Advanced Grid Modeling Research Program
We support the nation’s foundational capacity to analyze the electric power system using advanced mathematical theory, optimization, big data and high-performance computing to support innovation in power system planning, operation and analysis. Berkeley Lab is one of the National Labs contributing to this program through the projects described below.
Advanced Grid Modeling Projects
Risk-controlled Expansion and Planning with Distributed Resources (REPAIR)
The Risk-controlled Expansion Planning with Distributed Resources (REPAIR), developed by Lawrence Berkeley National Lab, is an innovative tool to support decisions around utility grid planning to prevent and mitigate the impact of outages caused by routine equipment failures (reliability) or by extreme events (resilience), such as storms, earthquakes or wildfires that long term interruption of service.
More information about REPAIR can be found here.
Scientific Machine Learning for Simulation and Control in Large Scale Power Systems
This project aims to develop new tools at the intersection of scientific machine learning (SciML) and power systems engineering. These tools will accelerate the simulation of power systems with high penetration of power electronic converters (PECs), to ensure that we can simulate these systems in near-real time. This acceleration will be achieved by 1) using SciML to develop accurate models of aggregations of PECs in order to reduce the number of equations we need to solve and 2) using SciML to improve the mathematical techniques we use for solving these equations.
More information can be found here.
