GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On Data-Driven Modeling and Control in Modern Power Grids Stability: Survey and Perspective
Modern power grids are fast evolving with the increasing volatile renewable generation, distributed energy resources (DERs) and time-varying operating conditions. The DERs include rooftop photovoltaic (PV), small wind tu…
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power grids. In recent years, integrating a sign…
Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks
The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, el…
Decision MakingPhysical SimulationsBounding Regression Errors in Data-driven Power Grid Steady-state Models
Data-driven models analyze power grids under incomplete physical information, and their accuracy has been mostly validated empirically using certain training and testing datasets. This paper explores error bounds for dat…
regressionThe DeMaDs Open Source Modeling Framework for Power System Malfunction Detection
Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities -- especially with regard to data-driven applicatio…