How AI is Revolutionizing the Grid: Efficiency, Reliability, and Resilience
How can AI help make the grid more efficient, reliable, and resilient? Today, we’ll tackle some promising use cases on the supply side, in the bulk power system.
With transmission, AI can help with predictive maintenance. Operationally, it can help boost the performance of transmission lines by assisting certain grid enhancing technologies (known as GETs) that make more efficient use of existing infrastructure. Dynamic line rating replaces the historical method of limiting capacity based on static ratings in favor of an approach looking at actual ambient conditions. Lower temps and higher wind speeds pull heat from lines, allowing them to move more power, in some cases as much as 50% more.
That helps limit congestion bottlenecks and aids with the interconnection of more generating assets, and is most helpful to wind assets, since logically during the same periods when wind turbine output is high, that same wind is dissipating heat from the lines.
Then there’s topology optimization - opening and closing breakers to route power differently, facilitating higher utilization of assets. AI can help by more quickly assessing a wider variety of scenarios.
Then there’s interconnection, a big problem today. In 2000, it only took two years. Planners were dealing with fewer and far larger projects – mostly big gas and coal plants, with only about 300 projects in the queue. That number is now over 10,000. AI can help cut time required to evaluate scenarios and increase the number of scenarios that can be assessed.
On the generation side, gas generators can be run more efficiently based on operating conditions rather than prescribed schedules. Algorithms applied to data from sensors can tell grid operators how hard they can run a turbine, and better understand when to take turbine out for maintenance, rather than relying on fixed schedules.
AI also helps generate longer term and more geographically precise weather forecasts which help supply asset operators refine output projections and dispatch strategies, while optimizing utility scale battery storage and dispatch as well.
Within a wind farm, AI can minimize the disruptions in wind flow affecting downwind turbines by steering wakes and optimizing output. This can cut land requirements for future wind plants by an average of 18% and up to 60%.
AI can also help advanced geothermal projects that extract heat from solid rock miles underground and use it to generate power. Machines and algorithms can tell operators where to drill, physically guide the drill bit through rock, predict reservoir behavior and determine how much heat to extract from a given area over a specified duration.
Some of these applications are already happening with AI related to machine learning. But as the large language models become increasingly powerful and more sophisticated, the ability to develop generative AI – to understand the patterns of existing data and then generate new data to improve decision-making will take us to the next level.
If these AI-driven datacenters are going to stress the grid with all this new demand, we might as well get as much value out of these new capabilities as we possibly can.