Similar to how a young sunflower follows the sun to catch the light, wind turbines turn to face the wind to maximize the energy they produce. Although you probably haven’t noticed it happening, the head of a wind turbine can rotate left to right on the top of its tower to make the most of the available wind.
This is because the wind’s speed and direction often change. Fluctuations in air pressure and temperature, as well as the Earth’s rotation, influence how the wind blows and how much energy can be captured. Fixed features of the landscape—valleys, hills, forests, and buildings—also affect the direction and strength of the wind. But there’s one fixed feature that’s particularly disruptive: other turbines.
Picture how water changes when you are sailing in a boat, with your vessel leaving a wave pattern on the surface behind it. This physical phenomenon is known as the wake effect, and it poses an important challenge to wind farms. When wind flows through a turbine to generate electricity, a wake is created, making the wind that other turbines behind it use slower and more turbulent. When adding up the impact across an entire wind farm, a 2009 study estimated that total electricity production is reduced by 10 to 20 percent.
Now an algorithm has shown how adjusting the orientation of the turbines on a farm in a coordinated way can reduce the wake effect and boost the total output. Wind turbines “are extremely well designed to operate individually,” says Michael Howland, who led this research, which was published recently in Nature Energy. But “when wind turbines extract power, we know that there’s immediately less power available in the wind downwind.” Because turbines are almost always placed in wind farms, with turbines close to each other, that’s a problem.
“The incoming wind is different in every location,” says Howland, an assistant professor of civil and environmental engineering at the Massachusetts Institute of Technology. “Also the wind direction is constantly changing in time. And so the turbine has to react to that.” Typically, turbines respond individually to wind changes, turning to directly face the wind to try to maximize their own production, with no regard for the wake they create behind them. It’s this “selfishness” that leads to an overall energy loss.
To reduce these losses, Howland and his team’s algorithm first uses atmospheric physics and operational farm data—such as temperature and wind conditions—to estimate the wakes that turbines are creating and how these are impacting other turbines. The algorithm then predicts ways to arrange the turbines so they’re less disruptive to one another. Turning a turbine head so that it faces the oncoming wind at a slight angle, for example, changes the direction of the wake it produces and reduces how exposed turbines behind it are. While the strategy may slightly reduce the output of the turbines in front, as they aren’t facing the wind completely head-on, it increases the production of turbines behind, and the result is a net gain in electricity production.
Exactly how much energy is gained depends on factors such as the farm layout and the site’s wind conditions. However, when tested at a commercial farm in India, the algorithm boosted the energy output between 1 and 3 percent, depending on the wind speed, which would be the equivalent of powering 3 million homes if the software were deployed across the world’s existing farms, the study’s authors estimate.
And reaching that point isn’t as far-fetched as it may sound. One of the benefits of the approach is its potential for real-world scalability. “Usually to increase the production unit, you either need to put in a larger rotor or a more powerful generator, or you need to change some of the hardware,” says Xavi Vives, a control engineer at wind turbine manufacturer Siemens Gamesa. (Vives was not involved in the study, though Siemens Gamesa staff were part of the research.) “But this is pure software, so it’s very promising at a very low cost.”
For Varun Sivaram, one of the study’s coauthors, who at the time served as chief technology officer at ReNew Power, India’s leading renewable energy company, testing the tech in India was significant too. “I wanted to find a way to translate a technology from the lab scale into a real-world experiment. And I also wanted to do it in an emerging economy because that’s where the real need is for clean energy solutions—in these emerging economies where the energy demand is growing,” he says.
As well as raising the power output of turbines, the algorithm could also help wind farms by extending the life of turbines and reducing the wear that can diminish their output over time. “I think the most important conclusion from their study is that if you can even out the loads, if you can actually let more wind pass to subsequent turbines, you’re going to reduce the wear and tear on the first turbine,” says Mark Z. Jacobson, professor of civil and environmental engineering at Stanford University. Vives agrees: “The higher the turbulence, the higher the wear and tear … if you can reduce or steer the wake away, then you’re also giving more slack to the turbines so they can operate longer.”
While the study has shown promise, Jacobson thinks further experiments are needed before the software can be rolled out, as the initial testing focused on a setup involving three turbines under specific conditions. In reality, there are infinite potential configurations of turbines, wind speeds, and topographies, he explains. “I think they need to test more complex configurations and try to come up with general rules that are applicable regardless of the configuration,” he says. “You don’t want to try to be optimizing every single turbine and farm.”
As wind energy is scaled up, Sivaram believes algorithms like this will be needed to generate the most electricity possible. Ideal land sites for wind farms require specific circumstances—places with really fast wind speeds and plenty of land to place turbines far apart. The future is likely to see turbines placed close together as land becomes less available.
Offshore wind sites can help circumvent this challenge, and they can use turbines that are taller and larger, allowing for more energy to be captured. But scaling up reignites the wake problem, “because interactions between turbines scale with the rotor diameter,” says Howland. For offshore turbines, with their enormous blades, to completely get rid of wake interactions you would need to place turbines several kilometers apart, he says. This increases the price of leasing the area for the farm and building the transmission lines that connect turbines to each other and the grid. Using an algorithm to boost efficiency when turbines are closer together could be a better alternative.
Finding solutions to these sorts of issues will of course be worth it. Wind and solar are some of our safest and cleanest sources of energy, and so are “probably our primary tools for reaching net zero,” says Sivaram, who now serves as a senior advisor to John Kerry, the US special presidential envoy for climate. Green energy sources need to be optimized because as the years pass, we’re going to rely on them more and more—not just for our traditional power needs, Sivaram explains, but to power electric vehicles, industries moving over to electrification, and technologies that we could come to rely on, such as carbon capture or water desalinization.
In the future, renewables “will produce the lion’s share of the world’s electricity,” Sivaram says, so “every little bit of energy you can get out of solar and wind counts.”
