Solar panels have a love-hate relationship with nature.They need to be placed in exposed locations that get a lot of sunlight, but cloudy weather obviously reduces their production. Less obviously, more extreme weather—from snowstorms to hurricanes—can damage or even break solar hardware altogether. New research performed by Sandia National Laboratories and published in Applied Energy showcases how weather events can reduce the amount of energy produced by the United States’ solar farms.
To study this relationship, the researchers deployed a machine-learning algorithm on large sets of data from private solar farms. “This was a huge, collaborative effort,” Thushara Gunda, one of the paper’s authors and a researcher with Sandia, told Ars. Going forward, Gunda wants to expand this research to look into other extreme weather events and renewables such as wind, geothermal, and marine energy. Her team is in the early stages of this work, she said.
The team hopes that this research could be used to inform decisions about solar operations in the future. This is particularly true as climate change increases the frequency of extreme weather events, potentially causing more issues that impact solar production. “We do recognize that with the shift to renewable energy, there is an increased dependence on local environmental conditions,” Gunda said.
To start, Gunda and her co-author and fellow Sandia researcher Nicole Jackson collected more than 800 maintenance tickets from solar farms in 24 states. They then went back and forth with the solar companies, trying to understand the data sets—for example, different companies would sometimes use different terms for the same thing.
The team had to do a fair bit of parsing to determine what each company meant when they used the term “storm,” as some firms classified snow events or even hurricanes as storms on their maintenance tickets. “Definitely on an industry and every-day practice side of things, a storm can be any non-sunny day,” Jackson told Ars.
“Just because someone wants to share the data doesn’t mean you can automatically analyze it. There are nuances with regard to how the data was collected,” Gunda said.
The researchers also obtained more than two years of electricity-production data from more than 100 solar farms in 16 states, along with historical weather data from those areas. From there, the authors ran a machine-learning algorithm on the data sets to suss out the connection between rates of energy generation and severe weather events. The algorithm allowed the team to identify the points in which the weather-related drops in power matched up with the maintenance tickets and a slew of other variables.
Shedding light on the situation
The team found that snow events caused the greatest reductions in performance (54.5 percent), followed by hurricanes (12.6 percent) and, broadly, storms (1.1 percent). Somewhat surprisingly, hurricanes were mentioned in almost 15 percent of maintenance records. Other factors leading to low performance include the size, age, and location of the plant. “We did see that older farms were more likely to be affected by performance issues,” Gunda said.
There are some nuances to this work, however. For one, older sites being impacted more doesn’t necessarily mean that they’re unproductive—rather, they’ve just been exposed to more weather than their younger counterparts (even older farms are relatively young, between three and five years old). Further, the sites the researchers collected data from were biased toward North Carolina and California. These states tended to have severe weather events that other parts of the US might not have.
The team was also surprised to note that neither hail nor wildfires appeared in the data. This isn’t to say that these events aren’t happening—the West Coast catches fire a lot. Rather, these events were notably absent from the maintenance tickets because the companies only make them when there’s something there for them to do. Most likely, considering that hailstorms are covered by insurance, these events would appear in insurance databases.
“But we know from our conversations with industry and attending conferences that these particular events are certainly of interest,” Gunda said.
Insurance data does indeed tell a story when it comes to how much damage solar farms can face during hailstorms. A report from the National Renewable Energy Laboratory, published last year, uses data gathered from Verisk—an insurance services company—to dig into the amount of damage weather events can cause solar operations. (The insurance data also includes numbers on vandalism and theft).
The data, gathered between 2014 and 2019, suggests that hail caused the largest number of insurance claims with solar hardware, weighing in at 7,979 cases with an average cost of $2,555. “Hail is a big deal for solar panels,” Andy Walker, a senior research fellow at NREL, told Ars.
Fires were less common (1,282 cases), but they had much larger average claims, at $17,309. There were 79 cases of freezing, including ice and snow, averaging $5,288. These averages, however, include the cost of both commercial and residential solar operations. For instance, in the case of residential freezing, the average claim value was $4,195, but it was $32,964 for commercial operations.
Unpublished NREL research also suggests ways in which solar panels can better withstand extreme weather, Walker said. Methods include water-tight enclosures, modules mounted on three rails (rather than two), thicker glass, wind-calming fences, marine-grade steel, and through-bolting (rather than clamps). “It turns out that clamps are the smoking gun in a lot of module liberations, as it’s called when a [photovoltaic] module blows off a rack,” he said.
The upgrades end up costing a few cents for each watt produced, Walker said. Some of these methods can help with a wide variety of the weather events that solar panels will see and increase the magnitude of the threat that the panels can survive—from being crushed by excess snow to being blown off their racks to being bombarded by hail. “Solar panels are one of the most exposed things you’ll find in the built environment,” he added.
Applied Energy, 2021. DOI: 10.1016/j.apenergy.2021.117508 (About DOIs)