A Comprehensive Survey of LLMs for Sustainable and Renewable Energy Systems

Large Language Models (LLMs) are emerging as a new class of intelligent systems capable of reasoning over heterogeneous knowledge and interacting with human operators, yet their role in renewable energy systems remains insufficiently synthesized. This review provides a dedicated, systematic examination of LLMs as knowledge-centric, human-oriented decision-support tools for renewable energy infrastructure. In contrast to existing surveys that primarily emphasize numerical optimization, forecasting, or conventional machine learning methods, this work focuses on how LLMs enable textual reasoning, regulatory interpretation, operational intelligence, and interactive support across energy system lifecycles. We present a structured overview of recent literature, categorizing LLM applications by their functional roles in analysis, control, operation, and policy support. Furthermore, we analyze the contributions of LLMs to key decision-support tasks, including information retrieval, incident analysis, operational coordination, and strategic planning in smart grids and microgrids. The review also critically examines current limitations and risks associated with deploying LLMs in energy systems, including hallucination, reliability, domain adaptation, explainability, and real-time operational constraints. Finally, we identify emerging research directions, including energy-efficient LLM deployment, sustainability-aware AI design, and the alignment of LLM-based solutions with the goals of resilient, low-carbon, and environmentally sustainable energy systems.