POWE-GRID RESILIENCERestoring Power to the Grid

Published 31 January 2023

Computer scientists have been working on an innovative computer model to help grid operators quickly restore power to the electric grid after a complete disruption, a process called a black start.

Sandia computer scientists have been working on an innovative computer model to help grid operators quickly restore power to the electric grid after a complete disruption, a process called a black start.

Their model combines a restoration-optimization model with a computer model of how grid operators would make decisions when they don’t have complete knowledge of every generator and distribution line. The model also includes a physics-based understanding of how the individual power generators, distribution substations and power lines would react during the process of restoring power to the grid.

“We’ve spent a lot of time thinking about how we go beyond simply looking at this as a multilayered optimization problem,” said project lead Kevin Stamber. “When we start to discuss disruptions to the electric grid, being able to act on the available information and provide a response is critical. The operator still has to work that restoration solution against the grid and see whether or not they are getting the types of reactions from the system that they expect to see.”

The overarching model also can simulate black starts triggered by human-caused disruptions, such as a successful cyberattack.

Optimizing Power Restoration
The optimization portion of the model assesses the grid and its components to determine how to restore power as quickly as possible, said Bryan Arguello, a Sandia computer scientist who worked on this section of the model.

For example, the optimal approach might be to start with generator 1 to power up substation A. Once substation A is energized, generators 2-4 can safely power up, which in turn will provide power to substations B, C and D, as well as some critical infrastructures such as a water purification plant or an area hospital. Once substation D is energized, power plants 5-8 can power up, and so on until power is restored to the entire grid.

Once the power-restoration schedule is developed, the algorithm compares it against physical limitations to determine if the schedule is feasible, Bryan said. “The challenge here is bringing in just the right amount of information so that the model can make wise decisions, without bogging it down in too much detail.”

The restoration optimization portion is based off a similar model created by researchers at Lawrence Livermore National Laboratory and the University of California, Berkeley, which strategically adds more details to the model as the algorithm progresses, Bryan said.