Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets
This work addresses the need for AI integration in power systems, specifically for distribution grid planning, representing an incremental step in an uncharted territory.
The paper tackled the problem of distribution grid investment planning by introducing Hyperstructures Graph Convolutional Neural Networks (Hyper-GCNNs), which achieved substantial gains in computational efficiency and outperformed seven state-of-the-art deep learning models.
Nowadays, it is broadly recognized in the power system community that to meet the ever expanding energy sector's needs, it is no longer possible to rely solely on physics-based models and that reliable, timely and sustainable operation of energy systems is impossible without systematic integration of artificial intelligence (AI) tools. Nevertheless, the adoption of AI in power systems is still limited, while integration of AI particularly into distribution grid investment planning is still an uncharted territory. We make the first step forward to bridge this gap by showing how graph convolutional networks coupled with the hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning with resilience objectives. We further propose a Hyperstructures Graph Convolutional Neural Networks (Hyper-GCNNs) to capture hidden higher order representations of distribution networks with attention mechanism. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency compared to the prevailing methodology in distribution grid planning and also noticeably outperforms seven state-of-the-art models from deep learning (DL) community.