Using Doppler Wind Lidar to Assess Meteorological Controls on Offshore Wind Power Generation

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Wednesday, 7 January 2015: 11:30 AM
211A West Building (Phoenix Convention Center - West and North Buildings)
Ruben Delgado, JCET/Univ. of Maryland Baltimore County, Baltimore, MD; and N. Goudarzi, S. Rabenhorst, F. Daham, G. Antoszewski, D. Wesloh, and A. St. Pé

An interdependent relationship exists between meteorological controls (stability, turbulence, wind shear) and an offshore wind turbine's power curve performance. A turbine's power coefficient (Cp) represents its efficiency in converting the wind's kinetic energy into electrical power. For each wind turbine, the manufacturer is required to estimate the Cp and expected energy output, referred to as the Manufacturers' Power Curve (MPC). The MPC correlates wind speed at hub-height to expected turbine power output, however similar to the wind profile power law method, is quintessentially based on assumptions of neutrally stable wind profiles and only mechanical turbulence. Since a turbine's power output varies in proportion to the cube of the wind speed at hub-height, this unrealistic representation of the wind profile is a significant problem; a relatively small error in estimation of wind speed translates to a significant error in estimation of power generation and therefore anticipated project profit.

Limited high spatial and temporal met-ocean measurements impede characterization of physical processes causing a turbine's actual power curve to deviate from the estimated MPC. The advancement of remote sensing technology, particularly Doppler wind LIDAR deployed offshore, has assisted the offshore wind energy industry in reducing project uncertainty by providing a real-time measurement vertical wind profile variability. Recent research demonstrates an underperformance in expected wind power production during stable atmospheric regimes and overperformance during unstable conditions. Doppler wind LIDAR deployed offshore is well suited to investigate the impact of atmospheric stability on effective wind shear (blade tip to rotor (40m-100m)), rotor to top blade tip (100m-160m)) as well as deep layer vertical wind shear, which significantly contribute to (un)favorable conditions for offshore wind power generation. Further, turbulence intensity has been demonstrated to impact wind energy production and may also be derived from Doppler wind lidar measurements.

The proximity of Maryland's offshore Wind Energy Area (WEA) to the Mid-Atlantic's unique topography of juxtaposed Appalachia, urban core, and coastline, provides an ideal opportunity to investigate dynamic regional and small-scale weather systems' influence on offshore wind energy production. The University of Maryland Baltimore County (UMBC) deployed Doppler wind LIDAR during the Maryland Energy Administration sponsored research campaign (July-August 2013) and collected vertical wind profiles throughout the State's offshore WEA.

The average July-August 2013 diurnal wind variability suggests frequent development of nocturnal low-level wind maximum across a turbine's rotor layer. Mean diurnal variation of effective wind shear values also suggest greatest wind shear during the nocturnal low-level wind maxima. Wind profile data at a nearby coastal site (Cambridge, MD), reanalysis data, and high resolution model output provide corroborating evidence that the formation of coastal low-level jets (LLJs) and northwesterly down-slope flows across the Appalachian Mountains enhanced several low-level wind maximums in the WEA. Such weather phenomena frictionally decoupled the surface from the layer near a turbine's hub-height and evidence suggests may have initiated a feedback mechanism in which decreased turbulence and strong vertical wind shear occurred concurrently with wind maxima. Strong vertical wind shear near a turbine rotor layer creates less favorable conditions for optimal wind energy production.

Weibull scale parameter c is closely related to mean wind speed, and represents how windy a location is (m/s) while the shape parameter, K, is dimensionless and describes the width of the wind speed distribution, ranging from 1.0-3.5. The average c parameter at 100m, a turbine's typical hub-height, suggests favorable winds for power generation with mean winds near 5.4 m/s, however the low k parameter (1.5) demonstrates a variable wind distribution, highlighting the need to understand the physical mechanisms driving disparate wind regimes offshore. The average wind direction distribution demonstrate a bimodal distribution, with the majority of the greatest wind speeds (> 10 m/s) originating from the southwest and northeast. The predominate southwest distribution is expected in the Mid-Atlantic during the summer due to large temperature gradients between land and adjacent water, as well as the expansion of a semi-permanent high pressure system (Bermuda High), both of which act initiate/ reinforce southwesterly flow along the coast.

This research investigates the impact of distinct meteorological controls (stability, turbulence, effective wind shear) on turbine power curve performance with the objective to understand physical processes driving wind variability and overcome barriers associated with current wind measurement techniques. Several case studies from the UMBC offshore wind LIDAR deployment are presented to elucidate the potential impact of disparate Mid-Atlantic weather patterns on wind energy production. Radiosonde instruments, recording pressure, temperature, moisture and wind speed were launched on the research vessel to capture transitioning atmospheric stability regimes in the offshore environment.

July-August 2014 Doppler wind LIDAR measurements in Maryland's WEA serve as a foundation for understanding processes that drive the variability of Maryland's offshore wind energy resource on hours, days, and inter-annual timescales. Understanding meteorological controls impact on wind turbine's power curve performance is critical for reducing uncertainty in offshore wind energy projects as it may contribute to a more accurate estimation of offshore wind energy capacity (thereby reducing Annual Energy Production uncertainty), and provide insight into optimal wind farm layout (reducing energy loss from wake effect) and turbine design strategies. ->