The Mission Campaign is organized around three research thrusts answering fundamental questions that are relevant to improving resilience among US energy systems and other critical infrastructures. An integrated portfolio of projects across these three thrusts will ultimately result in a cohesive set of capabilities .
Thrust 1: Science of Vulnerabilities
Question: What are the vulnerabilities and physical failure mechanisms of node electrical hardware and control equipment for intentional and sophisticated kinetic, EMP, GMD, and cyber attacks?
Outcome: New tools to assess vulnerabilities and risk in the energy and critical infrastructure system.
- Dynamic Monitoring to Analyze Grid Vulnerability to Fire
- ADROC: Emulation Experimentation Platform for Advanced Resilience of Control Systems
- The "HARMONIE" Special Protection Scheme
- Hallucinating Canaries
- Low-Cost HEMP Testing
Thrust 2: Materials, Device, & Cyber Innovation
Question: What novel materials, devices, and cyber advances will most cost effectively increase resilience?
Outcome: New high-energy materials, cost effective physical and cyber solutions.
- Nano structural Granular Metals for Improved EMP Protection
- Discovery of New High-Frequency Magnetic Materials Enabled by Artificial Intelligence
- Signal-Based Fast-Tripping Protection Schemes for Electric Distribution System Resilience
- Solid-State Transformer Technology
Thrust 3: System-Level Threat-Informed Computational Science
Question: What are the critical elements of the electrical energy system, mechanisms of cascading failures, and potential approaches for a more resilient/secure architecture?
Outcome: Tools to analyze and asses how to optimize and deploy solutions for maximum benefit (economics, security, and resilience)
- Critical Node Identification, Vulnerability Modeling and Topology Optimization for the Electric Grid
- Development of Power System Models for Optimal Restoration Subject to Intentional Threats
- Dynamics-Informed Optimization for Resilient Energy
- REDLY: Resilience Enhancements Through Deep Learning Yields
- Power System Vulnerability Identification Through Deep Reinforcement Learning
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