In recent years, the electric power grid and other critical energy systems have become increasingly complicated, interconnected, and dynamic. These challenges are driven by factors like new technology deployments, evolving regulatory policies, and changing market environments. In spite of this additional complexity, the grid must continue to meet the nation’s growing energy needs. Grid operators and planners need advanced tools to utilize the vast amounts data now available about system conditions, which enable them to make optimized decisions and improve electric delivery system efficiency, reliability, resilience, and security.
To address this need, the DOE Office of Electricity’s Advanced Grid Modeling (AGM) program supports building capacity and capability within the electric sector to analyze the electricity delivery system using Big Data, advanced mathematical theory, and high-performance computing to assess the current state of the grid, mitigate reliability risks, and understand future needs. Los Alamos contributes to the AGM program and applies its expertise in optimization, control, machine learning, and statistical sciences.
Los Alamos AGM Focus Areas
- Theoretical and algorithm development for complex energy infrastructure optimization and control problems
- Third-party, independent, science-based input into complex problems of national concern
- Scientific analysis to turn down the noise around complex problems
The Los Alamos AGM team is composed of experts in physics, statistical physics, power engineering, applied mathematics, statistics, optimization, computer science, machine learning, chemical engineering, control theory, geospatial analysis, software development, and cloud computing.
AGM Project Highlights
- Grid Science Winter School and Conference (pdf)
- Robust Real-Time Control, Monitoring, and Protection of Large-Scale Power Grids in Response to Extreme Events (pdf)
- Hybrid Learning Assisted Optimization Methods for Uncertainty Management and Corrective Control (pdf)
- Weather Outage Prediction Model (pdf)
- Optimized Resilience for Distribution and Transmission Systems (pdf)
- SpaceWeather Mitigation Planning (pdf)