arXiv:2607. 13221v1 Announce Type: cross Abstract: Real-time N-1 contingency screening in an energy management system trades assurance against cost: verifying every credible outage with full power flow is too slow, while fast linear-sensitivity screening gives no statistical guarantee and can silently pass unsafe operating points, especially when a controller drives the system into unfamiliar regimes.
By Jayakumar Manoharan
arXiv:2606. 24298v1 Announce Type: cross Abstract: The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable transparent and reproducible evaluation.
By Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.
By Chenhan Xiao, Xinyu He, Haoran Li, Hanghang Tong, Yang Weng
arXiv:2607. 16031v1 Announce Type: cross Abstract: Data-driven pre-fault dynamic security assessment (DSA) rapidly evaluates the dynamic risk of credible contingencies on a power system using machine learning.
By Olayiwola Arowolo, Maosheng Yang, Jochen Cremer
arXiv:2608. 15391v1 Announce Type: new Abstract: Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility.
By Md Fazley Rafy
Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but syntactic validity does not imply physical admissibility. This pap...
The paper introduces a finite‑sample probabilistic safety certification framework for black‑box AI decision models used in closed‑loop grid operation. It transforms the AI‑grid evaluation into a binary unsafe outcome under a safety specification and applies exact binomial inference to provide a tight one‑sided upper bound on the unsafe operation probability, using held‑out calibration scenarios. The framework also incorporates physically interpretable sample‑space adversarial attacks to address distribution shifts and is validated through case studies involving 1,000‑agent AI models for grid‑edge flexibility coordination.
By Yihong Zhou, Hanbin Yang, Thomas Morstyn
Grid‑Orch is a framework that connects Large Language Models (LLMs) to power system simulation via the Model Context Protocol (MCP), allowing engineers to conduct complex distribution grid analyses using natural language. It offers 36 domain‑specific tools across eleven categories—including power flow, voltage analysis, quasi‑static time‑series simulation, and automated optimization—implemented with OpenDSS as the reference engine. The platform supports both cloud‑hosted and locally deployed LLMs, enabling air‑gapped operation, and demonstrates that tasks such as DER interconnection screening can be completed in under two minutes with results identical to traditional scripting.
By Boming Liu, Jin Dong, Jianming Lian
EvEMTBench is an open, executable, and versioned benchmark designed to standardize the evaluation of machine‑learning methods for power system protection. It defines 12 protection and event‑analysis functions across four grids (20–345 kV) as 24 scored tasks, enabling structured assessment under varied observability, distribution shifts, and cross‑grid transfer scenarios. The benchmark includes committed data partitions, leakage controls, and reproducible reporting, and demonstrates that wider observability does not always help, that shifted conditions expose hidden failures, and that fault detection transfers better than fault localization.
By Julian Oelhaf, Georg Kordowich, Christian Bergler, Andreas Maier, Johann J\"ager, Siming Bayer
arXiv:2606. 15964v1 Announce Type: cross Abstract: Foundation models are now used in settings where the prompts they receive can change quickly.
By Jeffery Opoku, David Banahene
arXiv:2505. 02299v2 Announce Type: replace-cross Abstract: Machine Learning (ML) models are trained on in-distribution (ID) data but often encounter out-of-distribution (OOD) inputs during deployment---posing serious risks in safety-critical domains.
By Daisuke Yamada, Harit Vishwakarma, Ramya Korlakai Vinayak
arXiv:2308. 07867v4 Announce Type: replace-cross Abstract: The absence of formal performance guarantees in machine learning (ML) has limited its adoption for safety-critical power system applications, where confidence and interpretability are as vital as accuracy.
By Parikshit Pareek, Sidhant Misra, Deepjyoti Deka