Advanced Grid Modeling

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.

REPAIR is a risk-based optimization and decision-making model allowing informed and transparent “cost vs risk” decisions regarding infrastructural planning of electric utilities. The model considers long-term resilience and reliability planning strategies that rely on traditional infrastructure upgrade (e.g. circuit hardening, reinforcement, new substations, etc.) or new investment alternatives, such as DERs.

Scientific Machine Learning for Simulation and Control in Large Scale Power Systems

With the growing penetration of wind, solar, and storage technologies, all interfaced to the grid via fast-acting power electronic converters (PECs), our power systems are rapidly evolving. One of the challenges with increased PECs is conducting computer time-series simulations of these systems, critical for understanding, and reliably operating, our electrical networks. The addition of PECs is resulting in these simulations taking significantly longer, under current approaches, due their fast response rates, and spatial diversity.

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 PEC 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.

In addition to producing reports and journal publications, all code will be developed in the Julia programming language and made available as open-source libraries.

 

Team Members

Program Leader
Staff Scientist
Staff Scientist
Scientific Engineering Associate
Senior Scientific Engineering Associate
Postdoctoral Researcher