GAT‐OPF: Robust and Scalable Topology Analysis in AC Optimal Power Flow With Graph Attention Networks Article Swipe
Jiale Zhang
,
Xiaoqing Bai
,
Peijie Li
,
Zonglong Weng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/gtd2.70039
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1049/gtd2.70039
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.
Related Topics
Concepts
Power flow
Scalability
Computer science
Topology (electrical circuits)
Power graph analysis
Network topology
Graph
Graph theory
Flow (mathematics)
Mathematical optimization
Electric power system
Distributed computing
Power (physics)
Theoretical computer science
Mathematics
Computer network
Engineering
Electrical engineering
Physics
Quantum mechanics
Geometry
Database
Combinatorics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/gtd2.70039
- OA Status
- gold
- References
- 33
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4408550090Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/gtd2.70039Digital Object Identifier
- Title
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GAT‐OPF: Robust and Scalable Topology Analysis in AC Optimal Power Flow With Graph Attention NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Jiale Zhang, Xiaoqing Bai, Peijie Li, Zonglong WengList of authors in order
- Landing page
-
https://doi.org/10.1049/gtd2.70039Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1049/gtd2.70039Direct OA link when available
- Concepts
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Power flow, Scalability, Computer science, Topology (electrical circuits), Power graph analysis, Network topology, Graph, Graph theory, Flow (mathematics), Mathematical optimization, Electric power system, Distributed computing, Power (physics), Theoretical computer science, Mathematics, Computer network, Engineering, Electrical engineering, Physics, Quantum mechanics, Geometry, Database, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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