Graph Neural Networks for Efficient AC Power Flow Prediction in Power Grids

TL;DR

This paper introduces Graph Neural Networks to efficiently predict AC power flow in power grids, demonstrating improved speed and scalability over traditional methods for real-time grid management.

Contribution

It presents a novel GNN-based approach for AC power flow prediction, exploring various architectures and showing superior performance in large-scale systems.

Findings

GNNs accurately predict power flow solutions.

GNNs outperform traditional solvers in computation time.

The approach scales well to larger power grid systems.

Abstract

This paper proposes a novel approach using Graph Neural Networks (GNNs) to solve the AC Power Flow problem in power grids. AC OPF is essential for minimizing generation costs while meeting the operational constraints of the grid. Traditional solvers struggle with scalability, especially in large systems with renewable energy sources. Our approach models the power grid as a graph, where buses are nodes and transmission lines are edges. We explore different GNN architectures, including GCN, GAT, SAGEConv, and GraphConv to predict AC power flow solutions efficiently. Our experiments on IEEE test systems show that GNNs can accurately predict power flow solutions and scale to larger systems, outperforming traditional solvers in terms of computation time. This work highlights the potential of GNNs for real-time power grid management, with future plans to apply the model to even larger grid systems.