What NREL Says About Solar Panel Degradation

Attribution

This article is written by SolarTech Editorial. It summarizes and links to primary publications from the U.S. National Renewable Energy Laboratory (NREL). NREL did not author this page.

Direct answer

NREL's long-running PV Lifetime Project shows that most modern crystalline silicon modules in its field study degrade slowly, often on the order of a few tenths of a percent per year after early stabilization, but degradation is not uniform across brands and cell architectures. Some module types in the study degraded much faster. NREL publishes annual reports with measured power loss rates (PLR) from modules operated outdoors and flash-tested over time. For homeowners, the practical takeaway is: treat datasheet warranty curves as a floor, and use conservative degradation assumptions in 25-year production models unless you have site-specific data.

What the PV Lifetime Project is

The U.S. Department of Energy launched the PV Lifetime Project in 2016. NREL and Sandia National Laboratories procure commercial module samples, deploy them in outdoor field conditions, and measure performance over time. The 2024 annual report states that more than 950 module samples from 12 manufacturers had been deployed and characterized, with flash testing repeated annually across multiple climates and locations. The project's stated goal is to determine module degradation profiles over time, including uncertainty and differences between module types.

That design matters. NREL is not guessing from lab labels alone. It is measuring real modules aging under sun, heat, rain, and thermal cycling, then publishing results so buyers, insurers, and modelers can ground assumptions in field evidence.

What NREL's field data highlights

NREL reports emphasize several patterns visible across recent annual updates:

NREL's 2025 annual report update notes examples at both ends of the spectrum: some newer module types showed relatively low loss in early field years, while certain heterojunction and other constructions in the same study degraded more rapidly, in some cases above 1% per year annualized over short deployment windows. Those outliers are a reminder that "tier-one" marketing language is not a substitute for measured performance.

How this connects to warranties and modeling

Manufacturer performance warranties typically guarantee a minimum output at year 10 and year 25, often assuming roughly 2% first-year loss and about 0.4% to 0.5% annual loss thereafter on premium lines. NREL field work helps explain why those curves exist: modules do lose output, but the rate depends on technology generation, installation quality, and climate stress.

If you are reviewing a proposal, compare the warranted degradation line on the datasheet to conservative planning assumptions. Our broader guide to reading degradation language is in solar degradation explained. For module selection context, see the complete guide to solar panels.

What NREL does not claim

NREL's PV Lifetime reports describe specific studied samples and locations, not every module sold globally. A passing warranty on paper does not guarantee your roof will match NREL's median curve. Conversely, one noisy year in monitoring does not prove a module is failing; weather normalization matters. NREL publishes technical reports, not product endorsements.

Practical checklist for owners and designers

1. Request the exact module model on the quote and open its performance warranty table. 2. Model at least two degradation scenarios (warranty floor and a conservative +0.1 to +0.2%/yr stress case). 3. Baseline year-one production with monitoring, then trend clear-sky peaks over multiple years. 4. Treat unusually fast decline as a warranty or inspection conversation, not a panic after one dusty month.

Closing

NREL's message for the industry is empirical: measure modules in the field, publish the spread, and improve designs with data. For readers planning a rooftop system, the same spirit applies: use real warranties, monitor real output, and keep assumptions transparent.