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

--The increasing penetration of distributed energy resources into active distribution networks (ADNs) has made effective ADN dispatch imperative. This knowledge gap renders reliance on human experts both costly and time -intensive . To address this challenge and enabl e 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 improv ing 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. Index Terms--Active distribution network, dispatch problem, large language model, automated modeling and optimization. The coupling of active and reactive power, along with complex bidirectional power flows, has made traditional passive control strategies much less reliable [3]. As a result, the ADN dispatch has been proposed, which coordinat es these DERs as well as other controllable devices within the A DN to enhance the overall safety and economic efficiency of the distribution system [4], [5] .