  In this paper, we propose a graph neural network architecture to solve the AC
power flow problem under realistic constraints. To ensure a safe and resilient
operation of distribution grids, AC power flow calculations are the means of
choice to determine grid operating limits or analyze grid asset utilization in
planning procedures. In our approach, we demonstrate the development of a
framework that uses graph neural networks to learn the physical constraints of
the power flow. We present our model architecture on which we perform
unsupervised training to learn a general solution of the AC power flow
formulation independent of the specific topologies and supply tasks used for
training. Finally, we demonstrate, validate and discuss our results on medium
voltage benchmark grids. In our approach, we focus on the physical and
topological properties of distribution grids to provide scalable solutions for
real grid topologies. Therefore, we take a data-driven approach, using large
and diverse data sets consisting of realistic grid topologies, for the
unsupervised training of the AC power flow graph neural network architecture
and compare the results to a prior neural architecture and the Newton-Raphson
method. Our approach shows a high increase in computation time and good
accuracy compared to state-of-the-art solvers. It also out-performs that neural
solver for power flow in terms of accuracy.
