Photovoltaic lifetime forecast model based on degradation patterns

Abstract

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.

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

“…54 Such phenomena are categorized as nonzero production incidents and they can result in reversible and irreversible performance losses based on F I G U R E 1 Flowchart of the proposed methodology describing the five consecutive steps for differentiating failures and trend-based performance losses the caused damage. 57 Most of the irreversible losses can be classified as material/component degradation of the PV module and balance of system. 57 In this study, degradation, soiling, and snow coverage were investigated by applying a statistical method on a single PV performance metric.…”

Photovoltaic Power Forecasting Methods

“…( 16)). However, some authors [42][43][44], have evaluated and modeled the non-linearity in degradation rates and performance. For example in [43] a non-linear power degradation model (Eq.…”

“…For example in [43] a non-linear power degradation model (Eq. ( 17)) was proposed and applied in [44] with a time-dependent degradation rates to predict PV performance lifetime.…”

Comparative Analysis of Change-Point Techniques for Nonlinear Photovoltaic Performance Degradation Rate Estimations

“…Ambitious targets for 50-year PV module lifetimes [14] tend to focus on design challenges, but they also require tools for predicting Rd at an early stage in order to inform service lifetime (e.g., [15]) and provide reassurance during operation. Of equal importance is the ability to detect subsequent changes in Rd, especially at the beginning and end of life, in order to help identify the mechanisms behind such behaviors.…”