  The increasing scale of alternating current and direct current (AC/DC) hybrid
systems necessitates a faster power flow analysis tool than ever. This letter
thus proposes a specific physics-guided graph neural network (PG-GNN). The
tailored graph modelling of AC and DC grids is firstly advanced to enhance the
topology adaptability of the PG-GNN. To eschew unreliable experience emulation
from data, AC/DC physics are embedded in the PG-GNN using duality. Augmented
Lagrangian method-based learning scheme is then presented to help the PG-GNN
better learn nonconvex patterns in an unsupervised label-free manner.
Multi-PG-GNN is finally conducted to master varied DC control modes. Case study
shows that, relative to the other 7 data-driven rivals, only the proposed
method matches the performance of the model-based benchmark, also beats it in
computational efficiency beyond 10 times.
