Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Photovoltaic Lifetime Forecast: Models for long-term photovoltaic degradation prediction and forecast

: Kaaya, Ismail
: Weiß, Karl-Anders; Sidrach de Cardona Ortín, Mariano

Málaga, 2020, VIII, 76 pp.
Málaga, Univ., Diss., 2020
Fraunhofer ISE ()

As the share of photovoltaic keeps increasing in the global electricity mix, it becomes critical to assess how the overall system and module performance (power) decreases over time. This is not only important for financial reasons but also technically, because it is crucial to understand the effects of the local climate on the performance degradation. Although different models have been proposed to quantify the impact of climatic stresses on performance degradation based on indoor ageing tests, less has been done to quantify these effects in outdoor operations. The available methods for outdoor application are mainly data-driven, meaning that the performance losses are evaluated from monitored performance data without an understanding of the influencing environmental variables. Moreover, these models are suitable for performance loss rates and not for degradation rates evaluations. Therefore, in the first part of this research, a physical model to determine the degradation rates of photovoltaic modules based on outdoor climatic variables is proposed. Through it, the impact of combined climatic loads on the module’s maximum power output is quantified. In this approach, three degradation precursor mechanisms, namely, hydrolysis, photodegradation, and thermo-mechanical, are assumed to be necessary for service lifetime prediction. For each mechanism, an empirical model that describe well the physical/chemical kinetics is selected or proposed. To validate the selected or proposed models, experimental data from accelerated ageing tests are used. A generalized model to quantify the effects of combined climatic loads for outdoor applications is then derived from the three models. The generalized model is calibrated and validated using outdoor experimental data of three identical mono-crystalline silicon modules installed in three benchmarking climates: maritime (Gran Canaria, Spain), arid (Negev, Israel), and alpine (Zugspitze, Germany). Finally, using the public climate database (ERA5), climatic data is processed to extract the climatic stresses necessary for the calculation of the degradation rate. These stresses are then applied to evaluate the degradation rates based on the three precursor mechanisms and also to evaluate the total degradation rates. Therefore, global degradation risk maps based on specific precursor mechanisms as well as total degradation rate are provided. We believe that these risk maps are useful to understand the dominating degradation mechanisms according to geographical locations and hence could be used to develop photovoltaic materials depending on the operating geographical locations. Other fundamental challenge of the available methods is their accuracy when long-term forecasts are needed after a short operation time and with limited data points. The second part of this research, addresses this challenge where a new data-driven method is proposed so as to improve the accuracy of long-term prediction with small degradation history. The model depends on the degradation patterns and a new concept of time dependent degradation rate is introduced. The model has been calibrated and validated using different photovoltaic modules and systems data with 5 to 35 years of field exposure. The new model is benchmarked against existing data-driven methods. The proposed model lowered the long-term forecast uncertainties when forecasts are made after a small performance degradation. Through this, the effects of long-term degradation to lifetime yield prediction are assessed. It has been shown that using the proposed approach, the lifetime yield predictions are more reliable due to more accurate long-term degradation forecast. Finally, the two approaches are combined to form a hybrid model based on both the physical and data-driven methods. Indeed, the hybrid modelis aimed to provide more reliable long-term degradation forecast as well as having a physical understanding of the dominating degradation mechanisms influencing the performance degradation. We believe such a model is useful to calculate more reliable levelized cost of energy and thus the economic viability of solar energy as well as to improve the development of new PV materials according to the operating climatic conditions.