X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysis

• URL: http://arxiv.org/abs/2512.20789v1
• Date: Tue, 23 Dec 2025 21:36:20 GMT
• Title: X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysis
• Authors: Yihan, Wen, Xin Chen,
• Abstract summary: X-GridAgent is a novel large language model (LLM)-powered agentic AI system designed to automate complex power system analysis through natural language queries.<n>System integrates domain-specific tools and specialized databases under a three-layer hierarchical architecture.
• Score: 5.5917393750392925
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The growing complexity of power system operations has created an urgent need for intelligent, automated tools to support reliable and efficient grid management. Conventional analysis tools often require significant domain expertise and manual effort, which limits their accessibility and adaptability. To address these challenges, this paper presents X-GridAgent, a novel large language model (LLM)-powered agentic AI system designed to automate complex power system analysis through natural language queries. The system integrates domain-specific tools and specialized databases under a three-layer hierarchical architecture comprising planning, coordination, and action layers. This architecture offers high flexibility and adaptability to previously unseen tasks, while providing a modular and extensible framework that can be readily expanded to incorporate new tools, data sources, or analytical capabilities. To further enhance performance, we introduce two novel algorithms: (1) LLM-driven prompt refinement with human feedback, and (2) schema-adaptive hybrid retrieval-augmented generation (RAG) for accurate information retrieval from large-scale structured grid datasets. Experimental evaluations across a variety of user queries and power grid cases demonstrate the effectiveness and reliability of X-GridAgent in automating interpretable and rigorous power system analysis.

Related papers

• ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks [62.031889234230725]
  6G networks rely on complex cross-layer optimization.<n> manually translating high-level intents into mathematical formulations remains a bottleneck.<n>We present ComAgent, a multi-LLM agentic AI framework.
  arXiv  Detail & Related papers  (2026-01-27T13:43:59Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• GridMind: LLMs-Powered Agents for Power System Analysis and Operations [3.7568206336846663]
  This paper presents a multi-agent AI system that integrates Large Language Models (LLMs) with deterministic engineering solvers to enable conversational scientific computing for power system analysis.<n>GridMind addresses workflow integration, knowledge accessibility, context preservation, and expert decision-support augmentation.<n>This work establishes agentic AI as a viable paradigm for scientific computing, demonstrating how conversational interfaces can enhance accessibility while preserving numerical rigor essential for critical engineering applications.
  arXiv  Detail & Related papers  (2025-09-02T16:42:18Z)
• PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses [1.9682121767943495]
  Rapid electrification and decarbonization are increasing the complexity of distribution grid (DG) operation and planning.<n>These analyses depend on disparate models, function calls, and data pipelines that require expert knowledge and remain difficult to automate.<n>We build an agentic system PowerChain, which is capable of autonomously performing complex grid analyses.
  arXiv  Detail & Related papers  (2025-08-23T17:24:46Z)
• Assemble Your Crew: Automatic Multi-agent Communication Topology Design via Autoregressive Graph Generation [91.17994756436259]
  Multi-agent systems (MAS) based on large language models (LLMs) have emerged as a powerful solution for dealing with complex problems across diverse domains.<n>Existing approaches are fundamentally constrained by their reliance on a template graph modification paradigm with a predefined set of agents and hard-coded interaction structures.<n>We propose ARG-Designer, a novel autoregressive model that operationalizes this paradigm by constructing the collaboration graph from scratch.
  arXiv  Detail & Related papers  (2025-07-24T09:17:41Z)
• AutoMind: Adaptive Knowledgeable Agent for Automated Data Science [70.33796196103499]
  Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems.<n>Existing frameworks depend on rigid, pre-defined and inflexible coding strategies.<n>We introduce AutoMind, an adaptive, knowledgeable LLM-agent framework.
  arXiv  Detail & Related papers  (2025-06-12T17:59:32Z)
• HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation [11.53083922927901]
  HM-RAG is a novel Hierarchical Multi-agent Multimodal RAG framework.<n>It pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data.
  arXiv  Detail & Related papers  (2025-04-13T06:55:33Z)
• A Survey on (M)LLM-Based GUI Agents [62.57899977018417]
  Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction.<n>Recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms.<n>This survey identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control.
  arXiv  Detail & Related papers  (2025-03-27T17:58:31Z)
• AI Agents for Ground-Based Gamma Astronomy [0.0]
  We present two prototypes that integrate with the Cherenkov Telescope Array Observatory pipelines for operations and offline data analysis.<n>These AI agents offer a transformative approach to system management and data analysis by automating complex tasks and providing intelligent assistance.
  arXiv  Detail & Related papers  (2025-03-02T09:55:54Z)
• Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline for Code Generation [58.799397354312596]
  Large language models (LLMs) have demonstrated remarkable capabilities in various domains, particularly in system 1 tasks.<n>Recent research on System2-to-System1 methods surge, exploring the System 2 reasoning knowledge via inference-time computation.<n>In this paper, we focus on code generation, which is a representative System 2 task, and identify two primary challenges.
  arXiv  Detail & Related papers  (2025-02-18T03:20:50Z)
• Advancing Agentic Systems: Dynamic Task Decomposition, Tool Integration and Evaluation using Novel Metrics and Dataset [1.904851064759821]
  Advanced Agentic Framework: A system that handles multi-hop queries, generates and executes task graphs, selects appropriate tools, and adapts to real-time changes. New Novel Evaluation Metrics: Introduction of Node F1 Score, Structural Similarity Index (SSI), and Tool F1 Score to comprehensively assess agentic systems. AsyncHow-based dataset for analyzing agent behavior across different task complexities.
  arXiv  Detail & Related papers  (2024-10-29T18:45:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.