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

• URL: http://arxiv.org/abs/2310.01181v2
• Date: Tue, 3 Oct 2023 09:42:23 GMT
• Title: Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid
• Authors: Charlotte Cambier van Nooten, Tom van de Poll, Sonja F\"ullhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova
• Abstract summary: 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.
• Score: 0.5242869847419834
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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