Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation
Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural Networks (GNNs) have been proposed as fast surrogates, yet existing solvers are trained for a single problem at a time, producing narrow models that must be rebuilt for each new task. We propose a more general approach: a single Heterogeneous Residual Gated Graph Convolutional Network that solves all three problems with one shared backbone. Rather than learning one mapping, the model learns a reusable representation of how the network behaves, from which PF, OPF, and SE can each be estimated. Trained jointly on ...
| Authors | Ferran Bohigas-Daranas, Hamid Latif-Martínez, Eduardo Prieto-Araujo et al. |
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| Primary category | eess.SY |
| Published | 15 September 2026 |
| Last updated at source | 15 September 2026 |
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