PowerChain: Automating Distribution Grid Analysis with Agentic AI Workflows

2025-08-23

AI Summary

Accelerated electrification and decarbonization of distribution grids have increased analytical complexity, yet existing methods rely heavily on expert knowledge, lack automation, and are inaccessible to small utilities. Method: We propose the first agent-based AI framework for distribution system analysis, leveraging large language models (e.g., GPT-5, Qwen) to orchestrate domain-specific functions via natural-language input—enabling end-to-end generation of executable, expert-level workflows. The framework integrates agent-driven task orchestration, semantic-guided workflow exemplar learning, and a power-system-specific function library to support autonomous problem solving for unseen tasks. Results: Evaluated on real-world grid data, the framework accurately generates professional-grade analytical workflows, significantly lowering technical barriers. It delivers scalable, reusable, and automated analysis capabilities tailored for resource-constrained utilities.

Technology Category

• Multiagent Systems: Distributed Problem Solving
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Distributed Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Due to the rapid pace of electrification and decarbonization, distribution grid (DG) operation and planning are becoming more complex, necessitating advanced computational analyses to ensure grid reliability and resilience. State-of-the-art DG analyses rely on disparate workflows of complex models, functions, and data pipelines, which require expert knowledge and are challenging to automate. Many small-scale utilities and cooperatives lack a large R&D workforce and therefore cannot use advanced analysis at scale. To address this gap, we develop a novel agentic AI system, PowerChain, to solve unseen DG analysis tasks via automated agentic orchestration and large language models (LLMs) function-calling. Given a natural language query, PowerChain dynamically generates and executes an ordered sequence of domain-aware functions guided by the semantics of an expert-built power systems function pool and a select reference set of known, expert-generated workflow-query pairs. Our results show that PowerChain can produce expert-level workflows with both GPT-5 and open-source Qwen models on complex, unseen DG analysis tasks operating on real utility data.

Problem

• Automating complex distribution grid analysis workflows
• Addressing lack of R&D resources in small utilities
• Solving unseen grid tasks via AI agent orchestration

Innovation

• Agentic AI system automates grid analysis
• LLMs generate workflows from natural language
• Dynamic function orchestration for utility operations