GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance

2025-08-18

AI Summary

To address the growing complexity of grid regulations in renewable energy transitions, low efficiency of manual interpretation, and lack of automated support, this paper proposes an end-to-end framework for grid regulation reasoning and compliance assessment. Methodologically, it introduces a multi-stage query refinement mechanism and integrates RAPTOR—a hierarchical retrieval-augmented approach—into conventional RAG pipelines to enhance semantic understanding of regulations and improve consistency in retrieving critical clauses. Furthermore, it synergizes large language models with a multi-dimensional automated evaluation benchmark to enable cross-regulatory compliance judgment. Experimental results demonstrate that the framework improves answer quality by 26.4% and increases recall of critical regulatory clauses by over tenfold. It significantly enhances accuracy, generalizability, and engineering practicality in regulatory analysis, offering a scalable solution for automated grid compliance in evolving energy systems.

Technology Category

• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and compliance increasingly vital. Grid codes, the regulations governing grid operations, are complex and often lack automated interpretation solutions, which hinders industry expansion and undermines profitability for electricity companies. We introduce GridCodex, an end to end framework for grid code reasoning and compliance that leverages large language models and retrieval-augmented generation (RAG). Our framework advances conventional RAG workflows through multi stage query refinement and enhanced retrieval with RAPTOR. We validate the effectiveness of GridCodex with comprehensive benchmarks, including automated answer assessment across multiple dimensions and regulatory agencies. Experimental results showcase a 26.4% improvement in answer quality and more than a 10 fold increase in recall rate. An ablation study further examines the impact of base model selection.

Problem

• Automating interpretation of complex power grid regulations
• Improving compliance efficiency for renewable energy integration
• Enhancing answer quality in regulatory reasoning systems

Innovation

• Uses large language models for grid code reasoning
• Enhances retrieval with RAPTOR technology
• Multi-stage query refinement improves answer quality