Graph neural networks for assessing the reliability of the medium-voltage grid

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.