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

TL;DR

This paper introduces Graph Isomorphic Networks (GINs) to improve the assessment of medium-voltage grid reliability, offering faster and more reliable predictions than traditional methods by leveraging graph-structured data.

Contribution

The paper presents a novel application of GINs for n-1 reliability assessment in medium-voltage grids, demonstrating significant speed and reliability improvements over existing approaches.

Findings

GINs outperform traditional methods in prediction speed by a factor of 1000.

The approach generalizes well to unseen grid configurations.

Results indicate enhanced reliability and efficiency in grid assessments.

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.