Rating PV Modules for Field Performance

As the solar industry has grown and become more sophisticated in recent years, stakeholders have been basing PV system and procurement decisions on total cost of ownership in addition to—or even instead of—installation cost. Ownership cost and asset valuation now often depend on the terms and conditions associated with the power purchase agreement, as well as how much electricity the PV system is expected to generate throughout its intended operating life, which is largely a function of component quality and system design and maintenance. As a result, PV systems are increasingly evaluated based on dollars per kilowatt- or megawatt-hour, rather than on dollars per watt.

In this article, we present a perspective on how PV module ratings have evolved to reflect the prevailing design and procurement criteria. Many in the industry agree that current nameplate ratings—specifically the power rating under standard test conditions (STC)—taken alone do not adequately represent PV module performance in today’s competitive marketplace. So what should buyers look for in PV module performance characteristics? Many buyers still rely on module ratings under PVUSA Test Conditions (PTC). Are PTC ratings adequate?

We review the motivations for and origin of the PTC module rating. We discuss how PV project valuation practices have evolved away from single-point capacity metrics in favor of more comprehensive measurements and simulation models for predicting PV module performance in the field. In the process, we highlight some promising new energy-based PV module rating methods that industry stakeholders would be wise to adopt going forward.

Evolution from STC to PTC Ratings

For many years, most PV system designers relied on the nameplate or STC ratings of PV modules as a proxy for expected project performance and the resulting return on investment. These ratings are based on PV module performance at standard testing conditions (STC), which are defined in IEC 61215 as 25°C cell temperature, 1,000 W/m2 global plane-of-array irradiance and a solar spectral irradiance at air mass (AM) 1.5. The problem with STC ratings is that the nameplate power rating of any PV module inherently reveals only one aspect of how that product will perform once installed. The reason for this is twofold: PV module—not to mention PV system—performance is largely a function of operating temperature and irradiance, and temperature and irradiance are highly variable and dependent on site location.

History of PTC ratings. Many PV system designers and installers use the PTC ratings system, which traces its origins to the PV for utility-scale applications (PVUSA) research project. The US Department of Energy, as well as local governments and utilities, sponsored this project. A primary goal was to construct, maintain and monitor grid-connected utility-scale projects for evaluation and testing purposes. In 1986, Pacific Gas and Electric Company (PG&E) commissioned the construction of an 86-acre solar farm just outside Davis, California. The large size of the installation (especially for its time) and the scope of its unique monitoring capabilities enabled PVUSA researchers to better understand what happens to PV module and system performance once a system is installed and connected to the grid.

The PVUSA research team developed the PTC rating to evaluate overall system performance over time. The team designed this rating for comparing plant performance as a power-output rating to contractual requirements for the PV system. The rating conditions—1,000 W/m2 irradiance, 20°C ambient (not cell) temperature and wind speed equivalent to 1 meter per second—represent the typical peak-production environment for an installed system in Northern California.

The research team determined the PTC rating by continuously monitoring the PG&E solar farm systems and performing regression analysis on the collected data, and then calculating the power output at rating conditions. This method eliminates the need to test under an exact condition or to scale a measurement taken under different conditions. Since the exact combination of temperature, wind speed and irradiance occurs only rarely, performing regression analysis on continuously monitored data enables more convenient and timely testing. This method also helps to avoid the larger uncertainties associated with scaling a single measured value under arbitrary conditions to a desired point.


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