pvdeg - Advanced Degradation Prediction Tool and Database
pvdeg is an open-source python library that provides set of tools to calculate degradation responses and degradation related parameters for PV.
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Engineering topics
Karas, Joe (ORCID:0000000240242544)
Publications and source records attributed to Karas, Joe (ORCID:0000000240242544).
An Open-Source Python Library for Modeling LETID and LID in Silicon Solar Cells and Wafers
This poster demonstrates an (in development) open-source software library, written in python, to aid in modeling and understanding light- and elevated temperature-induced degradation (LETID) and boron-oxygen light-induced degradation (B-O LID) in silicon solar cells and wafers. We discuss the underlying equations and necessary input parameters for constructing realistic models. We demonstrate several use cases for the library, including modeling LETID progression in scenarios like indoor, accelerated tests and outdoor field deployment. We also demonstrate using the library to model B-O LID, and we demonstrate using the library to model degradation in passivated wafers, rather than solar cells.
LETID↗
DuraMAT Technology Scouting Report: Assessing Module Reliability Risks Associated with Projected Technological Changes
Maintaining the reliability of photovoltaic (PV) modules in the face of rapidly changing technology is critical to maximizing solar energy's contribution to global decarbonization. Our presentation describes expected changes in PV technology and their impacts on performance and reliability. We leverage PV market reports, interviews with PV researchers and other industry stakeholders, and peer-reviewed literature to narrow the multitude of possible changes into a manageable set of 11 impactful trends likely to be incorporated in near-term crystalline-silicon module designs. We group the trends into four categories (module architecture, interconnect technologies, bifacial modules, and cell technology) and explore the drivers behind the changes, their interactions, and associated reliability risks and benefits. Our analysis identifies specific areas that would benefit from accelerating the PV reliability learning cycle, to assess emerging module products and designs more accurately. We recommend that researchers continue tracking module technologies and their reliability implications so efforts can be focused on the most impactful trends. As the rapid technological turnover continues, it is also critical to incorporate fundamental knowledge into models that can predict module reliability. Predictive capabilities complete the PV reliability learning cycle-reducing the time required to assess new designs and mitigating the risks associated with large-scale deployment of new products.
bifacial↗