In a series of field tests performed at the Max Planck Institute for Chemical Energy Conversion (MPI-CEC), key parameters in solar panel performance were measured to gather data that can be used for choosing the most appropriate panel option for local conditions in terms of long-term performance. Five inorganic photovoltaic (PV) technologies representative of current options on the market were installed and measured: micromorph thin-film silicon, cadmium telluride, copper-indium-gallium-selenium, polycrystalline silicon, and amorphous silicon. In testing solar panels, the sun’s intensity, the spectral composition, and the angle of light are important factors in understanding why certain panels are successful and others degrade more quickly, the researchers said. Tests must include many parameters beyond just temperature. To compensate for the knowledge gap in degradation mechanisms for various PV types, the researchers performed tests over a five-year period during which they collected weather data and panel performance information. The PV field-testing facility located at the Max Planck Institute for Chemical Energy Conversion (MPI-CEC), located in Mülheim an der Ruhr, Germany. The rooftop installation includes five different inorganic PV technologies. Courtesy of Thomas Hobirk, MPI-CEC. The degradation rates of module performance were computed from the obtained PV power normalized by both recorded and modeled solar irradiance. The results emphasize the relevance of using modeled irradiance data in addition to recorded solar irradiance to extract reliable degradation rates, the researchers said. The researchers employed an open-source testing methodology created by the National Renewable Energy Laboratory and Sun Power Corporation called clear sky irradiance — that is, the expected solar irradiance at a given location in ideal clear-sky conditions. They compared performance ratios based on measured “real-world” data and data modeled using clear sky irradiance to show the difference between data sets, highlight data inconsistencies, and report accurate performance over time. Robust degradation rates were extracted from experimental power data, based on modeled clear-sky irradiance and a combination of aggregation and regression strategies. The results showed distinctive degradation behaviors of the five available commercial PV modules in response to the local conditions. “Our study highlights that one of the proposed methods of tackling this problem, that is, applying the irradiance mask, might add bias to the data without decreasing the spread,” researcher Peter Kraus said. “What we were surprised by was that a simple data aggregation to a longer time interval, coupled with the year-on-year method for calculating degradation rates, yielded reasonable results that were validated when the pyranometer data was excluded.” Pyranometers — sensors used to measure sunlight irradiance — can be prone to errors when not regularly calibrated. International Electrotechnical Commission standards for PV degradation in solar panels do not include field tests. The researchers said that while some testing facilities have made data available, much of the data needed to make business decisions for PV is not available publicly. They plan to continue to produce detailed data on the MPI-CEC PV plant to expand the data set over longer periods of time and make raw performance data available to the public, so it can be used to improve the technology. The research was published in the Journal of Renewable and Sustainable Energy (www.doi.org/10.1063/1.5128171).