There may be growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and repair interruptions, and customarily slow the transition to scrub energy, the usage of artificial intelligence may help the energy transition.
For instance, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. As well as, AI helps to optimize the design and siting of latest wind and solar installations and energy storage facilities.
On electric power grids, using AI algorithms to manage operations helps to extend efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to forestall failure and possible blackouts. AI will help grid planners schedule investments in generation, energy storage, and other infrastructure that can be needed in the longer term. AI can be helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.
Researchers at MIT and elsewhere are actively investigating features of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member corporations considering addressing the challenges of information center power demand.
Controlling real-time operations
Customers generally depend on receiving a continuous supply of electricity, and grid operators get help from AI to make that occur — while optimizing the storage and distribution of energy from renewable sources at the identical time.
But with more installation of solar and wind farms — each of which offer power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the image,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Lively-Adaptive Control Laboratory. “Essentially, you have to introduce a complete information infrastructure to complement and complement the physical infrastructure.”
The electricity grid is a posh system that requires meticulous control on time scales starting from many years all the way in which right down to microseconds. The challenge might be traced to the fundamental laws of power physics: electricity supply must equal electricity demand at every easy, or generation might be interrupted. In past many years, grid operators generally assumed that generation was fixed — they might count on how much electricity each large power plant would produce — while demand varied over time in a reasonably predictable way. Consequently, operators could commission specific power plants to run as needed to fulfill demand the following day. If some outages occurred, specially designated units would initiate as needed to make up the shortfall.
Today and in the longer term, that matching of supply and demand must still occur, at the same time as the variety of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a way of achieving the complex management of knowledge needed to forecast inside just a couple of hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.
Furthermore, AI could make possible recent ways of accelerating supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), in addition to the one charged up by solar panels or wind turbines, can — when needed — function a source of additional power to be fed into the grid. And given real-time price signals, EV owners can decide to shift charging from a time when demand is peaking and costs are high to a time when demand and due to this fact prices are each lower. As well as, recent smart thermostats might be set to permit the indoor temperature to drop or rise — a variety defined by the shopper — when demand on the grid is peaking. And data centers themselves is usually a source of demand flexibility: chosen AI calculations might be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune each supply and demand as needed.
As well as, AI makes possible “predictive maintenance.” Any downtime is dear for the corporate and threatens shortages for the shoppers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that standard, the system can alert operators that something may be going flawed, giving them a likelihood to intervene. That capability prevents equipment failures, reduces the necessity for routine inspections, increases employee productivity, and extends the lifetime of key equipment.
Annaswamy stresses that “determining how one can architect this recent power grid with these AI components would require many various experts to return together.” She notes that electrical engineers, computer scientists, and energy economists “may have to rub shoulders with enlightened regulators and policymakers to ensure that this is just not just a tutorial exercise, but will actually get implemented. All the various stakeholders need to learn from one another. And you wish guarantees that nothing goes to fail. You possibly can’t have blackouts.”
Using AI to assist plan investments in infrastructure for the longer term
Grid corporations continuously have to plan for expanding generation, transmission, storage, and more, and getting all of the needed infrastructure built and operating may take a few years, in some cases greater than a decade. So, they should predict what infrastructure they’ll need to make sure reliability in the longer term. “It’s complicated because you might have to forecast over a decade ahead of time what to construct and where to construct it,” says Deepjyoti Deka, a research scientist in MITEI.
One challenge with anticipating what can be needed is predicting how the longer term system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. Up to now, operators could depend on “spinning reserves,” that’s, generating capability that’s not currently in use but could come online in a matter of minutes to fulfill any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators might be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of every bit of apparatus at sufficiently effective time scales. “So, you almost certainly don’t know exactly the way it’s going to run,” says Deka.
After which there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against when it comes to weather. The long run grid needs to be reliable not only in on a regular basis weather, but in addition during low-probability but high-risk events akin to hurricanes, floods, and wildfires, all of which have gotten increasingly frequent, notes Deka. AI will help by predicting such events and even tracking changes in weather patterns attributable to climate change.
Deka points out one other, less-obvious advantage of the speed of AI evaluation. Any infrastructure development plan should be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to at least one set of reviewers. After making any requested changes and repeating the evaluation, the applicant would resubmit a revised version to the reviewers to see if the new edition was acceptable. AI tools can speed up the required evaluation so the method moves along more quickly. Planners may even reduce the variety of times a proposal is rejected by utilizing large language models to look regulatory publications and summarize what’s essential for a proposed infrastructure installation.
Harnessing AI to find and exploit advanced materials needed for the energy transition
“Use of AI for materials development is booming right away,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two important directions.
First, AI makes possible faster physics-based simulations on the atomic scale. The result’s a greater atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to assist guide the event and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.
And second, AI will help guide experiments in real time as they happen within the lab. Li explains: “AI assists us in selecting the perfect experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests recent experiments.”
He describes what happens in his own lab. Human scientists interact with a big language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by organising and performing the following step within the experimental sequence, synthesizing the fabric, testing the performance, and taking images of samples when appropriate. Based on a combination of literature knowledge, human intuition, and former experimental results, AI thus coordinates lively learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”
The consequence, says Li, is each higher design of experiments and speeding up the “work flow.” Traditionally, the technique of developing recent materials has required synthesizing the precursors, making the fabric, testing its performance and characterizing the structure, making adjustments, and repeating the identical series of steps. AI guidance accelerates that process, “helping us to design critical, low-cost experiments that may give us the utmost amount of knowledge feedback,” says Li.
“Having this capability actually will speed up material discovery, and this stands out as the thing that may really help us within the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from many years, as prior to now, to simply a couple of years.”
MITEI’s contributions
At MIT, researchers are working on various features of the opportunities described above. In projects supported by MITEI, teams are using AI to raised model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps to be able to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.
Other MITEI researchers are developing robots that may learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to scale back costs, improve safety, and speed up the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to scale back the energy demand of information centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for instance, to extend airflow in order to scale back the necessity for air con.
Along with providing leadership and funding for a lot of research projects, MITEI acts as a convenor, bringing together interested parties to contemplate common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as each an issue and a possible solution for the clean energy transition. On the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor within the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential advantages of AI to the energy transition is now a research priority for MITEI.”
