Graph neural networks for assessing the reliability of the medium-voltage grid
Charlotte Cambier van Nooten, Tom van de Poll, Sonja Füllhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova
Abstract
Ensuring electricity grid reliability becomes increasingly challenging with the shift towards renewable energy and declining conventional capacities. Distribution System Operators (DSOs) aim to achieve grid reliability by verifying the n-1 contingency criterion, ensuring reconfiguring and restoring power distribution through switching strategies. While DSOs operate radial grids, government regulations and reliability metrics, such as the average minutes without power, necessitate achieving continuity as closely as possible through reconfiguration. Despite the critical role of reliability assessment, current methods such as mathematical optimisation approaches are often computationally expensive and impractical for large-scale grids. This paper addresses these limitations by proposing a novel application of Graph Neural Networks (GNNs) to tackle the n-1 contingency criterion, directly leveraging the inherent graph structure of electrical networks. Unlike traditional machine learning methods, GNNs directly handle graph-structured data, making them well-suited for complex grid topologies. This study introduces a Graph Isomorphic Network (GIN)-inspired framework designed to incorporate both node and edge features, enabling a more comprehensive representation of grid assets and connectivity. The GIN-inspired framework not only generalises effectively to unseen grid structures but also significantly reduces computation times, demonstrating prediction times up to 1000 times faster compared to traditional optimisation-based approaches. These findings indicate that our approach provides a computationally efficient and scalable solution for DSOs, enhancing the reliability and operational efficiency of energy grid assessments, and opening up the way for more robust real-time contingency planning. • Introduces a GIN-based framework for n-1 contingency analysis in medium voltage grids. • Utilises graph neural networks to effectively model complex grid topologies. • Outperforms traditional optimisation method and classical GNN frameworks. • Fast and scalable n-1 contingency analysis achieves a 1000x speed-up in efficiency. • Generalises well to unseen grid structures and enabling real-time grid assessments. • Demonstrates machine learning in the power grid sector for real-time reliability assessments.