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

TL;DR

This paper introduces an LLM-powered automated system for modeling and optimizing active distribution network dispatch, simplifying complex tasks for operators through natural language interaction and multi-agent coordination.

Contribution

It presents a novel multi-LLM framework for automated modeling and optimization of ADN dispatch problems, reducing reliance on expert knowledge and improving efficiency.

Findings

Effective problem decomposition and multi-agent coordination.

High accuracy and reliability in generated models and code.

Enhanced user experience with natural language interface.

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.