In this study, we investigate the performance ratio (PR) of about 100 German photovoltaic system installations. Monitored PR is found to be systematically lower by~2-4% when calculated with irradiation data obtained by pyranometers (henceforth denoted as PR Pyr ) as compared with irradiation amounts measured by reference cells (denoted as PR Si ). Annual PR Si for the~100 systems is found to be between~70% and~90% for the year 2010, with a median PR of~84%. Next, simulations were performed to determine loss mechanisms of the top 10 performing systems, revealing a number of these loss mechanisms may still allow for some optimization. Despite the fact that we do not see such values from our monitoring data base up to now, we believe PR Si values above 90% are realistic even today, using today's commercially available components, and should be expected more frequently in the future. This contribution may help in deepening our knowledge on both energy loss mechanisms and efficiency limits on the system level and standardization processes of system-related aspects.

Yield predictions are performed to predict the solar resource, the performance and the energy production over the expected lifetime of a photovoltaic (PV) system. In this study, we compare yield predictions and monitored data for 26 PV power plants located in southern Germany and Spain. The monitoring data include in‐plane irradiance for comparison with the estimated solar resource and energy yield for comparison with predicted performance. The results show that because of increased irradiance in recent years (‘global brightening’) the yield predictions systematically underestimate the energy yield of PV systems by about 5%. Because common irradiance databases and averaging times were used for the yield predictions analysed in this paper, it is concluded that yield predictions for areas where the global brightening effect occurred in general underestimated the energy yield by the same magnitude. Using recent satellite‐derived irradiance time series avoids this underestimation. The observed performance ratio of the analysed systems decreases by 0.5%/year in average with a relatively high spread between individual systems. This decrease is a main factor for the combined uncertainty of yield predictions. It is attributed to non‐reversible degradation of PV cells or modules and to reversible effects, like soiling. Based on the results of the validation, the combined uncertainty of state of the art yield predictions using recent solar irradiance data is estimated to about 8%. Copyright © 2015 John Wiley & Sons, Ltd.

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In the framework of the H2020 SERENDI‐PV project, it is aspired to tackle challenges in photovoltaic (PV) modeling and yield simulations, that are emerging today, on four interrelated aspects: i) improved modeling of loss/degradation mechanisms, ii) improved modeling of bifacial PV, floating PV, and building integrated photovoltaics systems, iii) solar resource and uncertainties modeling, and iv) financial risks modeling. As groundwork for this effort, a comprehensive 8‐month study is carried out, the results of which are presented in this article. The study has two parts and main objectives: i) a comprehensive survey addressed to multiple stakeholders, to identify and assess today's “best practices” and needs of the PV industry on PV energy yield simulations; ii) a multi‐model multi‐case benchmarking and evaluation study, i.e., of eight state‐of‐the‐art tools/software for PV energy yield simulations of seven real‐life PV systems addressing diverse “scenarios” (different climates, site characteristics, PV typologies, and technologies).

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