Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance.
arXiv:2605.23194v2 Announce Type: replace-cross
Abstract: Fast and reliable optimal power flow (OPF) approximation is important for power system operation, yet heterogeneous OPF graph models are ofte...
By Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim, Teja Kuruganti
The paper introduces Multi-Channel Physics-Aware Random Walk Fingerprints (MC-PA-RWF), a lightweight graph-level representation that incorporates physical edge states into random-walk propagation for power grid graphs. By constructing multiple edge-weighted channels from domain-relevant attributes and concatenating channel-specific fingerprints, the method achieves high balanced accuracy on PowerGraph benchmarks, outperforming topology-only RWF and matching or surpassing several graph neural network baselines. Experiments on three benchmark systems show statistically significant improvements, with the node-edge extension reaching up to 99.32% balanced accuracy and boosting failure-class F1 scores by 1.60–5.84 percentage points.
By Adnan Anwar
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.
By Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias, Juan Pablo Maroto Herrera, Ane Miren Florez-Tapia, Marco Quartulli. Izaro Goienetxea Urziku
arXiv:2609.16738v1 Announce Type: cross
Abstract: Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computati...
By Ferran Bohigas-Daranas, Hamid Latif-Mart\'inez, Eduardo Prieto-Araujo, Oriol Gomis-Bellmunt, Pere Barlet-Ros
arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.
By Martin Sadric, Sebastian P\"utz, Christian Nauck, Veit Hagenmeyer, Frank Hellmann, Dirk Witthaut, Benjamin Sch\"afer