Abstract
Although common practice for estimating photovoltaic (PV) degradation rate (RD) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear RD based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio (PR) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, “nonconventional” models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.
Highlights
A CCURATELY predicting the lifetime performance of photovoltaic (PV) systems is critical for determining the financial payback of the project
The minimum and maximum absolute mean linear degradation rates were observed in the Suntechnics back-contact cell technology (0.74%/year) and Wurth Solar CIGS system (2.62%/year), respectively
This article successfully detected and quantified the impact of nonlinear PV degradation on levelized cost of energy (LCOE) by applying the Facebook Prophet (FBP) change-point algorithm on trends of monthly performance ratio (PR) time-series. This is important since identifying change points in the PV performance trend may mitigate the bias in the RD computations when applying statistical analysis techniques that assume linearity
Summary
A CCURATELY predicting the lifetime performance of photovoltaic (PV) systems is critical for determining the financial payback of the project. One of the parameters that influence PV performance prediction is the degradation rate (RD) or performance loss rate defined as the decrease of system efficiency over time. Knowledge of this metric is important for reducing uncertainties and financial risks [2]. The work of Andreas Livera, George Makrides, and George E.
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