Despite its age, the U.S. electric grid remains one of the great workhorses of modern life. Whether it can maintain that performance over the next few years may determine how well the U.S. competes in an AI-driven world.
AI is a big part of the challenge. Its vast data centers suck up energy like small cities. But a recent RAND study suggests AI could be a big part of the solution, too. There are risks here—some obvious, some not—and grid operators need to move with caution. But AI could usher in an energy future that is more resilient, more efficient, and more affordable for customers.
“The important thing is to do it without rocking the boat,” said Ismael Arciniegas Rueda, a former energy executive, now a senior economist at RAND. “The grid can fail, definitely, and I don't think people understand the consequences if that does happen. It's not just the lights going out. Our whole life depends on whether or not energy is available 100 percent of the time.”
Companies working with AI have warned that they are already struggling to find the power they need. Keeping them on U.S. soil has become a national imperative, especially in light of the deepening competition with China. That means upgrading and modernizing the grid, much of which was built in the 1960s and ‘70s.
AI could usher in an energy future that is more resilient, more efficient, and more affordable for customers.
Grid operators in the U.S. and globally have already started to use AI to monitor transmission lines and isolate faults. AI systems are also analyzing huge amounts of data in real-time to better predict fluctuations in supply and demand. They're creating a grid that operates by the second, much faster than human operators can respond.
Arciniegas Rueda wanted to know whether the benefits really pencil out—for customers as well as for companies. He teamed up with researchers from RAND Europe to look at two AI applications that have started to come online in recent years. One acts as a kind of cruise control for energy use in commercial and industrial buildings. It automatically adjusts their heating and cooling systems to maintain temperatures more efficiently. The other smooths out demand spikes, shifting energy use to lower-cost times of day. It might, for example, wait until evening to switch on a washing machine.
The best data the researchers could find came from Europe. So, they modeled one bitterly cold week there, when the grid strained to keep homes warm. They found that the cruise-control AI, known as load reduction, more than doubled energy reserves and reduced average costs by around 10 percent. The other AI, known as load shifting, had little effect on prices, but it did improve energy reserves and prevent sharp fluctuations in demand. A third AI application that the team looked at, automatic control of wind-farm turbines, produced almost no savings.
But then the researchers looked at the risks. Any new technology presents a new target for cyberattacks. But AI could also make confusing or harmful decisions on its own. And when it does, there might be no way to understand its reasoning or how to fix the problem. AI systems also have no human ethics, so if diverting energy away from older, less energy-efficient homes could maximize grid performance, it might not hesitate. An AI also could make erratic decisions in the middle of a natural disaster or other emergency not well-represented in its training data.
RAND researchers identified one other cause for concern in a recent working paper. They ran thousands of simulations of a working power grid. As they increased the number of grid operators with AI capabilities, something unexpected happened. The performance of the overall grid began to slip. Operators with AI started to make decisions from a much wider menu of options. Those without AI struggled to make sense of their often-unexpected moves and to respond as grid performance started to swing. The system “performs well,” researchers wrote, “until a critical mass of AI-enabled operators begins to take hold.”
AI can make parts of the grid run more efficiently, researchers concluded. But it still needs active and capable human oversight. Energy companies will be under tremendous pressure in the years to come to deploy AI to reduce costs. But they need to fully understand what they're rolling out before it goes live.
AI can make parts of the grid run more efficiently, but it still needs active and capable human oversight.
Regulators should develop so-called sandboxes where companies can test AI applications before deploying them to the grid. They should require power companies to report any use of AI in the electric system—and those companies should be open about their plans. After all, researchers wrote, the AI transition, with all of its potential benefits, will only succeed if policymakers and the public buy into it.
“AI can do a lot of good,” Arciniegas Rueda said. “It can predict problems before they happen; it can identify equipment that needs repair before it fails. It allows companies to integrate more kinds of energy, like wind and solar. But at the same time, we've seen what happens when regulation falls short, and things get out of hand. You get rolling blackouts in California. You get Enron.
“We cannot afford that kind of whiplash, especially not with all of these new data centers coming online, all of these new demands. The grid is the network of all networks. The key over the next few years is going to be fixing it without breaking anything.”
