Assessing the impact of environmental stresses on the performance of photovoltaic (PV) modules by predictive modeling is crucial for evaluating their sustainability and economic viability. While various data-driven and analytical models for PV degradation have been reported, no prior efforts have integrated these approaches for a more robust prediction. This article presents the development of an open-sourced holistic degradation modeling framework, called MAPLE, which integrates three distinct modeling approaches identified from the literature: coupled failure modeling (CFM), mode-specific modeling (MSM), and multi-scale failure modeling (MFM). In this work, CFM is extended to modern cell technologies by fitting field degradation data from literature for modules with heterojunction cells. MSM employs selected analytical models that represent the failure of modules due to specific degradation modes, with a multiplicative coupling method to combine the effects of multiple degradation modes on performance. Additionally, the concept of equivalent time is implemented to extrapolate indoor models for outdoor predictions. The MFM approach introduces a novel first principles method for degradation modeling by aggregating degradation across varying length scales, exemplified by simulating overall power loss of a module from hail damage. MFM simulation results are validated against a hail-impacted module, showing a deviation of 5.75%. The advantages and limitations of the three modeling approaches are discussed, along with future research directions to enhance the modeling approaches within MAPLE. • Identification of key degradation modeling strategies by reviewing models in literature. • Extension of CFM modeling strategy to predict degradation evolution for heterojunction modules. • Indoor to field degradation prediction through the implementation of equivalent time approach. • Integrating individual PoF models to predict degradation using any location’s weather. • Computation of total degradation from uneven, localized damage.
Ravindrababu et al. (Sat,) studied this question.