Abstract
A linear performance drop is generally assumed during the photovoltaic (PV) lifetime. However, operational data demonstrate that the PV module degradation rate ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> ) is often nonlinear, which, if neglected, may increase the financial uncertainty. Although nonlinear behavior has been the subject of numerous publications, it was only recently that statistical models able to detect change-points and extract multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> values from PV performance time-series were introduced. A comparative analysis of six open-source libraries, which can detect change-points and calculate nonlinear <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> , is presented in this article. Since the real <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> and change-point locations are unknown in field data, 960 synthetic datasets from six locations and two PV module technologies have been generated using different aggregation and normalization decisions and nonlinear degradation rate patterns. The results demonstrated that coarser temporal aggregation (i.e., monthly vs. weekly), temperature correction, and both PV module technologies and climates with lower seasonality can benefit the change-point detection and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> extraction. This also raises a concern that statistical models typically deployed for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> analysis may be highly climatic- and technology-dependent. The comparative analysis of the six approaches demonstrated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">median</i> mean absolute errors (MAE) ranging from 0.06 to 0.26%/year, given a maximum absolute <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rd</i> of 2.9%/year. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">median</i> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MAE</i> in change-point position detection varied from 3.5 months to 6 years.
Highlights
D EGRADATION rates (Rd) inform photovoltaic (PV) lifetime energy yield predictions
It is known that PV performance fluctuates due to a number of seasonally related factors such as temperature [3], spectrum [4], soiling [5], or even abrupt changes caused by failures [6], etc
Such changes can be either continuous or discontinuous and in the case of nonlinear degradation, the change is considered as continuous since the segments have the same Rd value at the change-point [18]
Summary
D EGRADATION rates (Rd) inform photovoltaic (PV) lifetime energy yield predictions. Knowledge of this metric is important for feasibility, reliability and financial analyses of PV systems. Simplistic assumptions may cause detrimental effects increasing PV financial uncertainties [1] and, investment risk [2]. Such assumptions may include the usage of: single Rd values from literature; values reported from different climatic conditions; values reported for a specific technology but built with differing manufacturing quality or with different packaging materials; and the assumption of constant performance loss over time. The statistical tools are available and relatively easy to use, it is inherently challenging to extract reproducible and accurate PV Rds [7], [8]
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