{"title":"GAT-OPF: Robust and Scalable Topology Analysis in AC Optimal Power Flow With Graph Attention Networks","authors":"Jiale Zhang, Xiaoqing Bai, Peijie Li, Zonglong Weng","doi":"10.1049/gtd2.70039","DOIUrl":null,"url":null,"abstract":"<p>As power systems rapidly grow in scale and complexity, existing data-driven methods are limited when applied to large-scale networks due to issues with low prediction accuracy and constraint violations. This paper proposes an innovative hybrid framework, GAT-OPF, which, for the first time, combines graph attention networks (GAT) with deep neural networks (DNN) to form the GAT-DNN model, designed to dynamically adapt to topology changes in the AC optimal power flow (AC-OPF) problem. A hybrid loss function is also developed, combining prediction error with a constraint violation penalty term and incorporating a dynamic Lagrange multiplier adjustment mechanism to ensure constraint compliance throughout training. The model was tested under topology changes on the IEEE 30-bus system and validated for scalability on larger systems, including IEEE 300-bus, 1354-bus, and 9241-bus systems. The results show that the proposed model significantly enhances the computational efficiency of large-scale power systems while effectively balancing high prediction accuracy and low constraint violations without post-processing, highlighting its potential for real-time optimization in large-scale power systems.</p>","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"19 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70039","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Generation Transmission & Distribution","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/gtd2.70039","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
As power systems rapidly grow in scale and complexity, existing data-driven methods are limited when applied to large-scale networks due to issues with low prediction accuracy and constraint violations. This paper proposes an innovative hybrid framework, GAT-OPF, which, for the first time, combines graph attention networks (GAT) with deep neural networks (DNN) to form the GAT-DNN model, designed to dynamically adapt to topology changes in the AC optimal power flow (AC-OPF) problem. A hybrid loss function is also developed, combining prediction error with a constraint violation penalty term and incorporating a dynamic Lagrange multiplier adjustment mechanism to ensure constraint compliance throughout training. The model was tested under topology changes on the IEEE 30-bus system and validated for scalability on larger systems, including IEEE 300-bus, 1354-bus, and 9241-bus systems. The results show that the proposed model significantly enhances the computational efficiency of large-scale power systems while effectively balancing high prediction accuracy and low constraint violations without post-processing, highlighting its potential for real-time optimization in large-scale power systems.
期刊介绍:
IET Generation, Transmission & Distribution is intended as a forum for the publication and discussion of current practice and future developments in electric power generation, transmission and distribution. Practical papers in which examples of good present practice can be described and disseminated are particularly sought. Papers of high technical merit relying on mathematical arguments and computation will be considered, but authors are asked to relegate, as far as possible, the details of analysis to an appendix.
The scope of IET Generation, Transmission & Distribution includes the following:
Design of transmission and distribution systems
Operation and control of power generation
Power system management, planning and economics
Power system operation, protection and control
Power system measurement and modelling
Computer applications and computational intelligence in power flexible AC or DC transmission systems
Special Issues. Current Call for papers:
Next Generation of Synchrophasor-based Power System Monitoring, Operation and Control - https://digital-library.theiet.org/files/IET_GTD_CFP_NGSPSMOC.pdf