Self-Supervised Graph Neural Networks for Optimal Substation Reconfiguration
This work addresses the challenge of real-time decision-making in power grid management by providing a faster, though slightly less optimal, alternative to prohibitively slow optimization methods.
The paper tackles the problem of finding optimal switch states in substations to maximize exchange capacity, framing it as an amortized optimization problem using a self-supervised Graph Neural Network (GNN). The GNN model achieves a 10.2% average improvement in exchange capacity with drastically smaller computing times compared to a classical MILP solver, which reaches 15.2% improvement but with orders-of-magnitude larger times.
Changing the transmission system topology is an efficient and costless lever to reduce congestion or increase exchange capacities. The problem of finding the optimal switch states within substations is called Optimal Substation Reconfiguration (OSR), and may be framed as a Mixed Integer Linear Program (MILP). Current state-of-the-art optimization techniques come with prohibitive computing times, making them impractical for real-time decision-making. Meanwhile, deep learning offers a promising perspective with drastically smaller computing times, at the price of an expensive training phase and the absence of optimality guarantees. In this work, we frame OSR as an Amortized Optimization problem, where a Graph Neural Network (GNN) model -- our data being graphs -- is trained in a self-supervised way to improve the objective function. We apply our approach to the maximization of the exchange capacity between two areas of a small-scale 12-substations system. Once trained, our GNN model improves the exchange capacity by 10.2% on average compared to the all connected configuration, while a classical MILP solver reaches an average improvement of 15.2% with orders-of-magnitude larger computing times.