“When we start with the consequence, the first step is to identify critical failures that can cause it. Squirrel finds critical failure points in an automated way.”
– Jovana Helms
What would it take for an entire American city to lose power? What circumstances and failures in the electrical grid’s infrastructure would lead to a dramatic, long-term blackout? And what weak points could utility companies invest in to help prevent a catastrophic shutdown?
A three-year LLNL project is attempting to answer those questions using a new algorithm called “Squirrel” to model power outages and enable government agencies and utilities to automatically identify weaknesses in the power grid. Squirrel is part of a project aimed at determining the risk to the grid from a cyberattack, called the Quantitative Intelligent Adversary Risk Assessment. But because Squirrel is “cause agnostic,” according to project manager Jovana Helms, it can be used with any kind of threat or hazard, including a malicious hack, earthquake, or even squirrels (which often chew into electrical wires and cause outages).
“Squirrel is part of a methodology to assess the risk by identifying critical failures,” Helms says. “It solves the inverse problem: for a given consequence of interest it enumerates critical failures that can lead to that consequence. It tells you where to pay attention and how to prioritize your resources. Once you enumerate critical failures, you can determine which hazards can cause it and develop mitigations that are hazard agnostic or tailored to a specific hazard.”
Researchers say one of the major challenges in determining risks to the grid is the cascade effect. If one substation fails, it could impact the entire grid infrastructure. Using Squirrel, in conjunction with GridDyn, an open-source power grid simulator developed at LLNL that models transmission power flow, researchers analyzed what series of actions would have to happen to cause a 500-megawatt load loss on a small grid model. Surprisingly, Helms said, the simulation found 730 critical failures of consequence, including the most susceptible relays and grid components. In about half of all critical failures, one particular relay was consistently part of the enumerated failures. Such insight could be particularly crucial when resources for bolstering grid resiliency are limited, researchers said.
“Let’s say our concern is that a bad actor will take one gigawatt of power offline. We’re looking at what are the ways that could happen,” says principal investigator Meghan McGarry. “Squirrel helps us identify what critical failures would lead to that outcome. We want to know what input conditions are required for the load loss of our output. What changes do I have to make to the input to lose that one gigawatt?”
Squirrel, the researchers said, could allow government agencies and public utilities to narrow the list of possible scenarios that could lead to catastrophic failures and determine where to prioritize protection, which would be virtually impossible with manual methods. Using the Lab’s high-performance computing capabilities, the algorithms are able to work off the GridDyn model, change various parameters, and look at potential solutions that could stave off a massive outage.
LLNL researchers will further develop Squirrel’s modeling capabilities in the coming years and push for more complex, coupled models that can consider combined consequences like communication and power flow, gas, and electricity, as well as more complicated transmission and distribution models. Follow-on work could involve collaborating directly with utility companies to identify vulnerabilities and perform risk assessment using actual grid systems from the utilities to create more accurate models. Squirrel has garnered interest from utility partners. If successful, researchers said, Squirrel could help utilities and government agencies increase the resilience of the grid against any type of impactful situation and could later be applied to oil and gas pipelines and transportation.
“Right now, we have a simple model that might have 46 transmission lines that can be switched on or off,” McGarry said. “Even in a simplified grid, you’re already in a problem space that’s 2⁴⁶ possible configurations, which is too big to search with brute force. The main aspect now is developing algorithms for a more intelligent analysis of the space. Our goal is to model a 10,000-line system, that would be a useful scale. But even at the level we are now, you can determine components that are critical to the system.”