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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The present invention relates to PMU-based control systems for dampening inter-area oscillations in large-scale interconnected power systems or grids to protect against a catastrophic blackout. The control systems receive phasor measurements from two or more locations on an AC transmission line and generates a power control command to a power resource on the grid.
Abstract not provided.
Cyberattacks come in many forms, including one of the newest and most threatening of attacks: ransomware. The rise in ransomware impacts businesses small and large -- and have the potential to significantly disrupt the operations of critical infrastructure. This presentation will explain what utilities can expect from a ransomware attack, sharing examples from specific events, provide guidance on how to prepare for the unknown, and offer possible ways to respond when an attack occurs. The presentation will also discuss cybersecurity governance as an effective method in taking preventative action to ransomware by identifying and addressing vulnerabilities through monitoring and assessment.
Abstract not provided.
Abstract not provided.
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An overview of Sandia National Labs Wildfire Grid Resilience Program aimed to mitigate grid ignited wildfires and reduce the consequence to critical infrastructure.
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The rapid integration of artificial intelligence (AI) in the utility transmission and distribution (T&D) sector is revolutionizing traditional grid management practices. As utilities encounter complexities from evolving consumer behaviors and energy integration, AI becomes a critical solution for enhancing grid monitoring, fault detection, and operational optimization. However, increased reliance on interconnected technologies introduces significant cybersecurity risks, regulatory compliance challenges, and human factors concerns. This study proposes a strategic, responsible and consequence-driven approach to AI implementation, examining the dual nature of AI adoption by highlighting its transformative benefits for utilities and associated risks. It provides utilities with a framework for evaluating AI integration, enabling them to navigate challenges and capitalize on opportunities to achieve greater reliability, efficiency, and resilience in an increasingly complex energy landscape.
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.