Power Flow Balancing with Decentralized Graph Neural Networks

TL;DR

This paper introduces a Graph Neural Network framework for balancing power flows in energy grids, demonstrating robustness to topology changes and improved generalization across different grid configurations.

Contribution

The paper presents a novel GNN-based approach for power flow balancing that is accurate, robust, and capable of handling multiple grid topologies simultaneously.

Findings

Localized GNN operations outperform global ones.

Model is resilient to topology perturbations.

Training on multiple topologies improves generalization.

Abstract

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.