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.
distributed DR might also bring unintended consequences. Changing behavior affecting customer demand might complicate load forecasting for system operators, who make ongoing decisions based on load rising and falling in a predictable way.
“Smart grids offer a host of reliability benefits, and could allow operators to see patterns they’re not seeing now,” Lauby says. “But to some degree, bulk-power system operators assume certain characteristics about load and the direction of energy flows, based on experience. Now, with two-way flows and with communication between customers and the market, the predictable nature of the load will change. We’ll need to get experience with that.”
As valuable as forecasting is becoming, system operators harbor well-justified fears about the inherently chaotic behavior of large weather patterns—and the way they can rapidly change the output from wind farms. Large-scale ramping events, in which the wind suddenly dies or rises to a gale, make it difficult for system operators to rely on wind capacity. And they create special problems for forecasters, because these events are atypical and tough to predict—precisely because they’re atypical.
“To get a good forecast, you need a good sample of events,” says John Zack, director of forecasting for AWS Truepower (formerly AWS Truewind). “Most wind farms haven’t been operating for a long time, so you’re always facing new and different events that you haven’t seen before in the history of the system. And the big events, the ones that are of greatest interest for reliability and security, are anomalous events.” As a result, forecasters have little or no data to show them the specific local conditions that lead to major ramp events at a given site.
“Predicting large-scale ramps is tricky, and it will remain tricky,” Leick says. “Forecasts can indicate a high probability that a thunderstorm will occur tomorrow afternoon, and that you’ll experience some ramp events. But will it be at 1:30 or 2:15? That’s the level of precision people want.”
In practical terms, gauging the exact timing and location of a large-scale ramp event is beyond the reach of current technologies and weather models. As a result, researchers are focusing a great deal of attention on this area.
Initial approaches are using information technology to produce more accurate and useful forecast information for operators. For example, probabilistic forecasts can provide a clearer idea of the mathematical chance a ramp event will happen within a given time period. “Operators need to know what the probability of an event is in order to assess the risk and decide whether to take action,” says Storck of 3Tier. So forecasting companies are working to refine the way their systems calculate and provide probabilistic data.
Other computational approaches include high-performance modeling systems, such as those developed by the Geophysical Fluid Dynamics Laboratory at the National Oceanic and Atmospheric Administration (NOAA). Although such systems are developed for weather forecasting generally—to help the National Weather Service (NWS) predict thunderstorms, tornadoes and blizzards—the same technology helps wind energy forecasters to improve their computational models of the conditions that lead to ramp events.
Ultimately, however, all models depend