The electric power industry has undergone extensive changes over the past several decades and has become substantially more complicated, dynamic, and uncertain. The adoption of new market rules, business models, regulatory policies, and technologies along with new energy components being integrated into the systems are just a few examples of the complexity with the industry. In addition to traditional requirements on operational stability and security, resilience, and cyber-security have become core concerns of system operators. These changes have created significant challenges to the operations and planning of electricity grid and demand more advanced analytical tools to make better decisions faster in a dynamic, complex, uncertain environment.
The Advanced Grid Modeling Program (AGM) under Department of Energy’s Office of Electricity (OE) supports the nation’s foundational capacity to analyze the electric power system using big data, advanced mathematical theory, and high-performance computing to assess the current state of the grid and understand future needs. Under the leadership and support of AGM program, significant breakthroughs have been made in mathematical modeling, computing and simulation methods, and operation/planning tools.
Argonne’s expertise in grid resilience, advanced power system optimization and computing, and artificial intelligence helps support OE’s objectives in tackling power grid issues.
- Accurate and Tractable Cascade Failure Models for Resilience-Based Decisions in the Power Grid
- Advanced computational algorithms for power system restoration
- A General Framework for AI-Accelerated Power Systems Optimization
- Macro-Resiliency of the North American Power
- Measurement-Based Hierarchical Framework for Time-Varying Stochastic Load Modeling
- NERC SPIDERWG Support – Studies of System Planning Impacts from Distributed Energy Resources Using T&D-co-simulation
- A Novel Security Analysis Toolbox for National Grid Resilience Modeling
- Outage Prediction and Grid Vulnerability Identification Using Machine Learning on Utility Outage Data
- Scalable Deep Learning Framework for Resilient Grid Operations Under Contingency Events in Power Systems