Concern over how much artificial intelligence will strain the power grid just continues to mount. In fact, from 2022 to 2023, utilities across the country doubled projections for load growth in the next five years.
But it’s unclear precisely how much electricity AI actually needs.
- The top line: Experts point out that the correlation between AI and energy use isn’t direct: more of one doesn’t inevitably mean more of the other. According to Alp Kucukelbir, co-founder and chief scientist at Fero Labs, on a recent episode of Columbia Energy Exchange the technology’s applications in the power sector could — if utilities themselves play their cards right — result in a cleaner, more efficient, and reliable grid.
- The outlook: Globally, power demand from generative AI could increase at a 70% compound annual growth rate, said David Sandalow, who is a former official with the Department of Energy and is currently a distinguished visiting fellow at Columbia University researching the overlap of energy use and AI, on the same episode. As AI creeps into almost every industry, it is driving more demand for data centers and computing power. However, there’s still room for the supporting hardware and software to increase in efficiency. And the technology can also create efficiencies in the power system, and help integrate more clean resources to meet rising demand.
Forecasts vary on how much power AI will require in the next decade.
“The recent rise of AI is…putting pressure on data centers that large technology companies are operating,” explained Kucukelbir on an episode of Columbia Energy Exchange, adding that “this type of computation has been traditionally serving things like the e-commerce sector, search in general, and other…’commodity applications’.”
An “enormous” amount of growth is already being forecast in these areas, especially for large language models. That application, he said, “is new and is not fundamentally based on computation,” unlike applications like cryptocurrency mining. (Bitcoin specifically relies on a principle called proof of work, which requires a certain amount of computational power to be used to verify a new “block” on the chain.)
That isn’t the only way that AI is fundamentally different from crypto, though: “We already see the academic community, scientific community, working towards reducing the energy required to achieve similar outcomes in AI,” Kucukelbir said. “This is a statement I can’t make in crypto.”
He added that hardware manufacturers are already thinking about how new electronics and chips will reduce energy consumption and achieve similar outcomes to today’s large language models.
“Think about how computation looked…in the 1970s, 1980s,” he said. “We can replicate, if not far exceed, the supercomputers of those decades with the devices in our pockets, who draw, I don’t know how many orders of magnitude less energy.”
Applications for the power system
The intersection of artificial intelligence and energy is not limited to power demand, though. The technology can also help solve problems related to siting and permitting, renewable intermittency, and power flow, according to both Kucukelbir and Sandalow.
“I think we need to be ambitious and creative about using AI tools to help get over the challenges that we’re facing right now in managing the electric grid,” said Sandalow.
The three main ways the technology can be used are pattern recognition, forecasting or predicting, and optimization.
Pattern recognition is useful for sifting through large amounts of data. Today, it’s commonly used by search engines to find commonalities among all the words typed into a search bar. This type of data is characterized as unstructured, and Kucukelbir says it “physically cannot” be structured. But machine learning can identify patterns in that lack of structure instantaneously. And that could help, for instance, make buildings more energy-efficient by making sense of their energy use tendencies.
Meanwhile, the use cases of forecasting and predicting are frequently used interchangeably. But Kucukelbir said there is a key difference between the two: the time period over which the prediction occurs. Forecasting involves looking further into the future, and is essentially predicting over a specific time horizon. Prediction is commonly used today at solar and wind farms to maximize the output of variable resources, for example via tilting a solar panel toward the sun or turning a turbine to catch more wind.
Forecasting further into the future, though, is useful for determining where to build those renewables, said Sandalow: “Any siting of electricity generation assets can benefit from AI technologies in terms of both weather and power demand in the area.”
Optimization, however, complicates things. When predicting, a user feeds inputs to a model and asks for an outcome. But with optimization, a user creates an outcome, and asks the model what inputs are needed to achieve it.
This, Kuckelbir said, is “arguably” the most valuable to users, at least when applied well. For instance, this type of machine learning can be used to optimize how power flows through the grid. Current algorithms can take hours or longer to run power flow optimizations, but AI can both speed things up and make them more accurate, Kucukelbir added.
“Optimal power flow, I would say is…a problem space that would make the typical champions of AI very afraid,” Kucukelbir said. “You’ve got physical constraints of how energy is going to flow over a particular grid, network, or topology, and you need to satisfy physical requirements,” which is why approximations need to be as close to a perfect solution as possible. He added that AI is particularly well-suited to factoring in complexities like variable power supply or unmodeled demand.
That technology is already progressing rapidly, according to Sandalow.
“All across the power sector this technology can make a big difference,” he said. “And it’s already starting to happen, but it’s going to I think progress dramatically in the years ahead.”
Listen to the full episode on Columbia Energy Exchange:
This story borrows from an interview that appeared on the Columbia Energy Exchange, a Latitude Studios partner podcast.
Columbia Energy Exchange is a show that features in-depth conversations with the world’s top energy and climate leaders from government, business, academia, and civil society, produced by Latitude Studios for Columbia University. Follow on Apple, Spotify, or wherever you get your shows.
