Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models

Bohan Zhang; Zhongkai Yi; Ying Xu; Zhenghong Tu; Shuai Dong

doi:10.17775/CSEEJPES.2025.00780

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Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models

Special Section Papers | 更新时间：2026-04-16

- Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models
- Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第2期页码：609-621
- 作者机构：
  
  School of Electrical Engineering and Automation, Harbin Institute of Technology,Harbin,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2025.00780
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Bohan Zhang, Zhongkai Yi, Ying Xu, 等. Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):609-621.

Bohan Zhang, Zhongkai Yi, Ying Xu, et al. Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 609-621.
Bohan Zhang, Zhongkai Yi, Ying Xu, 等. Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):609-621. DOI： 10.17775/CSEEJPES.2025.00780.

Bohan Zhang, Zhongkai Yi, Ying Xu, et al. Automatic Programming for the Optimal Power Flow in Distribution Networks: An Edge-side Adaptive Computing Approach Based on Large Language Models[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 609-621. DOI： 10.17775/CSEEJPES.2025.00780.

摘要

Abstract

Existing optimal power flow (OPF) computations typically require manual programming tailored to a fixed operational environment. However

the environment of the distribution network (DN) varies with the frequent changing of modern power systems. This paper proposed an adaptive OPF approach based on large language models (LLMs) to achieve automatic programming of environment changes. First

the approach utilizes publicly available DN datasets and OPF computation scripts

combined with expert knowledge

to construct a supervised fine-tuning (SFT) dataset. Second

the generated dataset is used to perform post-training on low-parameter-scale LLMs

which are suitable for edge-side deployment

using a low-rank adaptation (LoRA) method. Third

to further enhance the accuracy of edge-side LLMs

a feedback self-correction mechanism based on a multi-agent workflow is introduced. Simulation results demonstrate that the proposed approach significantly enhances the capability of edge-side LLMs in solving the OPF problems for the DNs with frequent environment changes.

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