Wind Farm Performance-Prediction and Optimization Spotlight

“Starting a project and securing funding sources can be challenging if you don’t already have a track record of having developed the ideas,” says Dabiri. “What we were beginning to work on was still in the nascent stages.”

So, to initiate their collaboration, they applied for a seed grant from the TomKat Center for Sustainable Energy in 2016, and with that support in place, they began to explore the complementary reach of their research. Stanford has its own wind farm test site in Northern Los Angeles County, and that seemed like a logical place to start.

“At our field site in Southern California, you can move around the physical turbines,” says Dabiri. “But you have to pick, OK, what’s the first experimental configuration? What’s the second? And how do you prioritize them?”

That’s where Lele’s theoretical framework comes in. Together, he and Dabiri can use physics to point the way to promising directions for the experimental design, and, in turn, the test site reports back wind power data that either confirms or redirects the scientific calculations.

“In the present age of machine learning, data has often been used to inform models—but what’s been missing is the physics,” says Lele. “Fluid mechanicians have a long tradition of trying to understand the physics and come up with simple models.”

Their combined expertise has opened up new prospects for optimizing the more than 200,000 wind turbines currently in operation the world over. Their science is already out of the lab and into actual wind farms, through alliances with a Canadian wind power company, as well as a wind energy complex planned in the southern United States. With 170,000 acres and up to 3,000 megawatts of wind power, this complex could become one of the largest wind farms ever built.

The professors hope that their analytical sweet spot will give wind farm developers the information they need to select turbine size and orientation, within a budget and timeline that makes the process actionable. If their models prove to be as high-quality as they hope, then wind farmers around the world could use the tool to generate more profits and more renewable energy.

Source: Stanford University