Figuring out methods to maximize electricity output from solar panels is a challenge for utilities and solar developers alike. Small variations in daily performance can significantly affect the long-term output of an array.

Now, a new study has outlined the three variables that can help power providers manage expectations and maximize performance of utility-scale plants.

Those three factors are the amount of sun hitting the panels, the use of tracking, and how much load is on the inverter (inverter load ratio). Together, they account for 92% of the performance variation, according to the new report from the Lawrence Berkeley National Laboratory (LBNL).

That means utilities making rate-based investments or signing long-term contracts can better understand where these projects fall in the capacity factor spectrum, allowing them to plan how they will invest with more confidence, the report said.

The report analyzed a sample of 128 utility-scale PV projects across 18 states to analyze their actual performance in the field. The numbers, said study co-author Mark Bolinger, should “instill confidence in investors that the projects, in this sample at least, have largely performed as expected.”

Those numbers also gives an idea to investors as to the profitability of the projects.

“Profitability depends on performance and profitability is what attracts investors,” Bolinger said.

Global horizontal irradiance — the big performer

The first finding in the research is not big news: The strength and concentration of solar energy hitting a panel, technically called the global horizontal irradiance (GHI), accounts for 71.6% of performance variations.

The biggest determinant of GHI is a project’s location. In the LBNL study, California alone accounts for 49% of the sample capacity and 32% of the projects. The top five states in the sample — Arizona, California, Nevada, Colorado, and New Mexico — also have ample sun and space for sites.

The researchers were careful to assess year-to-year data so that seasonal changes did not skew the results, Bolinger said. However, more “subtle questions” remain about the impact of temperature and module type on performance.

“All modules perform better at lower temperatures and most lose capacity factor at higher temperatures,” he said. The paper noted that the model would “presumably be improved with good data on power temperature coefficients and module operating temperatures.”

GHI, or the strength of the solar resource, is the largest determinant in an array's output. It, in turn, largely depends on location. NREL

Tracking vs. fixed racking

Tracking, or the way a panel follows the sun, will also play a part in how well the solar array performs, the study noted. The data included 63 projects with fixed racking and 65 projects built with single-axis tracking.

GHI is measured in kilowatt-hours per square meter per day (kWh/m2/day), and researchers found that projects with tracking could take better advantage of changes in the solar resource.

For each 1 kWh/m2/day increase in GHI, measured in 2014, the model predicts a 4.23% increase in 2014 capacity factor for fixed tilt arrays, compared with a 6.08% increase for projects built with tracking. Arrays built to track exhibit significant increases in capacity factor over fixed tilt arrays, with benefit increasing at sites hosting a strong solar resource.

While those findings are significant, tracking also adds expense to solar projects.

The analysis did not include economic factors, Bolinger said, but tracking is more common in higher GHI states because “you get more bang for your buck tracking a stronger rather than a weaker resource.”

For example, the capacity factor of a fixed-tilt array is at 25%, but “if you add single-axis tracking, you kick it up to 29%,” Bolinger said. “The shift to tracking as the smarter investment may have happened because the cost and reliability of tracking has improved.”

Projects with tracking generally do better than projects without it, but the level of variation depends on where the array is located. LBNL paper

The load on the inverter

The third major factor is the inverter load ratio (ILR), which describes how much direct current generation from a project’s modules is converted to alternating current by the inverters and sent to the grid.

Module prices have fallen faster than inverters in recent years, the LBNL report says, leading some developers to load more modules on each inverter. This means that inverters more commonly run at or near full capacity, which increases the capacity factor (in AC terms) of the entire array. But it can also lead to power “clipping” — an inverter preventing modules from generating at full output to avoid being overloaded.

This clipping, LBNL reports, is most likely to occur during the summer months when the solar resource is strongest, and adding modules and higher-powered inverters can also add cost to arrays. Again, LBNL researchers did not factor in those economics into this calculation, Bollinger said.

“The extent to which ILRs will continue to move higher in the future depends in large part on the relative pricing of modules and inverters (and perhaps even trackers),” the paper reported. But the analysis finds “diminishing marginal returns to higher ILRs.”

The decision on whether to move to a higher ILR may depend on power purchase agreement (PPA) terms, Bolinger said. “Higher price PPAs are more forgiving in terms of the costs you can add.”

Across their entire sample, the LBNL team found that GHI, ILR and the presence of tracking accounted for about 92% of performance variations between arrays. LBNL paper

Finding exact performance: It’s complicated

Interactions between the three key variables can increase or decrease performance, according to the study. For example, tracking or the way a solar panel aligns with the sun’s movement, plays a part in maximizing performance if the project is in a desirable solar site. But tracking can also reduce output when inverters are overloaded and the project is located in a less-than-desirable site, the report said.

For instance, a fixed-tilt project built in 2007 with average solar input and inverter loading ratio will have a capacity factor of 22.02%, the study reported, and adding single-axis tracking boosts the capacity factor by 4.05% to 26.07%.

A third example of the interactivity of the variables is that the benefit of a higher ILR increases with a better global horizontal irradiance, but that benefit can then diminish with tracking. For both fixed-tilt and tracking projects, the capacity factor “increases by 1.00% when moving from an ILR of 1.05 to 1.10, but by only 0.73% when moving from an ILR of 1.45 to 1.50,” the paper reported, likely due to power clipping.

The age of a project also impacts its performance, researchers found. As expected, older projects tend to have lower capacity factors than newer ones, but since the building of utility-scale projects only began in 2007, Bolinger said there was little opportunity to include a meaningful historical perspective on performance.

What utilities need to know

But choosing the site and design to obtain a high capacity factor depends on the price of power, Bolinger said. The installed cost of U.S. utility-scale PV projects continue to drop, with the median price for PPAs falling 50% in 2014 to $3.10/watt.

A PPA price set at $0.15/kWh allows more ease to make those choices, but not every project will be built at a site with a desirable GHI as seen in the Southwest. As PPA prices decline, new projects might not have inverters that allow for high ILRs, which means less capacity and less potential revenue.

However, Bolinger said the main takeaway from the was how these factors can explain 92% of the performance variation.

Utility CEOs should remember that project design choices can encourage or discourage investors, Bollinger added. Projects incorporating design choices that lead to underperformance take longer to pay needed returns. Those that maximize the three variables “can give investors more confidence the project will perform,” he said.