Graph Machine Learning: An Opportunity for Power Systems
2026-08-17 • Machine Learning
Machine LearningArtificial IntelligenceComputational Engineering, Finance, and Science
AI summaryⓘ
The authors review about 800 research papers on using graph machine learning (GML) to help manage modern power systems, which are getting more complex due to renewables and decentralization. They explain that GML naturally fits power grids because it considers how parts of the network connect and interact. While GML can make operations faster and potentially more efficient, the authors point out challenges like few real-world applications, the need for models that are easy to understand, and a lack of common datasets for testing. To improve the field, they suggest creating standardized benchmarks and sharing data openly.
power systemsgraph machine learninggrid topologystate estimationreal-time decision-makingrenewable energybenchmark datasetsmodel interpretabilityoptimizationcybersecurity
Authors
Martin Sadric, Sebastian Pütz, Christian Nauck, Veit Hagenmeyer, Frank Hellmann, Dirk Witthaut, Benjamin Schäfer
Abstract
Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales. Addressing these challenges traditionally relies on model-based methods that, while accurate, can be too slow for operational demands. Machine learning (ML) has therefore emerged as a faster, data-driven alternative. As grid topology plays a central role in power system operation, graph machine learning (GML) methods offer a natural framework for incorporating topological dependencies as an inductive bias. We survey nearly 800 papers at the intersection of GML and power systems, covering forecasting, state estimation, optimization, control, fault diagnosis, and cybersecurity. Power systems constitute an unusually rich benchmark setting for GML, as they combine hard physical constraints, multi-scale dynamics, safety-critical requirements, and scarce labeled data within a single, well-defined domain. Conversely, power systems can benefit from utilizing GML to complement classical solvers, as GML provide scalable, topology-aware approximations with promising generalization and computational efficiency. We identify open challenges, including limited real-world deployment and the need for interpretable models in safety-critical settings. Despite the rapidly growing number of publications, standardized benchmarks and open datasets remain scarce, leaving many results difficult to reproduce and undermining the long-term scientific credibility of the field. We further derive a structured requirements catalog for ML-ready power grid benchmarks, intended to guide future dataset development and improve reproducibility across studies. We call on the community to prioritize dedicated benchmark studies and the release of open datasets and models.