Extrapolating Satellite Winds to Turbine Operating Heights

AbstractOcean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellite-based wind maps. Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs. The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.02°. Calculations of the corresponding wind power density and Weibull parameters are shown. Comparisons with mast observations reveal that NWP model outputs can correct successfully for long-term stability effects and also, to some extent, for the limited number of satellite samples. The satellite-based and NWP-simulated wind profiles are almost equally accurate with respect to those from the mast. However, the satellite-based maps have a higher spatial resolution, which is particularly important in nearshore areas where most offshore wind farms are built.

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Time-Dependent Upper Limits to the Performance of Large Wind Farms Due to Mesoscale Atmospheric Response

Energies ◽

10.3390/en14196437 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6437

Author(s):

Kelan Patel ◽

Thomas D. Dunstan ◽

Takafumi Nishino

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Weather Prediction ◽

Wind Farms ◽

Time Dependent ◽

New Method ◽

Offshore Wind ◽

Dependent Manner ◽

Upper Limits ◽

Large Wind

A prototype of a new physics-based wind resource assessment method is presented, which allows the prediction of upper limits to the performance of large wind farms (including the power loss due to wind farm blockage) in a site-specific and time-dependent manner. The new method combines the two-scale momentum theory with a numerical weather prediction (NWP) model to assess the “extractability” of wind, i.e., how high the wind speed at a given site can be maintained as we increase the number of turbines installed. The new method is applied to an offshore wind farm site in the North Sea to demonstrate that: (1) Only a pair of NWP simulations (one without wind farm and the other with wind farm with an arbitrary level of flow resistance) are required to predict the extractability. (2) The extractability varies significantly from time to time, which may cause more than 30% of change in the upper limit of the performance of medium-to-high-density offshore wind farms. These results suggest the importance of considering not only the natural wind speed but also its extractability in the prediction of (both long- and short-term) power production of large wind farms.

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Applications of satellite winds for the offshore wind farm site Anholt

Wind Energy Science ◽

10.5194/wes-3-573-2018 ◽

2018 ◽

Vol 3 (2) ◽

pp. 573-588 ◽

Cited By ~ 5

Author(s):

Tobias Ahsbahs ◽

Merete Badger ◽

Patrick Volker ◽

Kurt S. Hansen ◽

Charlotte B. Hasager

Keyword(s):

Wind Speed ◽

Wind Energy ◽

Wind Farm ◽

Weather Prediction ◽

Wind Farms ◽

Offshore Wind ◽

Horizontal Wind ◽

Offshore Wind Energy ◽

Site Assessment ◽

Scada System

Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other 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 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 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 along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and 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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Long-term effects of wakes from offshore wind farms on the wind conditions at FINO1

10.5194/dkt-12-57 ◽

2020 ◽

Author(s):

Marcos Ortensi ◽

Richard Fruehmann ◽

Thomas Neumann

Keyword(s):

Wind Speed ◽

Turbulence Intensity ◽

Wind Farms ◽

Offshore Wind ◽

Wind Flow ◽

Long Term Effects ◽

Offshore Wind Farms ◽

Clear Effect ◽

Research Platform

<p>Investigation on how the wind conditions at the FINO1 research platform have changed through the construction of new wind farms in the vicinity. The long measurement recorded at FINO1 opens the opportunity to analyze how the progressive construction of wind farms influences the downwind wind conditions over a range of distances. In previous publications it has been shown that the wakes from the nearby wind farms Alpha Ventus, Borkum Riffgrund 1 and Trianel Windpark Borkum I have a clear effect on the wind flow, causing a reduction in wind speed and an increase in turbulence intensity.</p>

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Assessing Boundary Condition and Parametric Uncertainty in Numerical-Weather-Prediction-Modeled, Long-Term Offshore Wind Speed Through Machine Learning and Analog Ensemble

10.5194/wes-2021-33 ◽

2021 ◽

Author(s):

Nicola Bodini ◽

Weiming Hu ◽

Mike Optis ◽

Guido Cervone ◽

Stefano Alessandrini

Keyword(s):

Boundary Condition ◽

Wind Speed ◽

Weather Prediction ◽

Parametric Uncertainty ◽

Offshore Wind ◽

Gradient Boosting ◽

Single Model ◽

Short Term ◽

Wind Resource

Abstract. To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed, but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup where a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, where only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R2 > 0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability of the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

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Ten Years of Meteorological Measurements for Offshore Wind Farms

Journal of Solar Energy Engineering ◽

10.1115/1.1850489 ◽

2005 ◽

Vol 127 (2) ◽

pp. 170-176 ◽

Cited By ~ 42

Author(s):

Rebecca Barthelmie ◽

Ole Frost Hansen ◽

Karen Enevoldsen ◽

Jørgen Højstrup ◽

Sten Frandsen ◽

...

Keyword(s):

Wind Speed ◽

Power Output ◽

Wind Farms ◽

Offshore Wind ◽

Short Term ◽

Offshore Wind Farms ◽

Monitoring Strategies ◽

Wind Resource ◽

Wind Resources

Risø has been monitoring wind resources and power output from offshore wind farms since 1993. A considerable degree of expertise has been developed in optimizing measurements and in using these databases to develop and validate models for offshore environments. This paper describes the evolution of monitoring strategies to a fully automated satellite based retrieval that provides near-real time access to offshore data, even at remote stand-alone masts. An overview of wind speed and turbulence at offshore sites in Denmark is given. Finally, three methods of generating long-term wind resource estimates from short-term measurements are outlined.

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