Massive wind farms change wind patterns

Large wind farm installations create atmospheric disruptions that current prediction models fail to capture, leading to costly overestimations of power generation.

Researchers from the University of British Columbia Okanagan and Delft University of Technology have identified a critical flaw in how the wind energy industry forecasts wind farm performance. The discovery could save operators millions of dollars while improving renewable energy efficiency.

Current models miss key interactions

The problem centers on the atmospheric boundary layer. This is the section of the atmosphere where wind speed, temperature, and pressure change with altitude.

As wind flows through massive installations, these installations don’t simply harvest energy. They fundamentally alter the structure of incoming wind patterns.

Current engineering models fail to account for how large wind farms modify approaching airstreams. This oversight creates a significant overestimation of power generation capabilities.

“Financially disastrous” miscalculations

“Wind farms are getting so large that they can actually alter the structure of the incoming wind,” explained Dr. Joshua Brinkerhoff, an associate professor in UBC Okanagan’s School of Engineering.

The atmospheric boundary layer monitors how wind speed, temperature, and pressure vary with altitude. Understanding these changes is crucial for accurate power forecasting.

“The most significant finding is that our model can capture the interaction between large wind farms and the oncoming wind. To date, this hasn’t been captured properly, leading to overestimation of how much power a wind farm will produce. This kind of overestimation is ‘financially disastrous’ for the wind farm operators,” said Dr. Brinkerhoff.

Revolutionary modeling framework emerges

To solve this challenge, the research team developed TOSCA. The Toolbox for Stratified Convective Atmospheres represents an open-source modeling framework.

TOSCA captures the complex interactions between wind farms and atmospheric conditions with unprecedented accuracy. The framework addresses two significant challenges currently facing the wind energy industry.

First, it simulates boundary layer turbulence over large areas. Second, it models entire wind farms under realistic atmospheric flow conditions.

Optimizing turbine placement strategies

Better wind prediction models enable strategic turbine placement within farms. This optimization maximizes energy output while minimizing environmental impact.

Fine-tuning individual turbine locations within wind farm groupings proves paramount to power output. Poor design leads to generation shortfalls, making wind farms uneconomical.

“The results of this research will lead to a better understanding of potential wind farm power estimates and an increase in their energy outputs. This new modeling framework can serve as a roadmap for the industry,” said doctoral student Sebastiano Stipa, who conducted research at Delft University as part of a Mitacs Globalink exchange.

Global renewable energy implications

Wind energy plays an increasingly vital role in renewable energy portfolios worldwide. Accurate forecasting becomes essential as installations grow larger and more numerous.

This research represents a significant step toward making wind farms more efficient and economically viable. The open-source nature of TOSCA ensures widespread accessibility for industry adoption.

The framework’s ability to predict wind installation interactions with atmospheric gravity waves offers new insights into farm-scale physical phenomena. These capabilities extend beyond individual installations to cluster wake interactions between multiple wind farms.

Research collaboration success

The international collaboration between UBC Okanagan and Delft University demonstrates the power of cross-border scientific partnership. Computational resources came from the Digital Research Alliance of Canada and Advanced Research Computing at the University of British Columbia.

Research support included funding from Mitacs Globalink, UL Renewables, and the Natural Science and Engineering Research Council of Canada.

The study was published in the journal Wind Energy Science.