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 principle, ensuring continuous operation in case of component failure. Electricity networks' complex graph-based data holds crucial information for n-1 assessment: graph structure and data about stations/cables. Unlike traditional machine learning methods, Graph Neural Networks (GNNs) directly handle graph-structured data. This paper proposes using Graph Isomorphic Networks (GINs) for n-1 assessments in medium voltage grids. The GIN framework is designed to generalise to unseen grids and utilise graph structure and data about stations/cables. The proposed GIN approach demonstrates faster and more reliable grid assessments than a traditional mathematical optimisation approach, reducing prediction times by approximately a factor of 1000. The findings offer a promising approach to address computational challenges and enhance the reliability and efficiency of energy grid assessments.

翻译：随着可再生能源转型和传统发电容量减少，确保电网可靠性变得愈发具有挑战性。配电系统运营商（DSO）旨在通过验证n-1原则来实现电网可靠性，即确保在组件故障情况下仍能持续运行。电网的复杂图结构数据包含对n-1评估至关重要的信息：图结构以及变电站/电缆数据。与传统机器学习方法不同，图神经网络（GNN）可直接处理图结构数据。本文提出使用图同构网络（GIN）进行中压电网的n-1评估。该GIN框架被设计为可泛化至未见过的电网，并利用图结构及变电站/电缆数据进行评估。与传统的数学优化方法相比，所提出的GIN方法能够实现更快、更可靠的电网评估，预测时间缩短约1000倍。该研究成果为解决计算挑战、提升能源电网评估的可靠性与效率提供了一种有前景的方案。