This blog is part of the Southern Wind Energy Association’s Windy Wednesday series leading up to the wind energy industry’s largest annual event, WINDPOWER 2016, being hosted in New Orleans May 23-26. Registration and details available here. You can read the other blogs in this series by clicking on #WindyWednesday.
Wind turbine technology has advanced significantly in the past five years. With longer blades and taller towers, wind energy can now be generated throughout the South. However, outdated maps regarding wind energy resource potential continue to be used by some anti-wind energy activists to argue that wind energy cannot be generated within the region. A new free toolkit, made available by the National Renewable Energy Lab (NREL), helps eliminate some of the confusion regarding wind energy viability in the South.
The Wind Integration National Dataset (WIND) Toolkit provides data on 144,000 data points all across the country in an interactive map. Each datapoint provides 10-minute weather data from 2007-2012, and then extrapolates wind speeds into wind power output based on modern turbines.
The WIND Toolkit helps reduce a number of key problems associated with using older NREL wind energy resource maps – such as the 50 meter, 80 meter and 100 meter hub height maps. As some experts have noted previously, the older NREL wind energy resource maps flatten out mountaintops, and can result in lower-than-actual wind speed averages. The WIND Toolkit can provide more pin-point analysis, thus alleviating the effects of low-resolution wind speed maps. Also, the wind energy resource maps only show wind speed, not wind power output. While wind speed plays a large role in determining wind power output, it is not the only factor. Air density is another important factor; when flowing, dense air contains more energy than lighter air, even if both exhibit the same wind speed. A wind turbine’s specific power curve is necessary to provide a more complete analysis of a wind farm’s viability. The WIND Toolkit uses not only wind speed, but also air density and a variety of turbine power curves to provide actual wind power output estimates.
One of the greatest advantages of the WIND Toolkit is its level of specific weather data. Electric utilities must plan for extreme weather events, and respond to changes in electric demand multiple times per hour. In the South, a number of electric utilities exhibit “winter peaks”, where electric load is highest during the winter time, usually early mornings in February. In other parts of the country, wind energy has proven indispensable in providing electricity during extreme winter peaking events, like the recent Polar Vortex. During that event, wind energy saved consumers in the MidAtlantic $1 billion by reducing the need to rely on more expensive forms of electric generation during peak electric demand. The WIND Toolkit data allows electric utilities to sync actual historical weather conditions to actual historical electric load, and see where wind energy could make a positive impact for ratepayers.
Finally, the WIND Toolkit helps eliminate any guesswork by wind energy advocates regarding newly proposed wind farm projects. Using the old 50 meter, 80 meter or 100 meter wind speed maps used to be the only way the public had any sense of what “good” wind resources looked like. However, those maps always proved too coarse for the average viewer to interpret accurately. Anti-wind activists frequently used outdated maps, some even dating back to the 1980s, to make a case against wind energy. The WIND Toolkit can now provide better analysis for stakeholders interested in learning more about wind energy. As a quick case study, the image below shows the results of the WIND Toolkit query compared to a 100 meter wind speed map in Northeastern North Carolina. The WIND Toolkit shows an average wind speed of approximately 7 meters-per-second (15.7 MPH), but the 100 meter wind map shows speeds of <6 m/s (13.4MPH). That 1 m/s difference results in the difference between a 30% capacity factor and a 40% capacity factor, based on the WIND Toolkit’s power curve. In real terms, that is a 33% improvement in capacity factor. North Carolina’s first wind farm recently broke ground in that region, and reports suggest average capacity factors of that wind farm to be near 40% – very similar to the results of the WIND Toolkit.
Despite all the advancements of the WIND Toolkit over its predecessors, there are still some limitations. Not all sites have been directly verified, thus wind farm development companies will still plan on collecting local data, and a wind farm developer’s data is likely to be much more accurate to a specific site than the WIND Toolkit. Also, the WIND Toolkit did not measure every feasible location all across the country, thus anti-wind activists may falsely claim that a particular county only has one small viable spot when in fact a particular county may have much larger potential. Finally, the WIND Toolkit used aggregated wind turbine power curves, not specific turbine power curves that may be better suited for a location. The WIND Toolkit evaluated wind energy resources in the Great Plains using an IEC-2 type wind turbine, even though wind developers are now frequently deploying IEC-3 type wind turbines in that region and receiving significantly higher capacity factors than what are reported in the WIND Toolkit.
Access the WIND Toolkit by navigating through NREL’s Wind Prospector tool, here: https://maps.nrel.gov/wind-prospector
Tags: #windpower16, #WindyWednesday, 100 meter map, air density, American Wind Energy Association, National Renewable Energy Lab, NREL, offshore wind, wind energy, wind energy integration, wind power, wind speed, WIND Toolkit, wind turbine, WINDPOWER2016
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