The field of renewable energy systems is witnessing significant advancements with the integration of large language models (LLMs) and deep learning techniques. Researchers are exploring the potential of LLMs in optimizing microgrids and active distribution networks, as well as in detecting defects in solar photovoltaic modules. The use of LLMs is also being extended to supply chain management, where it is being used to facilitate decision-making and improve the understanding of complex systems. Notably, the application of LLMs in these areas is enabling the automation of tasks, improving efficiency, and reducing the need for human expertise. Noteworthy papers in this area include: The paper on Solar Photovoltaic Assessment with Large Language Model, which proposes a framework for detecting solar panels in satellite imagery using LLMs. The paper on Lightweight Transformer-Driven Segmentation of Hotspots and Snail Trails in Solar PV Thermal Imagery, which presents a deep learning framework for segmenting thermal infrared images of PV panels. The paper on Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems, which proposes an LLM-powered approach for optimizing ADN dispatch problems.