Develops a heterogeneous GNN workflow on HydraGNN for large-scale OPF surrogate modeling across varied grid topologies and shows that pretraining improves fine-tuning on feasibility and N-1 contingency tasks.
Topology-aware graph neural networks for learning feasible and adaptive ac-opf solutions,
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Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
Develops a heterogeneous GNN workflow on HydraGNN for large-scale OPF surrogate modeling across varied grid topologies and shows that pretraining improves fine-tuning on feasibility and N-1 contingency tasks.