We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as supervised vertex regression task, where GNN trained predict current and injections at each grid branch that yield flow balance. By representing line graph with branches vertices, we can train accurate robust changes topology. In addition, by using spec...