Jan. 6 — A research team led by the National Oceanic and Atmospheric Administration (NOAA) is performing simulations at the Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, to develop numerical weather prediction models that can provide more accurate wind forecasts in regions with complex terrain. The team, funded by DOE in support of its Wind Forecast Improvement Project II (WFIP 2), is testing and validating the computational models with data being collected from a network of environmental sensors in the Columbia River Gorge region.
Wind turbines dotting the Columbia River Gorge in Washington and Oregon can collectively generate about 4,500 megawatts (MW) of power, or more than that of five, 800-MW nuclear power plants. However, the gorge region and its dramatic topography create highly variable wind conditions, posing a challenge for utility operators who use weather forecast models to predict when wind power will be available on the grid.
If predictions are unreliable, operators must depend on steady power sources like coal and nuclear plants to meet demand. Because they take a long time to fuel and heat, conventional power plants operate on less flexible timetables and can generate power that is then wasted if wind energy unexpectedly floods the grid.
To produce accurate wind predictions over complex terrain, researchers are using Mira, the ALCF’s 10-petaflops IBM Blue Gene/Q supercomputer, to increase resolution and improve physical representations to better simulate wind features in national forecast models. In a unique intersection of field observation and computer simulation, the research team has installed and is collecting data from a network of environmental instruments in the Columbia River Gorge region that is being used to test and validate model improvements.
This research is part of the Wind Forecast Improvement Project II (WFIP 2), an effort sponsored by DOE in collaboration with NOAA, Vaisala—a manufacturer of environmental and meteorological equipment—and a number of national laboratories and universities. DOE aims to increase U.S. wind energy from five to 20 percent of total energy use by 2020, which means optimizing how wind is used on the grid.
“Our goal is to give utility operators better forecasts, which could ultimately help make the cost of wind energy a little cheaper,” said lead model developer Joe Olson of NOAA. “For example, if the forecast calls for a windy day but operators don’t trust the forecast, they won’t be able to turn off coal plants, which are releasing carbon dioxide when maybe there was renewable wind energy available.”
The entire article can be found here.
Source: Katie Jones, Argonne Leadership Computing Facility