Spatio-Temporal GraphSAGE Framework for Generalizable Power Grid Representations

To address the need for generalizable representation in foundation grid models, we propose a heterogeneous GraphSAGE framework that learns tokens for grid elements representation by combining temporal behaviour and physical attributes with network context obtained through edge message passing. The model is jointly trained with multiple objectives to encourage general-purpose representations, and is evaluated on multi-horizon forecasting and state estimation. Across two ACTIVSg grids, the model achieves in most cases performance comparable to task specific baselines while supporting zero-shot transfer to an unseen grid. The results suggest that the learned representations can generalize across both tasks and topologies.