In this work, we investigate practical approaches of available degradation models and their usage in photovoltaic (PV) modules and systems. On the one hand, degradation prediction of models is described for the calculation of degradation at system level where the degradation mode is unknown and hence the physics cannot be included by the use of analytical models. Several statistical models are thus described and applied for the calculation of the performance loss using as case study two PV systems, installed in Bolzano/Italy. Namely, simple linear regression (SLR), classical seasonal-decomposition, seasonal-and trend-decomposition using Loess (STL), Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) are discussed. The performance loss results show that SLR produces results with highest uncertainties. In comparison, STL and ARIMA perform with the highest accuracy, whereby STL is favored because of its easier implementation. On the other hand, if monitoring data at PV module level are available in controlled conditions, analytical models can be applied. Several analytical models depending on different degradations modes are thus discussed. A comparison study is carried out for models proposed for corrosion. Although the results of the models in question agree in explanation of experimental observations, a big difference in degradation prediction was observed. Finally, a model proposed for potential induced degradation was applied to simulate the degradation of PV systems maximum power in three climatic zones: alpine (Zugspitze, Germany), maritime (Gran Canaria, Spain), and arid (Negev, Israel). As expected, a more severe degradation is predicted for arid climates.

Photovoltaic (PV) systems are the cheapest source of electricity in sunny locations and nearly all European countries. However, the fast deployment of PV systems around the world is bringing uncertainty to the PV community in terms of the reliability and long-term performance of PV modules under different climatic stresses, such as irradiation, temperature changes, and humidity. Methodologies and models to estimate the annual degradation rates of PV modules have been studied in the past, yet, an evaluation of the issue at global scale has not been addressed so far. Hereby, we process the ERA5 climate re-analysis dataset to extract and model the climatic stresses necessary for the calculation of degradation rates. These stresses are then applied to evaluate three degradation mechanisms (hydrolysis-degradation, thermomechanical-degradation, and photo-degradation) and the total degradation rate of PV modules due to the combination of temperature, humidity, and ultraviolet irradiation. Further on, spatial distribution of the degradation rates worldwide is computed and discussed proving direct correlation with the Köppen-Geiger-Photovoltaic climate zones, showing that the typical value considered for the degradation rate on PV design and manufacturer warranties (i.e., 0.5%/a) can vary ± 0.3%/a in the temperate zones of Europe and rise up to 1.5%/a globally. The mapping of degradation mechanisms and total degradation rates is provided for a monocrystalline silicon PV module. Additionally, we analyze the temporal evolution of degradation rates, where a global degradation rate is introduced and its dependence on global ambient temperature demonstrated. Finally, the categorization of degradation rates is made for Europe and worldwide to facilitate the understanding of the climatic stresses.

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The ever‐growing secondary market of photovoltaic (PV) systems (i.e., the transaction of solar plants ownership) calls for reliable and high‐quality long‐term PV degradation forecasts to mitigate the financial risks. However, when long‐term PV performance degradation forecasts are required after a short time with limited degradation history, the existing physical and data‐driven methods often provide unrealistic degradation scenarios. Therefore, we present a new data‐driven method to forecast PV lifetime after a small performance degradation of only 3%. To achieve an accurate and reliable forecast, the developed method addresses the fundamental challenges that usually affect long‐term degradation evaluation such as data treatment, choosing a good degradation model, and understanding the different degradation patterns. In the paper, we propose and describe an algorithm for degradation trend evaluation, a new concept of multiple “time‐ and degradation pattern‐dependent” degradation factors. The proposed method has been calibrated and validated using different PV modules and systems data of 5 to 35 years of field exposure. The model has been benchmarked against existing statistical models evaluating 11 experimental PV systems with different technologies. The key advantage of our model over statistical ones is the ability to perform more reliable forecasts with limited degradation history. With an average relative uncertainty of 7.0%, our model is outstanding in consistency for different forecasting time horizons. Moreover, the model is applicable to all PV technologies. The proposed method will aid in making reliable financial decisions and also in adequately planning operation and maintenance activities.