About AGM at PNNL
POC: Yousu Chen
As the electric power grid becomes more complex, dynamic, and uncertain, it is important to improve its reliability, resiliency, security and flexibility. The industry and regulators need advanced tools to help them make better use of the vast amount of grid data to support their planning and decision making. This becomes particularly important under the uncertainty environment brought by renewable energy and advanced smart technologies applied to smart loads.
To address this need, the Office of Electricity in DOE has provided leadership in the research area of Advanced Grid Modeling (AGM) for many years. PNNL is one of the main supporting organizations for the AGM program.
This program uses advanced computing techniques, artificial intelligent and machine learning technology, advanced modeling and control approaches, quantitative risk and uncertainty methods, efficient data management and analytics tools, and advanced visualization tools to perform grid analytics, evaluate the real-time status of the grid, predict systems’ stability status and operating conditions to enable preventative actions, and provide significant support to system operation and planning.
This is achieved by providing faster and better decision-support that leads to a more reliable and efficient power grid. It benefits the industry by giving operators wide-area real-time visibility of the grid conditions and offering grid operation decision makers sophisticated methods with predictive capability. It also provides governmental policy makers with valuable analytical tools.
PNNL AGM research team is engaged in transforming the electric power gird analysis and tools using mathematical and computational methods. PNNL's primary areas of research under AGM program include:
- Adaptive RAS/SPS System Settings for Improving Grid Reliability and Asset Utilization through Predictive Simulation and Controls.
- Coordination of Transmission, Distribution and Communication Systems for Prompt Power System Recovery after Disasters
- EMS 2.0 -- A Hybrid State Estimator for Multi-Scale Grid Resilience and Reliability
- GridPACK™: An Open Source Framework for Developing High Performance Computing Simulations of the Power Grid
- Integrated State Estimation and Contingency Analysis
- Open-Source High-Fidelity Aggregate Composite Load Models of Emerging Load Behaviors for large-Sale Analysis
- Dynamic Paradigm for Grid Operation
- Stochastic Operations and Planning