SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids

TL;DR

SafePowerGraph is a comprehensive, safety-focused benchmark framework for evaluating Graph Neural Networks in power grid operations, emphasizing robustness and realistic safety-critical scenarios to improve operational reliability.

Contribution

It introduces the first safety-aware, simulator-agnostic benchmark for GNNs in power systems, integrating multiple simulators and diverse scenarios for robust evaluation.

Findings

Self-supervised learning enhances GNN robustness.

Graph attention architectures outperform other models.

Safety-critical scenario evaluation reveals vulnerabilities.

Abstract

Power grids are critical infrastructures of paramount importance to modern society and their rapid evolution and interconnections has heightened the complexity of power systems (PS) operations. Traditional methods for grid analysis struggle with the computational demands of large-scale RES and ES integration, prompting the adoption of machine learning (ML) techniques, particularly Graph Neural Networks (GNNs). GNNs have proven effective in solving the alternating current (AC) Power Flow (PF) and Optimal Power Flow (OPF) problems, crucial for operational planning. However, existing benchmarks and datasets completely ignore safety and robustness requirements in their evaluation and never consider realistic safety-critical scenarios that most impact the operations of the power grids. We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for GNNs in PS operations. SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages. Our extensive experiments underscore the importance of self-supervised learning and graph attention architectures for GNN robustness. We provide at https://github.com/yamizi/SafePowerGraph our open-source repository, a comprehensive leaderboard, a dataset and model zoo and expect our framework to standardize and advance research in the critical field of GNN for power systems.