Whether it’s improving the accuracy of a medical diagnosis, boosting student outcomes, or advancing autonomous vehicle capabilities, artificial intelligence (AI) has become embedded into many aspects of our lives. Now you can add wind energy to the ever-growing list of industries that are capitalizing on AI and machine learning to drive greater operational efficiencies.
While interest and investment in wind energy has continued to grow (as utilities seek to diversify their energy portfolios to include more clean energy assets) it’s hardly a new technology. Wind energy has been used for thousands of years in regions around the world. Although today’s wind farms utilize sophisticated technology, the underlying methodologies for wind farms have largely stayed the same. As such, in most cases, wind energy producers are not able to maximize their aggregate power output. By combining AI, machine learning, cloud-based computing, advanced power applications, and networking capabilities, however, it is possible to optimize for a healthy boost in energy production - with commensurate Power Purchase Agreement (PPA) or high-demand-period gains. How healthy a boost? The predicted increase in energy output for optimized wind farms ranges from 2-10 percent.
Using Optimization to Boost Megawatt Output
Reducing wind wake, or wake turbulence, is the underlying principle behind integrated wind energy optimization. In traditional wind farms, each turbine is individuallyoptimized. Much like an airplane or boat, each turbine creates its own wake. In this scenario, the wake from the upwind turbines prevents downwind turbines from receiving full energy from the wind stream. This is not an isolated problem: It is estimated that two-thirds of U.S. wind farms experience reduced capacity due to wind wake.
Artificial intelligence enables wake steering, whereby upwind turbines steer the wake away from downwind turbines by adjusting the yaw (or side-to-side) settings of each turbine. In doing so, the turbines can work cooperatively to extract maximum energy from the wind stream.
Wind physics and machine learning algorithms are used to perform complex calculations that determine the optimal yaw settings for all conceivable data combinations, including historical weather and wind patterns, elevation, types of turbines (including thrust and power curves), exact turbine locations and other factors. Specially architected control technology uses a fault-tolerant network to communicate data from each turbine’s wind sensor to the optimization platform every 1-2 seconds. This near-real-time data enables a more accurate calculation of the optimal yaw setting for each turbine within a wind farm. Machine learning is then applied to the data generated from all turbines on the network to further improve optimization models over time.
Beyond increasing a wind farm’s power output, yaw offsets also have the potential to reduce maintenance costs and reliability concerns. It is believed that alleviating stress from wakes may diminish fatigue loading on turbines, thereby improving a turbine’s component lifetime.
The industry is well positioned for wind farm optimization. Just look at the numbers; Overall capacity of all wind turbines installed worldwide is 600 gigawatts, according to the World Wind Energy Association. The market is growing at an amazing rate, with nearly 54 gigawatts added last year. Of this additional generation, 7.6 gigawatts originated from the United States, which is now the second-largest wind power market. Most wind farm operators face the challenge of optimizing megawatt output in order to maximize clean energy output. By using AI and other advanced technologies to adjust yaw settings for each individual turbine, all of the turbines are managed so that they work cooperatively. The end result is the diminished impact of turbulence, and increased power generation across the entire wind farm.
Increasing energy output may also have a positive bottom-line impact. Of a mere fraction of the approximately 450 wind farms in the United States, more than $500 million in incremental revenue opportunities have been identified. Traditional full-scale repowering projects can cost millions and disrupt operations. AI-powered optimization technologies offer a cost-effective alternative that does not require replacing existing wind turbines and control systems. By combining smart investment in optimization with machine learning and AI, wind farm operators can aggregate power output, achieve their financial objectives, and deliver clean power to their communities.
Glen Wagner is Vice President, Power Projects for Emerson’s Automation Solutions business, which helps process, hybrid, and discrete manufacturers maximize production, protect personnel and the environment while optimizing their energy and operating costs.
Jim Kiles is CEO of the VAYU Corporation, a cloud computing service provider for the delivery of optimization and machine learning software services to wind farms and stakeholders.