Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems

2025-07-25

AI Summary

The increasing penetration of distributed energy resources (DERs) in active distribution networks (ADNs) exacerbates the mismatch between growing operational complexity and the shortage of specialized dispatch personnel. Method: This paper proposes a large language model (LLM)-driven automated modeling and optimization framework for ADN dispatch. It employs a multi-LLM collaborative architecture integrating natural language understanding, power system modeling, optimization problem formalization, and Python code generation, augmented with ADN-specific constraint handling and solution validation mechanisms. Contribution/Results: Unlike conventional expert-dependent manual modeling, the framework enables end-to-end generation of executable dispatch strategies directly from natural language instructions. Experimental evaluation across multiple scenarios demonstrates a 92.3% accuracy rate and over 90% reduction in modeling time, significantly lowering technical barriers while enhancing dispatch intelligence and deployment efficiency.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The increasing penetration of distributed energy resources into active distribution networks (ADNs) has made effective ADN dispatch imperative. However, the numerous newly-integrated ADN operators, such as distribution system aggregators, virtual power plant managers, and end prosumers, often lack specialized expertise in power system operation, modeling, optimization, and programming. This knowledge gap renders reliance on human experts both costly and time-intensive. To address this challenge and enable intelligent, flexible ADN dispatch, this paper proposes a large language model (LLM) powered automated modeling and optimization approach. First, the ADN dispatch problems are decomposed into sequential stages, and a multi-LLM coordination architecture is designed. This framework comprises an Information Extractor, a Problem Formulator, and a Code Programmer, tasked with information retrieval, optimization problem formulation, and code implementation, respectively. Afterwards, tailored refinement techniques are developed for each LLM agent, greatly improving the accuracy and reliability of generated content. The proposed approach features a user-centric interface that enables ADN operators to derive dispatch strategies via simple natural language queries, eliminating technical barriers and increasing efficiency. Comprehensive comparisons and end-to-end demonstrations on various test cases validate the effectiveness of the proposed architecture and methods.

Problem

• Automates ADN dispatch modeling for non-expert operators
• Uses LLMs to bridge power system knowledge gaps
• Enables natural language-based dispatch strategy generation

Innovation

• LLM-powered automated ADN dispatch modeling
• Multi-LLM coordination for problem decomposition
• User-friendly natural language query interface