurate representation of the wind profile and associated hub-height wind speed variability. The second objective of this research is to elucidate the impact of offshore micrometeorology controls (stability, wind shear, turbulence) on a turbine’s ability to produce power.
Compared to lidar measurements, power law extrapolation estimates and operational National Weather Service models underestimated hub-height wind speeds in the WEA. In addition, lidar observations suggest the frequent development of a low-level wind maximum (LLWM), with high turbinelayer wind shear and low turbulence intensity within a turbine’s rotor layer (40m-160m). Results elucidate the advantages of using Doppler wind lidar technology to improve offshore wind resource estimates and its ability to monitor under-sampled offshore meteorological controls impact on a potential turbine’s ability to produce power.
© Owned by the authors, published by EDP Sciences, 2016
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