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

TL;DR

GridCodex is an innovative AI framework that uses advanced language models and retrieval techniques to interpret and ensure compliance with complex power grid regulations, improving accuracy and recall significantly.

Contribution

It introduces a novel end-to-end framework combining multi-stage query refinement and RAPTOR retrieval to enhance grid code reasoning and compliance automation.

Findings

26.4% improvement in answer quality

Over 10-fold increase in retrieval recall

Effective across multiple regulatory agencies

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.