Power Flow Balancing with Decentralized Graph Neural Networks
This work addresses power grid management for energy systems, offering a robust and efficient solution, though it appears incremental as it builds on existing GNN methods for a specific domain.
The authors tackled the problem of balancing power flows in energy grids by proposing a decentralized Graph Neural Network (GNN) framework that predicts current and power injections to achieve balance, demonstrating robustness to topology changes and improved generalization to unseen grids.
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 a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.