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Title:
 
Challenges Associated with Inconsistent Photovoltaic Degradation Rate Estimations
 
Author(s):
 
M. Theristis, J. Ascencio-Vásquez, B.H. King, M. Topic, J.S. Stein
 
Keywords:
 
Degradation, Performance, Simulation, Photovoltaic (PV), Modeling
 
Topic:
 
PV Systems and Storage – Modelling, Design, Operation and Performance
Subtopic: Operation, Performance and Maintenance of PV Systems
Event: 37th European Photovoltaic Solar Energy Conference and Exhibition
Session: 5DO.5.2
 
Pages:
 
1401 - 1404
ISBN: 3-936338-73-6
Paper DOI: 10.4229/EUPVSEC20202020-5DO.5.2
 
Price:
 
 
0,00 EUR
 
Document(s): paper
 

Abstract/Summary:


Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the PV module degradation rate. Since the real value of degradation rate is unknown, a variety of unvalidated values has been reported in the literature. As such, the PV industry commonly treats this metric in an assumptive manner based on a statistically extracted range from the literature. However, the accuracy and uncertainty of degradation rate depends on a number of parameters including seasonality in respect to the local climatic conditions and also the response of a particular PV technology. In addition, the selection of data pipeline and statistical method may compound on the accuracy and uncertainty. In order to provide insights, a framework of bulk simulations of PV performance datasets using data from different climates is under development. Known degradation rates are emulated and large parametric studies are conducted in order to observe the convergence time on different PV module types based on several selection criteria such as performance metric, statistical method, etc. The preliminary results that are presented in this paper confirm that, indeed, climates and PV module types with typically lower seasonality can provide accurate degradation rate results in a shorter time period, compared to locations and PV module types that exhibit higher seasonality. As expected, the selection of data pipeline (e.g. metric, temperature correction, etc.) and statistical method also has a strong influence and therefore, introduces additional challenges.