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Utility-scale solar moves over hills and under clouds (reducing power production)

How SmarTrack™ machine learning improves solar energy gains

4 minute read

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Historically, utility-scale solar sites have been built in locations with plenty of sunny days and flat plots of land requiring minimal grading. Developers preferred to build photovoltaic (PV) installations in places like Southern California, Arizona, and Texas, where cloudy weather is rare and open land is abundant. 

In the most recent US Solar Market Insight®, an analysis of publicly available data found that the US PV installed capacity (MWdc) increased by 117% between 2017 and 2021.

The wide deployment of solar over the last few years has made finding those ideal sites for maximum energy yields more challenging. This is especially true as PV development expands into parts of the US like the Northeast, Midwest, and South, where potential sites that are flat with consistent sunshine are few and far between.

Take North Carolina, for example. The cumulative installed PV capacity for the state rose from 4,195.1 (MWdc) in 2017 to 7,497.3 (MWdc) in 2021. This Southeast state isn't usually associated with large areas of flat, sunny land but rather mountains and various seasons.

One way to optimize energy production is with single-axis trackers that position solar panels where they will receive the maximum amount of direct sunlight throughout the day. However, two common challenges utility-scale solar sites face are:

  1. Cloudy days, when only diffuse light is available, thereby reducing energy output, and
  2. Undulations in site topography, which may cause shading between rows of solar trackers during backtracking hours, thus dampening energy yields

From the perspective of solar site owners and operators, a reduction in energy production means a decline in profitability. Therefore, exploring new territory for PV installations requires new solutions for these site-specific issues. 

How one company is working to produce more energy on hilly terrain

Since the early 1990s, solar trackers have used a control algorithm called backtracking to avoid row-to-row shading by adjusting the solar panels to shallower angles when the sun is low. Unfortunately, the standard algorithm assumes perfectly flat topography and no installation variability.

But for real-world installations with undulations, standard backtracking may end up causing the very thing it's meant to prevent and, in turn, non-linear power losses for crystalline silicon modules.

To optimize tracker position on sites with varied topography, Array Technologies developed the Array SmarTrack™ Backtracking algorithm using a proprietary machine learning approach, allowing utility-scale solar sites to minimize shading and maximize production during backtracking hours.

Accounting for site-specific variables—like row height differences, east-west slopes, installation tolerances, monofacial or bifacial module use, to name a few—SmarTrack™ Backtracking is able to quickly "learn" and apply an optimal strategy for a site after just two sunny mornings and two sunny evenings. Moreover, these parameters are valid for the life of the project, and since the system uses on-site measurements and the algorithm accounts for seasonal variability, no additional sensors or site visits are required.

"What SmarTrack™ is doing, regardless of the topography or whether the posts are installed perfectly in line with each other, is finding the tracker angles for optimum power output. A traditional algorithm that just looks at the time of year and the solar angles to calculate it might miss some of those real-world details about the site," says Coleen Mahoney, Vice President of Product Management at Array Technologies.

Produce more energy in cloudy areas

On days when the skies are clear, sites will point panels at the sun to maximize power production. But on cloudy days, when the incident irradiance is coming from all directions, it's better to position your panels at a flatter stow angle. 

For SmarTrack™ Diffuse, Array installs a SmarTrack™ Controller on the site's network for real-time analysis and continuously monitors on-site data to make diffuse logic decisions.

During operating hours, the SmarTrack™ Controller frequently examines quality-checked irradiance data and decides if the sunlight is diffuse enough that going to a flatter angle will increase production. Array's proprietary diffuse algorithm includes checks and limits that mitigate the risk of the trackers being out of position when the clouds clear.

Sanket Shah, Array Technologies Senior Product Manager, describes it this way, "Trackers may need to move to a shallower angle, increasing the module's view of the sky dome during cloudy conditions. The SmarTrack™ Controller continuously reviews the data, and if it finds an opportunity where the tracker can produce more power in a flat position compared to normal tracking, it'll move the trackers into diffuse stow." 

An added benefit of SmarTrack™ is that it also protects against cybersecurity concerns by not requiring ongoing communication between the site network and external entities.

Verified energy gains and lifetime costs for operators

Independent firm DNV reviewed two sites that implemented SmarTrack™ Backtracking, verifying annualized gains of 3.61% on a monosloping site in New Mexico and 3.15% on an undulating site in Arizona.

A separate DNV study found energy gains of 0.60% on a New York site using SmarTrack™ Diffuse during a period with a 33.5% diffuse fraction. DNV noted that higher gains are possible with higher slopes using SmarTrack™ Backtracking or higher diffuse fraction using SmarTrack™ Diffuse.

Sub-optimal construction and long-term operation can affect profitability over a site's lifetime. Therefore, an accurate view of a site's projected energy production and optimization potential is essential for site operators.

Contact Array to find out how much gain SmarTrack™ can offer for your site, whether it's in development or already commissioned.

For more on Array Technologies and SmarTrack™, visit their website.