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arXiv · 2310.01181

Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid

Charlotte Cambier van Nooten·Tom van de Poll·Sonja Füllhase·Jacco Heres·Tom Heskes·Yuliya Shapovalova
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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 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.

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Charlotte Cambier van Nooten, Tom van de Poll, Sonja Füllhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova. 2023-10-02. Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid. https://doi.org/10.1016/j.apenergy.2025.125401

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