For decades now, wind turbines have been generating electricity more cheaply than most other (non-hydro) renewable energy technologies. In particular, wind has maintained a comfortable lead over...
Forecasting brings wind energy under control.
on the quality and quantity of data that feeds into them. And given the lack of historic data, what forecasters really need is a larger sample of real-time and ongoing wind measurements.
“Input data limits forecasting ability,” Zack says. “Observations available right now aren’t adequate to forecast ramp events even a few hours ahead. To get a big improvement you need better measurements of the atmosphere than we have right now, and you need different measurements than those that typically serve weather forecasts.”
Given NWS’s mission to predict weather events that threaten public safety, its work concentrates on understanding conditions where storms are spawned, in the upper and middle atmosphere. “There aren’t a lot of observations of winds near the earth, and those observations are critical for forecasting wind-ramp events,” Zack says.
Such observations come from wind-measuring equipment in the field, but there’s a practical limit to the number of anemometers that forecasting companies and wind developers can afford to install. AWS is trying to work around this limit, to some degree, by building forecast-sensitive models that might help researchers identify locations where anemometers can yield the most valuable measurements.
“How do we get the most out of our observational dollar?” Zack asks. “The answer is to do observational targeting, using models that point out areas that will be most sensitive for forecasting for a given site, depending on topography, time of year and other factors.”
AWS is proving out this approach in a DOE-funded project in Hawaii. “Hawaii has one of the most critical wind management problems of anywhere in the United States,” Zack says. “It’s an island grid with no regional interconnection, so they have no flexibility, and they have a very high wind penetration. That’s why DOE targeted that area to test methods for improving the short-term forecast.”
In addition to the Hawaii project and other efforts to advance forecasting technology, DOE and NOAA are considering options for large-scale deployment of measurement devices—including anemometers and also wind profilers. Profilers are sensitive Doppler radar stations that point upward to measure wind speed and direction at various elevations. Deploying such devices would be extremely helpful for improving the resolution of wind models, but at a cost that’s beyond what wind developers or forecasters can reasonably afford—hence the rationale for a government program that might cost billions of dollars.
Fortunately, such a wind-measurement Manhattan project might not be necessary to provide a major breakthrough in observational data.
“We need to develop technologies that bring costs down to a manageable level,” Zack says. “For example, we’re excited about low-cost Doppler radar systems that might be mounted to cell towers, and could give you good boundary-layer wind measurements over a 30- to 50- kilometer radius.” Such technologies are in early development phases, but by about 2015 they might become economical enough to allow private companies to deploy a whole network of them, Zack says.
“You could take a lot of measurements remotely and feed the data rapidly into forecasting models. That’s where the technology needs to advance to really improve forecasting.”