America’s power grid system is not only large but dynamic, which makes it especially challenging to manage.
America’s power grid system is not only large but dynamic, which makes it especially challenging to manage. Human operators know how to maintain systems when conditions are static. But when conditions change quickly, due to sudden faults for example, operators lack a clear way of anticipating how the system should best adapt to meet system security and safety requirements.
At the U.S. Department of Energy’s (DOE) Argonne National Laboratory a research team has developed a novel approach to help system operators understand how to better control power systems with the help of artificial intelligence. Their new approach could help operators control power systems in a more effective way, which could enhance the resilience of America’s power grid, according to a recent article in IEEE Transactions on Power Systems.
Converging dynamic and static calculations
The new approach allows operators to make decisions considering both static and dynamic features of a power system in a single decision-making model with better accuracy — a historically tough challenge.
“The decision to turn a generator off or on and determine its power output level is an example of a static decision, an action that does not change within a certain amount of time. Electrical frequency, though — which is related to the speed of a generator — is an example of a dynamic feature, because it could fluctuate over time in case of a disruption (e.g., a load tripped) or an operation (e.g., a switch closed),” said Argonne computational scientist Feng Qiu, who co-authored the study. “If you put dynamic and static formulations together in the same model, it’s essentially impossible to solve.”
Read more at DOE/Argonne National Laboratory
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