用大模型自动解决电网调度难题，让非专业人士也能轻松操作。
Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems
- 分阶段设计多大模型协作框架，分别负责信息提取、建模和代码生成。
- 针对各环节优化提示词与反馈机制，显著提升输出准确率。
- 用户只需自然语言提问，即可获得电网调度方案，适合电力新手使用。
分布式能源资源在主动配电网（ADN）中的渗透率持续上升，亟需高效调度。然而，大量新接入的运营商（如配电系统聚合商、虚拟电厂管理者、终端产消者）普遍缺乏电力系统运行、建模、优化与编程的专业知识，依赖人工专家既昂贵又耗时。为应对挑战，实现智能灵活的ADN调度，本文提出一种基于大语言模型（LLM）的自动化建模与优化方法。首先将调度问题分解为多个顺序阶段，并设计多LLM协同架构，包括信息提取器、问题建模器和代码生成器，分别完成信息检索、优化问题构建和代码实现。随后，针对每个代理开发定制化精炼技术，大幅提高生成内容的准确性与可靠性。所提方法具备以用户为中心的交互界面，使ADN运营商仅通过自然语言查询即可获得调度策略，消除技术门槛，提升效率。在多种测试案例上的全面对比与端到端演示验证了该架构与方法的有效性。
原文摘要 · Abstract (English)
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.
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