  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.
