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

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

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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.

“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.

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