Publications

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Sandia and Semprius have partnered to evaluate the operational performance of a 3.5 kW (nominal) R&D system using 40 Semprius modules. Eight months of operational data has been collected and evaluated. Analysis includes determination of Pmp, Imp and Vmp at CSTC conditions, Pmp as a function of DNI, effect of wind speed on module temperature and seasonal variations in performance. As expected, on-sun Pmp and Imp of the installed system were found to be ~10% lower than the values determined from flash testing at CSTC, while Vmp was found to be nearly identical to the results of flash testing. The differences in the flash test and outdoor data are attributed to string mismatch, soiling, seasonal variation in solar spectrum, discrepancy in the cell temperature model, and uncertainty in the power and current reported by the inverter. An apparent limitation to the degree of module cooling that can be expected from wind speed was observed. The system was observed to display seasonal variation in performance, likely due to seasonal variation in spectrum.

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Many popular models for photovoltaic system performance (e.g., [1], [2]) employ a single diode model (e.g., [3]) to compute the IV curve for a module or string of modules for given irradiance and temperature conditions. Most commonly (e.g., [4]), parameters are determined using only current and voltage at short circuit, open circuit and maximum power from a single IV curve at standard test conditions, along with reported temperature coefficients. In contrast, module testing frequently records IV curves at a wide range of irradiance and temperature conditions, such as those specified in IEC 61853-1 [5], which, when available, should also be used to parameterize the performance model. We propose a parameter estimation method that makes use of the full range of available IV curves, and demonstrate the accuracy of the resulting performance model. © 2013 IEEE.

Variability of photovoltaic (PV) power output is a potential concern to utilities because it can lead to voltage changes on the distribution system and have other adverse impacts on power quality unless additional equipment is added or operational practices are changed to mitigate these effects. This paper develops and evaluates a simple yet novel approach for quantifying irradiance variability over various timescales. The approach involves comparison between measured irradiance and a reference, clear sky irradiance, determined from a model. Conceptually, the "Variability Index" is the ratio of the "length" of the measured irradiance plotted against time divided by the "length" of the reference clear sky irradiance signal. Adjustments are proposed that correct for different measurement intervals. By evaluating the variability index at several sites, we show how annual and monthly distributions of this metric can help to classify sites and periods of time when variability is significant. Copyright © (2012) by American Solar Energy Society.

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