图机器学习：电力系统的一个机遇
Graph Machine Learning: An Opportunity for Power Systems
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- Helmholtz AI（亥姆霍兹人工智能）
- Potsdam Institute for Climate Impact（波茨坦气候影响研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究调研近800篇图机器学习与电力系统交叉论文，分析其在电力系统多场景的应用价值，指出领域现存挑战，推导电网基准需求目录并呼吁优先开展基准研究与开放资源发布。
AI中文摘要：
现代电力系统因可再生能源整合、去中心化以及跨多时间尺度实时决策的需求，面临日益增长的运行复杂性。传统上解决这些挑战依赖基于模型的方法，这类方法虽准确，但可能无法满足运行需求的速度要求。因此，机器学习（ML）作为一种更快的数据驱动替代方案应运而生。由于电网拓扑在电力系统运行中处于核心地位，图机器学习（GML）方法提供了一个自然框架，可将拓扑依赖关系作为归纳偏置纳入。我们调研了近800篇GML与电力系统交叉领域的论文，涵盖预测、状态估计、优化、控制、故障诊断及网络安全。电力系统构成了GML异常丰富的基准场景，因为它在单一明确的领域内结合了严格的物理约束、多尺度动态、安全关键要求以及稀缺的标注数据。相反，电力系统可利用GML补充经典求解器，因为GML提供了可扩展、感知拓扑的近似，且具有良好的泛化性和计算效率。我们确定了开放挑战，包括有限的实际部署以及安全关键场景中对可解释模型的需求。尽管相关出版物数量迅速增长，但标准化基准和开放数据集仍然稀缺，使得许多结果难以复现，损害了该领域的长期科学可信度。我们进一步推导了面向ML的电网基准的结构化需求目录，旨在指导未来数据集开发并提高研究间的可复现性。我们呼吁学术界优先开展专门的基准研究，并发布开放数据集和模型。
英文摘要：
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.
