• Energy Research

Abstract The interest in the assessment of performance loss rate (PLR) of Photovoltaic (PV) modules and arrays has been increasing as long as the global installed power expands and ages. Reliable performance metrics, statistical methods and filtering techniques exploiting continuous outdoor measurements are therefore needed, in order to foster solar bankability of PV systems. This work presents an improved estimation method to decrease the uncertainty associated to PLR assessment by (a) using the array generated power metric corrected to Standard Test Conditions (STC), namely P max , STC , to minimize seasonal oscillations, (b) applying a filtering technique to eliminate outliers and (c) performing linear interpolation on P max , STC monthly averages series. Estimated PLR and its uncertainty are assessed using three-years data from twenty-four grid-connected PV arrays representing nine different PV technologies and results are compared with two other widely-recognized performance metrics, namely: the Array Performance Ratio ( PR a ) and the Array Photovoltaic for Utility Systems Applications ( PVUSA a ). Results show (a) that adding spectral correction to irradiance and temperature correction reduces the uncertainty of 25% on average and (b) that the uncertainty associated to P max , STC metric is reduced to more than 60% on average with respect to the other investigated metrics for crystalline silicon-based technologies, while it is comparable in the case of thin-film technologies. Finally, two procedures estimating the first year PLR and the the first five months PLR are presented and discussed.

Photovoltaic (PV) module temperature predictions are crucial to accurately assess the efficiency of PV installations. In this study we focus on the cooling effect of wind on PV cell temperature. We show that for most of the technologies installed at a PV test facility in Bolzano (Italy), models including wind data predict PV cell temperature better than standard approaches which do not include wind data. Moreover, we show that wind data from numerical weather prediction models can replace in-situ wind measurements: when they are used as model input, the prediction also improves significantly compared to the standard approach. European Geosciences Union General Assembly 2013, EGUDivision Energy, Resources & the Environment, ERE Energy Procedia, 40 ISSN:1876-6102