A comprehensive analysis of crystalline silicon photovoltaic durability modelling methods and best Practice recommendations

Silicon solar panels remain a prevalent technology in the solar photovoltaic (PV) industry. Silicon makes up the greatest percentage of panels in the solar industry and are projected to continue as a dominant technology given their proven reliability, established manufacturing infrastructure, continuous technological advancements and research, and cost-effectiveness. Silicon panels have maintained prominence in the solar industry, and their long-term durability is a crucial factor determining their reliability, field performance, and the system’s levelized cost of energy. Accelerated stress testing, field data collection and durability modelling are vital for providing insights into degradation predictions over the lifetime of the solar panels, and understanding the complex relationships between materials, designs and environmental stressors. Despite the importance of PV degradation estimations and modelling, no study explicitly analyzes and reports the shortcoming of existing degradation models specific to crystalline silicon PV technologies, providing recommendations and research opportunities for improvements. This article presents a comprehensive analysis of silicon solar photovoltaic (PV) durability modelling approaches, laying emphasis on best practices, recent advancements and future research directions. Different degradation pathways are represented using statistical, analytical, empirical, and machine learning durability models, digital twin frameworks, multi-scale and multi-physics modelling approaches. The article also compares empirical, analytical, and machine learning modelling approaches used in durability predictions. Major highlights include the importance of integrating field data, multiscale and stress analysis and uncertainty evaluations to improve durability analyses. The study concludes with recommendations for standardizing durability studies and models to amplify sustainable PV development.