Graph-Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical Power Networks

Conference · Mon Dec 01 04:00:00 EST 2025

DOI:https://doi.org/10.1109/NAPS66256.2025.11272251· OSTI ID:3009448

Trigui, Ali ^[1]; Olama, Mohammed M ^[2]; Siopsis, George ^[3]; Eldakhakhni, Hatem ^[1]; Salhi, Marouane ^[1]

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1. Qubit Engineering Inc.
2. ORNL
3. University of Tennessee, Knoxville (UTK)

With the increasing complexity and data availability in modern power systems, learning-based approaches to AC Optimal Power Flow (AC OPF) have garnered significant attention. In particular, the structure of smart grids lends itself naturally to graph-based representations, where Graph Neural Networks (GNNs) can capture spatial and relational dependencies. This paper investigates attention-based GNN architectures tailored to heterogeneous graph representations of electric grids. We evaluate two major paradigms: relational attention, which distinguishes between edge types during message passing, and meta-path attention, which captures high-level semantics through multi-hop, typed paths. Using a large corpus of public AC OPF scenarios, we benchmark representative models of each type of attention. Our results demonstrate the benefits of heterogeneous attention-based models in accurately capturing grid dynamics; heterogeneous attention models achieve superior performance in both standard and perturbed settings. The findings highlight the importance of semantic-aware architectures for improving prediction robustness and interpretability in power system applications.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 3009448
• Resource Type: Conference paper/presentation
• Conference Information: The 57th North American Power Symposium (NAPS 2025) - Hartford, Connecticut, United States of America - 10/26/2025-10/28/2025
• Country of Publication: United States
• Language: English