Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator

Changhun Kim; Timon Conrad; Redwanul Karim; Julian Oelhaf; David Riebesel; Tomás Arias-Vergara; Andreas Maier; Johann Jäger; Siming Bayer

arXiv · 2509.22458

Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator

Changhun Kim·Timon Conrad·Redwanul Karim·Julian Oelhaf·David Riebesel·Tomás Arias-Vergara·Andreas Maier·Johann Jäger·Siming Bayer

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Abstract

Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements at parity speed; in particular, the soft constraint on the physics loss is inoperative at inference, which can deter operational adoption. We address this with PIGNN-Attn-LS, combining an edge-aware attention mechanism that explicitly encodes line physics via per-edge biases to form a fully differentiable knownoperator layer inside the computation graph, with a backtracking line-search-based globalized correction operator that restores an operative decrease criterion at inference. Training and testing use a realistic High-/Medium-Voltage scenario generator, with NR used only to construct reference states. On held-out HV cases consisting of 4-32-bus grids, PIGNN-Attn-LS achieves a test RMSE of 0.00033 p.u. in voltage and 0.08 deg in angle, outperforming the PIGNN-MLP baseline by 99.5% and 87.1%, respectively. With streaming micro-batches, it delivers 2-5x faster batched inference than NR on 4-1024-bus grids.

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Changhun Kim, Timon Conrad, Redwanul Karim, Julian Oelhaf, David Riebesel, Tomás Arias-Vergara, Andreas Maier, Johann Jäger, Siming Bayer. 2026-02-20. Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator. https://arxiv.org/abs/2509.22458

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