In the United States, a new wind turbine is erected every two and a half hours. In 2016, wind energy accounted for 5.6% of the total electricity generated—while it is over twice the amount produced by wind in 2010, it is still a long way from its potential.
A research team from The University of Texas at Dallas (UT Dallas) has developed a new method for extracting more energy from the wind. This approach can potentially enhance wind power generation considerably, resulting in increased revenue. Nearly 6%–7% of potential increases have been predicted by numerical simulations carried out at the Texas Advanced Computing Center (TACC).
The researchers believe that it is possible to produce the equivalent of $100 million in value on applying 1% development to all wind farms in the country. Hence, this new approach could potentially produce $600 million in additional wind power on the national scale.
In December 2017, the observations made by the team were reported in Wind Energy and Renewable Energy.
In fluid dynamics - a branch of physics - turbulence is a commonly modeled through large eddy simulations. Many years ago, a research team led by Stefano Leonardi produced models capable of integrating physical behavior across an extensive range of length scales—from tips of a blades having thickness in the range of centimeters, to turbine rotors 100 m in length—and accurately predicting wind power by means of supercomputers.
“We developed a code to mimic wind turbines, taking into account the interference between the wake of the tower and the nacelle [the cover that houses all of the generating components in a wind turbine] with the wake of the turbine rotor,” stated Leonardi, associate professor of mechanical engineering and one of the authors of the Wind Energy paper, which was selected for the cover.
Another difficulty besides the range of length scales is modeling the variability of wind for a specified region at a particular time. In order to tackle this, the team incorporated the Weather Research and Forecasting Model (WRF)—a leading weather prediction model developed at the National Center for Atmospheric Research—into their code.
We can get the wind field from the North American Mesoscale Model on a coarse grid, use it as an input for five nested domains with progressively higher resolution and reproduce with high fidelity the power generation of a real wind farm.
Leonardi, Associate Professor of Mechanical Engineering
Thanks to the growing power of computers, Leonardi and his colleagues could accurately model the wind field on a wind farm and the power production of every turbine. A 90% consistency between their predictions and the turbine’s efficiency was observed when the results of their model were tested against data from a wind farm in North Texas. Their results will be presented at a key wind energy research conference—Torque 2018.
Taking the Turbulence out of the Optimization Control Algorithm
The flow of wind is simply not smooth and unidirectional. It has wakes and turbulence which increase on grouping the turbines together, as on a wind farm. As reported by the U.S. Department of Energy, wake interactions are responsible for losses of up to 20% of annual production. It is essential to study the effects of turbulence on energy generation to adjust the behavior of the turbines in real-time, in order to obtain maximum power.
The control algorithms used to manage the operation of dynamic systems at wind farms were tested using their modeling capabilities. Among these were the control algorithms called extremum seeking control, a model-free way of acquiring the best performance out of dynamic systems even with limited knowledge of the system.
“Many thought it would not be possible to use this approach because of turbulence and the fact that it provides a situation where turbines are changing all the time,” Leonardi stated. “But we did a huge number of simulations to find out a way to filter turbulence out of the control scheme. This was the major challenge.”
With the help of extremum seeking control, the system—even as it measures the power and calculates the gradient—increases and reduces the rotational speed of a spinning turbine blade. This is repeated until the optimal operating speed is evaluated by the controller.
The important thing is that the control algorithm does not rely on a physics-based model. There are many uncertainties in a real wind farm, so you cannot model everything. The extremum seeking control can find the optimum no matter if there is erosion or icing on the blades. It's very robust and works despite uncertainties in the system.
Leonardi, Associate Professor of Mechanical Engineering
Simulating the Wind
To assess their new method, the group ran virtual wind experiments with the help of supercomputers at the TACC, including Lonestar 5 and Stampede2—two of the most robust across the globe. They could use these systems through the University of Texas Research Cyberinfrastructure (UTRC) initiative that, from 2007, has offered researchers from any of the University of Texas System’s 14 institutions access to TACC’s training, expertise, and resources.
Since wind turbines are expensive to construct and operate and not many wind research facilities are available to scientists, access to powerful supercomputers is essential.
“The benefits of using high performance computing to create a virtual platform for doing analyses of proposed solutions for wind energy are enormous,” stated Mario Rotea, professor of mechanical engineering at UT Dallas, who is also the site director of the National Science Foundation-supported Wind-Energy Science, Technology and Research (WindSTAR) Industry-University Cooperative Research Center (IUCRC). “The more we can do with computers, the less we have to do with testing, which is a big part of the costs. This benefits the nation by lowering the cost of energy.”
Although the application of extremum seeking control to wind farms has not yet been field-tested, the UT Dallas researchers already applied the approach to a single turbine at the National Renewable Energy Laboratory (NREL).
“The NREL test gave us experimental data supporting the value of extremum seeking control for wind power maximization,” added Rotea. “The experimental results show that extremum seeking control increases the power capture by 8-12% relative to a baseline controller.”
Since the computational and experimental results were positive, the UT Dallas team is intending to conduct an experimental campaign with a cluster of turbines in a wind farm.
Collaborations and Next Steps
The advancement of the fluid dynamics model for wind turbines was part of an international partnership between four U.S. institutions (Johns Hopkins University, UT Dallas, Texas Tech, and Smith College) and three European institutions (Technical University of Denmark, École polytechnique fédérale de Lausanne, and Katholieke Universiteit Leuven) funded by the National Science Foundation through a Partnerships for International Research and Education (PIRE) award.
Moreover, through the WindSTAR center, Leonardi, Rotea, and team at UTD partnered with nine major wind energy companies and equipment manufacturers. These companies are keen on commercializing or adopting the study.
The members of our center do not have access to a lot of horsepower in terms of HPC [high-performance computing]. The computers at TACC are an asset for us and give us a competitive advantage over other groups. In terms of solving actual problems, we create control systems that they may incorporate, or they may use HPC to develop new tools for forecasting wind resources or determine if there are turbines that are not performing.
Mario Rotea, Professor of Mechanical Engineering at UT Dallas
Besides developing the new control strategies and turbulence algorithms, the WindSTAR team members have established methods to accurately predict results on less powerful computers (work featured in the March 2018 issue of Wind Energy) and to determine the optimal distance between turbines to increase profits, with respect to the cost of land (presented at the 2018 Wind Symposium).
The long-term impacts of the research are not merely theoretical.
“The research allows us to optimize wind energy power production and increase the penetration of renewable energy in the grid,” stated Leonardi. “There will be more power generated by the same machines because we understand more about the flow physics in a wind farm, and for the same land use and deployment, we can get more energy.”