Monday, 11 January 2016
Given more than 70% of earth's surface is water, there is enormous potential to capture the kinetic energy of offshore wind and produce power. An offshore wind resource assessment (OWRA) is critical for developing a successful project as it attempts to accurately characterize the viability of a site-specific wind resource prior to wind farm construction. However, significant uncertainties exist during this stage, due in-part to limited offshore observations, thus limited understanding of complex wind regimes in the marine environment where turbines operate. Due to the cubic relationship between a turbine's power output and wind speed, a relatively small error in the wind resource estimate translates to significant error in expected power production. To quantify the impact of various wind estimate techniques, and the role of micrometeorology, on a turbine's expected power production, the University of Maryland Baltimore County (UMBC) deployed Doppler wind lidar technology, and other met-ocean remote sensing systems, on the Scarlett Isabella which traversed Maryland's offshore Wind Energy Area (WEA) from July-August 2013. Compared to offshore lidar hub-height wind measurements, results demonstrate a significant underestimation of the wind resource from industry-standard surface extrapolation and computer model wind resource estimates.
Discrepancy between estimates are related to the frequent development of nocturnal low-level wind maximums (LLWMs) and therefore ‘non-standard' offshore wind profiles in Maryland's WEA. Although LLWMs contribute to stronger winds at hub-height, associated wind speed and direction shear, as well as turbulence intensity throughout a turbine's rotor-layer (~ 40-160m) may also affect a turbine's ability to produce power. This research uses wind lidar to compare ‘equivalent wind speed' calculations that account for the impact of these micrometeorology features on a turbine's potential power output, providing a more comprehensive wind resource estimate and representation of favorable atmospheric regimes for offshore wind power production in Maryland's WEA. Finally, an actual wind resource 10-20% less than expected can be the difference between project profit and loss. Therefore, this research also investigates the economic implications of varying wind approximation techniques. A sensitivity analysis of a wind farm's estimated capacity factor from varied OWRA techniques is performed and incorporated into cost models to elucidate each method's contribution to a project's expected profitability.
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