Skip to Main Content

How one of the world’s largest wind companies is using AI to capture more energy

In 1898, Hans Søren Hansen arrived in Lem, Denmark, a small farming town about 160 miles from Copenhagen. The 22-year-old was eager to make his way in business and bought a blacksmith shop. In time, he became known to those in the area for his innovative spirit.

Hansen’s business went on to change with the times, morphing into building steel window frames. Future generations continued to expand on Hansen’s openness to change, evolving to building hydraulic cranes, and ultimately, in 1987, becoming Vestas Wind Systems, one of the largest wind turbine manufacturers in the world.

That tenacity to adapt and succeed has continued to define Vestas, which is now looking to optimize wind energy efficiency for customers who use its turbines in 85 countries.

Working on a proof of concept with Microsoft and Microsoft partner, Vestas successfully used artificial intelligence (AI) and high-performance computing to generate more energy from wind turbines by optimizing what is known as wake steering.

That potential energy increase is important. But also important, Vestas says, was the rapidity with which the proof of concept was developed – in a few months – and what that could mean for putting it into place. The company is not the first to study the issue, but the expedited results were a differentiator for it.

Sven Jesper Knudsen, Vestas Chief Specialist and modeling and analytics module design owner
Sven Jesper Knudsen, Vestas Chief Specialist and modeling and analytics module design owner.

“This is a theoretical exercise that has been living in the research community for years,” says Sven Jesper Knudsen, Vestas chief specialist and modeling and analytics module design owner. “And there have been some demonstrations by both our competitors and also some wind farm owners. We wanted to see if we could try to shorten the development cycle.

“Time to market is essential to the whole wind industry to meet aggressive targets that we all have,” Knudsen says.

Wind, like solar, energy is a clean alternative to fossil fuels for creating electricity. Both wind and solar are of growing importance as the world looks to decrease the use of coal, gas and crude oil to reduce carbon emissions to meet climate change goals.

Wind power also is one of the fastest-growing renewable energy technologies, according to the International Energy Agency (IEA), an organization that works with governments and industry to help them shape and secure a sustainable energy future.

In 2050, two-thirds of the world’s total energy supply will come from wind, solar, bioenergy, geothermal and hydro energy, with wind power expected to increase 11-fold, the agency said in a report last year, Net Zero by 2050: A Roadmap for the Global Energy Sector.

“In the net zero pathway, global energy demand in 2050 is around 8% smaller than today, but it serves an economy more than twice as big and a population with 2 billion more people,” the IEA says in the report.

Wind energy has many advantages. But one challenge is that the amount of energy that is harnessed can change daily based on wind conditions. Finding ways to better capture every part of wind energy is important to Vestas – hence what began last year as the “Grand Challenge,” as the company described it.

A woman works in Vestas’ blades factory in Nakskov, in south Denmark. (Photos courtesy of Vestas)
A woman works in Vestas’ blades factory in Nakskov, in south Denmark. (Photo courtesy of Vestas)

Wind turbines cast a wake, or a “shadow effect” that can slow other turbines that are located downstream, Knudsen says. Energy can be recaptured using wake steering, turning turbine rotors to point away from oncoming wind to deflect the wake.

“The idea is that you control that shadow effect away from downstream turbines and you then channel more wind energy to these downstream turbines,” he says.

To accomplish this, Vestas used Microsoft Azure high-performance computing, Azure Machine Learning and help from Microsoft partner, which used DeepSim, its reinforcement learning-based controller design platform.

Reinforcement learning is a type of machine learning in which AI agents can interact and learn from their environment in real-time, and largely by trial and error. Reinforcement learning tests out different actions in either a real or simulated world and gets a reward – say, higher points – when actions achieve a desired result.

Vestas’ use of Azure high-performance computing also meant getting results faster.

“I was completely blown away that one week into the project, we had an almost minimal live product,” Knudsen says. Vestas used the platform to run simulations to train controllers to react to wind conditions and yaw, or oscillate, to capture energy that otherwise would be lost.

Incorporating AI to maximize clean energy is of growing importance.

“You can use AI to both optimize the construction, siting and the operations of a wind farm, but more importantly, you can use AI to optimize across different systems, both when it comes to consumption but also production,” says Espen Mehlum, head of energy and materials program on benchmarking for the World Economic Forum.

“That’s where the huge untapped potential is for AI – we’re just scratching the surface and seeing the first use cases.”

Espen Mehlum, head of energy and materials program on benchmarking for the World Economic Forum. (Photo courtesy of Espen Mehlum)
Espen Mehlum, head of energy and materials program on benchmarking for the World Economic Forum. (Photo courtesy of Espen Mehlum)

Mehlum was one of the coauthors of a World Economic Forum report last fall, Harnessing Artificial Intelligence to Accelerate the Energy Transition, written in collaboration with BloombergNEF and the German Energy Agency, Deutsche Energie-Agentur.

“AI technology has the potential to rapidly accelerate the energy transition, particularly in the power sector,” the report noted, setting out nine principles for safely and responsibly incorporating AI in the energy transition.

The climate crisis is “all hands-on deck,” Mehlum says. “The world has decided that with the Paris Agreement, and now with the measures that were confirmed at the COP26 meeting late last year in Glasgow, to limit global warming to well below 2 degrees Celsius, and ideally to around 1.5 degrees.

“The increase is already at 1.1 degrees,” he says. “So, the global carbon budget that is left to emit – if you have any hope to reach 1.5 degrees or 2 degrees – is shrinking very fast.”

The New Energy Outlook 2021, BloombergNEF’s annual long-term scenario analysis on the future of the energy economy, notes the importance of employing every wind and solar technology that is feasible to help reduce emissions.

“Getting on track for net zero emissions in 2050 means deploying commercially available abatement technologies in each sector this decade,“ the report says. “More than three quarters of the effort to cut emissions in the next nine years falls to the power sector and to faster deployment of wind and solar PV (photovoltaic). ”

Vestas’ proof of concept is one piece of a very intricate and global challenge toward reducing carbon emissions and maximizing clean energy. For its work, the company was given an Editor’s Choice award for “Best Use of High-Performance Computing in Energy” last fall by HPCwire, a publication for the high-performance computing industry.

“The Grand Challenge was one of the most complicated cases we could find,” says Knudsen. “But it was also a case that we’ve been working on for some time. It’s something we tried with some other collaboration partners on this AI journey. And we haven’t been successful with these other partners. But we were very successful with and Microsoft.”

“There’s no silver bullet for climate change,” Mehlum says. “You have to look at many, many different areas. And AI is just one of those tools that can be very important both to reduce emissions and to optimize access and systems that we haven’t yet fully utilized.”