Model aims to optimise black start grid restoration

A team of researchers at Sandia National Laboratories created a computer model aimed at helping grid operators quickly restore power to the electric grid after a blackout.

According to the Sandia scientists, the model combines a restoration-optimisation 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.

Researchers say the model can help provide insight into how individual power generators, distribution substations and power lines would react during the process of restoring power.

The model also can simulate black starts that are triggered by disruptions such as a successful cyberattack.

In terms of optimising restoring power, 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.

Arguello said an example of an 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. These, in turn, will provide power to substations B, C and D, as well as some critical infrastructure 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, Arguello said. This process is based off a similar model created by researchers at Lawrence Livermore National Laboratory and the University of California Berkeley.

“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,” said Arguello.

The model can also accurately approximate alternating current power flow, according to the researchers, which they say is more complex than direct current. The model also offers a more accurate representation of the grid during severe disruptions such as black start conditions.

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Modeling operator decision-making

The operator decision-making code plays an essential role in the overall model, researchers said.

This algorithm takes the results from the optimisation code and enacts it on a third code, which they described as a “physics-based simulation of the grid and how it dynamically responds to the operator’s actions.”

The decision-making model is based on a model created by scientists at Carnegie Mellon University, but adapted for power restoration by coding in expert knowledge about the steps required to start a generator and then connect it to the nearest substation.

This also includes safeguards so the cognitive model wouldn’t freeze if the grid behaved unexpectedly, Sandia scientists said.

The operator model interacts with the grid model through a simulated console and is limited to the knowledge presented by the console, rather than presuming the grid operator knows everything, which is typically assumed in power-restoration models.

Researchers said the operator model can assess whether the network model’s behavior matches up with what it is expecting based on the results of the optimisation algorithm. The simulated console can also allow the team to swap in actual feeds of information from the grid for the network dynamic model, if a partner provides the information, they added.

“Black starts are really rare, extreme events, but when one happens it’s really bad,” said Systems Analyst Casey Doyle. “Even in partial blackouts, like what happened in Texas in 2021, people died because they didn’t have power, they didn’t have heat. If you have a complete blackout, it’s likely that it would be caused by a hurricane or earthquake and operators are trying to restore power to whole communities. Delays in power restoration could cause even more damage or loss of life. It’s hugely impactful to understand how to bring the power back as quickly as possible.”

The three-year project was funded by Sandia’s Laboratory Directed Research and Development programme. The researchers are currently looking for sponsors to continue and expand the project.

This story was originally published by Kevin Clark on Power-Grid International.