Power system operational reliability evaluation with retrieval-augmented generation enhanced large language model

Operational reliability evaluation of power grid dispatch is crucial for ensuring power system security and stability. Traditional evaluation involves system state assessment and operation regulations verification, with the latter relying heavily on dispatcher expertise, leading to inefficiencies and accuracy limitations. While rule-based automation has partially addressed these challenges, generalization to frequently updated regulations remains problematic. This paper proposes a Retrieval-Augmented Generation enhanced Large Language Model framework for automated operation regulations evaluation in power grid dispatch. The framework features natural language understanding capabilities for accurate semantic interpretation of dispatch regulations and supports real-time knowledge updates without additional model training. A Hierarchical Document Retrieval method improves retrieval precision by leveraging document structure, while an Operations AutoPrompt Generation technique automatically converts dispatch operations into optimized queries. Experimental validation using simulation data and real operational data from the Guangdong Power Grid demonstrates that the proposed method achieves an average evaluation accuracy of 90% across multiple international regulation datasets, with compliance accuracy reaching 93% on the Italian Grid Code. The framework processes evaluation queries in 5.6 to 7.0 s, meeting practical requirements for dispatch decision support. These results demonstrate the framework's strong generalization ability and practical applicability for real-world power grid operational reliability evaluation.