Energy models are only as good as the assumptions behind them.
And right now, one of the most widely used assumptions in utility-scale solar—how we model wind—may be quietly underestimating risk.
Relying on hourly wind data can underpredict wind stow energy losses by up to 4% annually. For utility-scale solar developers and asset owners, that’s not a rounding error—it’s a potential gap in revenue expectations, financing assumptions, and long-term performance.
The good news: this gap is measurable—and increasingly, fixable.
What is wind stow modeling and why does it matter for project economics?
Wind stow modeling estimates how often solar trackers enter protective stow positions due to high wind speeds and the resulting energy loss. Improving wind stow modeling can help developers increase solar project energy yield accuracy and provide greater visibility into [some of the uncertainty inherent in] financial forecasts.
When wind speeds exceed defined thresholds, single-axis trackers move into a stow position to reduce structural risk.
That protection comes at a cost:
- Reduced generation during stow events
- Compounding energy losses over time
- Increased uncertainty in P50/P90 (probability of achievement) estimates
For utility-scale projects, even modest errors in estimations can translate into meaningful financial impacts over the 30+ year life of the asset.
Why is wind stow modeling systematically underpredicting losses?
Wind stow modeling often underpredicts losses because standard industry datasets use hourly average wind speeds, which may fail to capture short-duration gusts that actually trigger solar tracker stow behavior.
The industry’s default approach relies heavily on Typical Meteorological Year 3 (TMY3) hourly wind data.
But here’s the disconnect:
- Tracker stow behavior is often driven by short-duration wind conditions—such as 3-second gusts or 1-minute averages—rather than hourly averages
- TMY3 represents hourly averages
That mismatch matters.
In practice:
- Real-world wind conditions frequently exceed stow thresholds
- Hourly datasets often do not reflect those exceedances
As shown in the analysis, hourly wind data can completely miss the conditions that trigger stow—meaning models may underestimate stow events.
Hourly TMY3 wind data (blue) smooths out short-duration gusts and may not exceed tracker stow thresholds (green), while 3-second gust wind speeds (red) frequently exceed those thresholds and can trigger stow events.
Why do rare wind events have such an outsized impact on energy loss?
Rare high-wind events disproportionately impact energy loss because they trigger stow conditions, even though they represent a small percentage of total wind data, making them difficult to capture accurately in standard modeling approaches.
Here’s the core modeling challenge: ~90% of wind data falls outside extreme gust conditions—yet those rare events drive a disproportionate share of wind stow energy losses.
Distribution of 1-minute peak wind speeds measured at 10 meters at NREL’s Flatirons Campus (2001–2026). While most wind conditions occur at relatively low speeds, infrequent high-wind events can drive a disproportionate share of tracker stow events and associated energy losses.
So while most datasets accurately represent “normal” conditions, they struggle with:
- Frequency of threshold exceedance
- Duration of stow events
- Clustering of gust events over time
This creates a blind spot where low-frequency, high-impact events are underrepresented—but financially significant.
How does wind data resolution affect modeling outcomes?
Wind data resolution directly affects modeling outcomes because higher-frequency datasets capture gust events more accurately, leading to more realistic predictions that are more representative of real-world solar tracker stow behavior and associated energy losses.
When you compare datasets side by side, the difference becomes clear:
- Hourly (TMY3): Smooth, averaged, often below stow thresholds
- 5-minute or sub-hourly data: Captures variability and threshold crossings
- Measured gust data (seconds/minutes): Most accurate representation of actual tracker response
In fact, comparisons between hourly datasets and higher-frequency time-series data show significantly more stow events in real-world conditions.
And that translates directly into higher—and more realistic—loss estimates.
How do passive vs. active tracker systems change the modeling approach?
Not all solar trackers respond to wind the same way and that difference matters in modeling.
Across the industry, most systems fall into two general categories:
- Control-driven (active) stow systems that use sensors, wind measurements, and control logic to initiate stow, and are often modeled using defined wind speed thresholds.
- Mechanically responsive (passive) stow systems that respond dynamically to real-time wind forces through mechanical design and require modeling of continuous physical behavior rather than discrete triggers.
ARRAY has implemented both approaches across its portfolio—including systems like ARRAY DuraTrack® and ARRAY STI H250® trackers—each engineered for different site conditions and risk profiles.
The key takeaway isn’t which approach is used but rather how that approach is modeled.
Because tracker design influences:
- Frequency and timing of stow events
- Sensitivity to short-duration gusts
- Duration and recovery from stow conditions
- Overall wind stow energy losses
Applying a one-size-fits-all modeling assumption—especially one based on low-resolution wind data—can overlook these differences and lead to less representative energy yield predictions.
What does more accurate wind stow modeling look like in practice?
More accurate wind stow modeling incorporates high-frequency wind data, applies gust corrections such as Durst curves, and validates model outputs against real-world tracker performance to better estimate wind stow energy losses.
Closing the gap requires three key upgrades:
1. Higher-frequency wind inputs
Using datasets like:
- ERA5 sub-hourly reanalysis
- 5-minute historical datasets
- Measured gust data where available
2. Gust translation methods
Applying Durst-curve corrections to convert between averaging intervals and better approximate gust behavior
3. Field validation
Comparing modeled outputs against:
- Real-world tracker response
- Measured stow events
- Site-specific wind conditions
Together, these steps create a more transparent and data-informed modeling framework.
What do regional differences tell us about wind stow risk?
Regional differences in wind patterns lead to significant variation in wind stow energy losses, reinforcing the need for site-specific modeling approaches rather than relying solely on generalized datasets.
Wind risk isn’t uniform—and neither are the losses.
The map below shows how modeled wind stow losses can vary across the U.S. when using higher-resolution time-series data instead of hourly TMY inputs. Some regions show materially higher losses when modeled with time-series data while others remain relatively stable.
Difference in modeled wind stow energy losses when using 5-minute time-series wind data versus traditional TMY3 hourly inputs across the U.S. Lighter regions indicate larger differences between modeling approaches, highlighting where hourly wind data may underestimate wind stow losses.
This variability reinforces a critical point: Wind stow modeling is often more effective when it is site-specific, data-informed, and validated—not generalized.
How does improved wind stow modeling translate into better project outcomes?
Improved wind stow modeling helps reduce uncertainty in energy estimates, enabling more informed financial projections, better technology selection, and stronger alignment between developers, EPCs, and asset owners.
When modeling improves, decision-making improves.
Developers and owners can:
- Build more informed P50/P90 cases
- Reduce downside risk in financing
- Better evaluate tracker technologies and configurations
- Align expectations across stakeholders
Understanding how passive or active tracker systems respond to real wind conditions allows for more informed tradeoffs between performance and protection
The result isn’t just better modeling.
It’s more confidence earlier in the project lifecycle—when it matters most.
Wind stow losses aren’t a new phenomenon. But the way we model them is evolving. As projects move into more complex environments and margins tighten, the industry is shifting toward:
- Higher-resolution datasets
- Transparent modeling assumptions
- Field-validated frameworks
Because the goal isn’t to eliminate uncertainty. It’s to understand it well enough to make better decisions.
Reach out to an ARRAY representative today, to see how wind stow losses could impact your project.
Note: Actual performance may vary based on site conditions. All claims based on ARRAY analysis and field data unless otherwise noted.
Click here for other articles by this author