Spinning the Data for Small Wind Turbines
First-of-its-kind study uncovers data to improve modeling tools for tinier turbines
The rising costs of electricity are placing a heavier burden on lower-income, African American and Latinx, and multifamily households.
More than 150 megawatts of small distributed wind turbines—either in standalone systems or used to complement other forms of electricity generation—are installed across the country. Stationed in underrepresented communities, these turbines can help even the energy equity score with improved energy accessibility and security.
Bringing these turbines to communities and individual residential homes and businesses depends, in part, on how accurate predictions are for how much energy would be produced at potential sites. In other words, will the energy produced be worth the upfront costs of investing in and installing these localized turbines?
The challenge is that wind energy models focus on either heights near the ground surface—10 meters or less—or at utility-scale wind turbine hub heights of 80 meters or higher. Turbines for home, business, or small community use are generally 30 to 50 meters tall.
Now, a research team led by the Pacific Northwest National Laboratory (PNNL) has revealed data that will improve wind modeling for these turbines and bring greater accuracy that will help consumers and others make the most informed investment decisions.
The results of the study, which was supported by the Department of Energy’s Wind Energy Technologies Office, were published in Wind Energy Science.
A two-fold study
The first-of-its-kind study by the team—which included wind energy experts from the National Renewable Energy Laboratory—was two-fold.
First, they assessed the accuracy of three wind models that are available to the distributed wind turbine industry. The goal was to gain a more complete understanding of simulated wind speed performance at the distributed wind turbine hub height—the area where the blades are attached. This information would help to identify areas where improvements to these models were needed.
Secondly, the team evaluated four affordable, user-friendly tools that simulated energy production to produce annual energy production estimates for specific sites. For this part of the study, they used actual distributed wind turbine performance data from across the United States to assess the four tools.
“Evaluating the wind resource models and simulation tools will help educate potential buyers of distributed wind turbines about the limitations of wind generation estimates so they can adjust their expectations accordingly,” said Lindsay Sheridan, the PNNL Earth scientist who led the study.
Modeling wind speed performance
Relative to wind speed accuracy, the team found that the wind models yielded only small differences in simple landscapes. For example, differences were noted within half a meter per second—the unit for measuring wind speed—and a mean absolute error, or the magnitude of error between modeled and observed data, of less than two meters per second.
“However, in more complex terrain—for example, deep, hilly areas—consumers who plan to adopt distributed wind turbines should expect wind speed differences and mean absolute errors of up to five meters per second from the models,” said Sheridan.
The results also showed that existing wind models overestimate wind speeds when winds are low, slower than five meters per second. This lower-speed range includes typical wind turbine “cut-in” speeds—when the blades start rotating and producing power—so an overestimation in wind speed can lead to an overestimation in the amount of energy production.
Evaluating the wind resource models and simulation tools will help educate potential buyers of distributed wind turbines about the limitations of wind generation estimates so they can adjust their expectations accordingly. - Lindsay Sheridan, PNNL Earth scientist
Conversely, the team found that when wind speeds are higher, the three wind models underestimate wind speeds, which can impact energy production estimates by underpredicting the amount of time spent at peak power as well as underestimating the number of turbine “cut-out” events—when turbines stop rotating and producing power to protect themselves against the most extreme winds.
Energy production estimation tools
Using existing small wind turbine energy generation data shared from systems across the United States, the team found that the four energy production estimation tools, which include wind resource data and loss assumptions, overpredicted turbine performance.
They discovered that two of the tools overestimated observed capacity factor within five percentage points, while the other two tools overestimated it by 11.5 and seven and a half percentage points. Capacity factor is the ratio of actual wind generation over a period of time against what the turbine would have generated if it were operating at rated capacity—the turbine’s power output at 11 meters per second—over that same period of time.
The tools also showed challenges in areas of complex terrain, overestimating or underestimating observed turbine capacity factors by more than 10 percentage points in some regions.
Recommendations for distributed wind turbine users
Given the significance of the variability of turbine energy production estimates, the team recommends that those who estimate wind energy production from distributed wind turbines select a tool that provides a range of annual production possibilities to set expectations for average-, high-, and low-wind production years.
Additionally, they suggest customizing complex terrain characteristics in the models where needed to improve net turbine production estimates.
“Collecting onsite observational wind resource data to evaluate energy generation potential is often too time and cost prohibitive for customers interested in installing small wind turbines for home, business, or small community use,” said Sheridan. “The tools we evaluated provide quick, low-cost energy production estimates that can be adjusted for their individual use from our findings.”
She adds, “Reducing the uncertainties of these models and tools can improve the confidence of those making decisions on whether to invest in distributed wind turbine systems as well as increase financing opportunities, for example, incentives, for small wind projects.”
In addition to helping set expectations for small wind turbine users, the team’s results provide baselines of comparison for future versions of wind speed models and energy production estimation tools.
Published: November 7, 2022