Abstract: [Objectives]　To address the challenges faced by new types of nuclear power systems in ensuring energy supply, promoting clean energy transition, and achieving “dual carbon” goals, such as structural diversification, load uncertainty, and data complexity, and to solve problems associated with the application of large language models in the nuclear power domain, such as knowledge limitations, hallucinations, and high reasoning costs, the study explores the application potential of combining large language models with knowledge graphs, especially graph retrieval-augmented generation (GRAG) technology. This combination aims to build more intelligent, efficient, and reliable information processing capabilities for nuclear power systems. [Methods]　The research status of large language models and knowledge graphs in the nuclear power domain, their respective advantages and disadvantages, and the complementarity of their integration are analyzed. The advantages of GRAG technology over traditional retrieval-augmented generation (RAG) are highlighted, along with its specific applications in scenarios such as nuclear power risk assessment, intelligent question-answering assistance, knowledge management and decision support, and fault diagnosis and prediction. Furthermore, the technical pathway for introducing and fine-tuning large language models, constructing domain-specific knowledge graphs, and implementing GRAG enhancement is outlined. Finally, an outlook on future research is provided, covering areas such as knowledge graph construction under heterogeneous data, cognitive reasoning and decision-making of large language models, and the controllability of human-computer interaction. [Conclusions]　GRAG technology combined with knowledge graphs can effectively alleviate the knowledge limitations and hallucination problems of large language models in specialized domains, enhancing the interpretability and reliability of the generated content. The research findings can provide references for the future optimization of knowledge graph construction in the nuclear power domain, enhancing the capabilities of large language models in complex reasoning tasks, and developing artificial intelligence agents for efficient interaction with experts in the nuclear power field.

Key words: dual carbon, nuclear power, smart grid, artificial intelligence (AI), deep learning, large language model, knowledge graph, retrieval-augmented generation