Extrapolating Satellite Winds to Turbine Operating Heights

Badger, Merete; Peña, Alfredo; Hahmann, Andrea N.; Mouche, Alexis; Hasager, Charlotte Bay

doi:10.1175/jamc-d-15-0197.1

Cited by 42 publications

(73 citation statements)

References 45 publications

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“…Over the long-term (i.e., over periods of at least one year), however, a stability correction parameter calculated from the same WRF outputs has been found to match mast observations very well [38]. The correction is based on adaptation of the Monin-Obukhov similarity theory and it can be utilized to extrapolate the annual mean wind speed retrieved from satellite observations from 10 m to higher levels [27].…”

Section: Extrapolating Wind Speed From 10 M To Hub-heightmentioning

confidence: 96%

“…The influence of atmospheric stability on the vertical wind profile we investigate for a shorter period using the method presented in [27]. The long-term average wind profiles calculated with and without stability correction for Fino1 and Hainan are shown in Figure 6.…”

Section: Linear Correlation Between Wind Speeds From Ssm/i In-situ Amentioning

confidence: 99%

“…SSM/I-derived winds vs. in-situ at 100 m have lower correlation than those derived from WRF vs. in-situ at 100 m. This is likely a consequence of errors introduced by the vertical extrapolation. The influence of atmospheric stability on the vertical wind profile we investigate for a shorter period using the method presented in [27]. The long-term average wind profiles calculated with and without stability correction for Fino1 and Hainan are shown in Figure 6.…”

Section: Linear Correlation Between Wind Speeds From Ssm/i In-situ Amentioning

confidence: 99%

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Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Hasager¹,

Astrup²,

Zhu

et al. 2016

Remote Sensing

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Abstract:We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas.

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Section: Extrapolating Wind Speed From 10 M To Hub-heightmentioning

confidence: 96%

Section: Linear Correlation Between Wind Speeds From Ssm/i In-situ Amentioning

confidence: 99%

Section: Linear Correlation Between Wind Speeds From Ssm/i In-situ Amentioning

confidence: 99%

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Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Hasager¹,

Astrup²,

Zhu

et al. 2016

Remote Sensing

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“…These include the lack of publicly accessible high accuracy data at WT relevant heights and high temporal resolution for evaluation of numerical simulations such as those presented herein (Kusiak, 2016). The National Weather Service (NWS) operates over 900 stations where wind speeds are measured at a height of 10-m a.g.l., but these data are not 25 at or close to WT hub-heights and the actual vertical profile of wind speed is strongly dependent on stability making vertical extrapolation highly uncertain (Badger et al, 2016;Barthelmie et al, 1993;Motta et al, 2005). Additionally, wind speeds as measured by 2-D sonic anemometers at NWS stations are recorded at a resolution of 1 knot (0.514 ms −1 ) rounded up to the nearest knot when they are archived.…”

Section: Observational Datamentioning

confidence: 99%

Inter-annual variability of wind climates and wind turbine annual energy production

Pryor¹,

Shepherd²,

Barthelmie³

2018

Preprint

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Abstract.Inter-annual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in pre-construction projected AEP and the difference in 50 th and 90 th percentile (P50 and P90) AEP derives in part from variability in wind climates. However, the magnitude of IAV in wind speeds at/close to wind 10 turbine hub-heights is poorly constrained and maybe overestimated by the 6% standard deviation of annual mean wind speeds that is widely applied within the wind energy industry. Thus there is a need for improved understanding of the longterm wind resource and the inter-annual variability therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub-heights over the eastern USA indicate median gross capacity factors (computed using 10-minute wind speeds close to wind turbine hub-heights and the 15 power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at/near to typical wind turbine hub-heights in these simulations is lower than is implied by assuming a standard deviation of 6%. Indeed, rather than in 9 in 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP, results presented herein indicate that over 90% of the area in the eastern USA that currently has operating wind turbines simulated AEP lies within 0.94 and 1.06 of the long-term average. 20Further, IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to pre-construction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

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“…These issues have been partially overcome: A method for extrapolation of mean wind speeds retrieved from SAR at 10 m above sea level to the wind turbine hub height has been developed (Badger et al 2016) and a number of new SAR sensors have been launched in recent years, Wind Energ. Sci.…”

mentioning

confidence: 99%

Applications of satellite winds for the offshore wind farm site Anholt

Ahsbahs¹,

Badger²,

Volker³

et al. 2018

Preprint

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Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other 5 and in the lee of large land masses. Synthetic Aperture Radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open access to data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and 10 experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free stream and waked conditions. Spatial wind speed variations within the wind farm derived from SAR wind maps prior to the wind farm construction are found to agree well with information gathered by the SCADA system and numerical weather prediction models. Wind farm wakes are detected by comparisons between images before and 15 after the wind farm construction. SAR wind maps clearly show wakes for long constant fetches but the wake effect is less pronounced for short varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.

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Extrapolating Satellite Winds to Turbine Operating Heights

Cited by 42 publications

References 45 publications

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Quarter-Century Offshore Winds from SSM/I and WRF in the North Sea and South China Sea

Inter-annual variability of wind climates and wind turbine annual energy production

Applications of satellite winds for the offshore wind farm site Anholt

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