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arXiv Machine Learning By Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias, Juan Pablo Maroto Herrera, Ane Miren Florez-Tapia, Marco Quartulli. Izaro Goienetxea Urziku

Towards Hierarchical GNNs for multi-grid power flow: generalization across operating scenarios

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The paper proposes a hierarchical graph neural network (GNN) architecture for multi‑grid power‑flow modeling that exchanges information through two reduced graphs within a GENCO‑based corrective network. Experiments on three grid topologies show that the Kron‑derived transport approach reduces macro family‑balanced voltage error by 85 % compared to a flat backbone and 31 % compared to a Quotient construction, outperforming per‑bus mean baselines across all seeds. Preliminary results indicate strong generalization across operating scenarios within the studied topologies, though cross‑topology transfer remains an open challenge.

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