There is 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 service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.

For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.

On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.

Researchers at MIT and elsewhere are actively investigating aspects 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 companies interested in addressing the challenges of data center power demand.

Controlling real-time operations

Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.

But with more installation of solar and wind farms — both of which provide 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 picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”

The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.

Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few 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.

Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.

In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.

Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”

Using AI to help plan investments in infrastructure for the future

Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.

One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet 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 can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.

And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.

Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must 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 one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.

Harnessing AI to discover and exploit advanced materials needed for the energy transition

“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.

First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.

And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”

He describes what happens in his own lab. Human scientists interact with a large 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 setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active 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 outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.

“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” 

MITEI’s contributions

At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better 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 in order 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 can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.

In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider 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 both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”

The U.S. Department of Energy (DOE) warns blackouts could increase by 100 times in 2030 if the nation “continues to shutter reliable power sources and fails to add additional firm capacity.” The forecast is a driving factor for school transportation departments seeking to incorporate cleaner alternatives for fueling buses.

The DOE report “Evaluating U.S. Grid Reliability and Security” released July 7, fulfills Section 3(b) of President Donald Trump’s Executive Order “Strengthening The Reliability and Security of the United States Electric Grid,” designed to deliver a uniform methodology to identify at-risk regions and guide federal reliability interventions.

• • The report finds the current path—retiring more generations without dependable replacements—threatens both grid reliability and the ability to meet growing AI-driven energy demand. Without intervention, the bulk power system cannot support AI growth, maintain reliability, or keep energy affordable.
  • Projected load growth is too large and fast for existing grid management and capacity planning methods to handle. A transformative shift is urgently needed.
  • The retirement of 104 giga-watts (GW) of firm capacity by 2030, without one-to-one replacement, worsens the resource adequacy challenge. Loss of this generation could cause major outages during unfavorable weather for wind and solar.
  • While 209 GW of new generation is projected by 2030, only 22 GW would be firm baseload power. Even without retirements, the model found increased risk of outages in 2030 by a factor of 34.
  • Current methods for assessing resource adequacy are outdated. Modern evaluations must consider not just peak demand, but also the frequency, magnitude and duration of outages, and model increasing interdependence with neighboring grids.

“Though demands on the electric grid are increasing, we do not foresee a meaningful logistics problem for school transportation directors,” noted Michelle Levinson, the World Resources Institute’s senior manager of eMobility Finance and Policy. “The report headline averages numbers across the whole of the U.S. The risk of additional outages is low and is brought up by high assumed data center demand in Electric Reliability Council of Texas and in PJM South (Virginia and Maryland).”

Levinson commented that the most recent data from the U.S. Energy Information Administration indicates electricity customers on average experienced approximately 5.5 hours of electricity interruptions in 2022.

“Even if all these outages occur on school days, which is unlikely, outages would account for only 0.19 percent of the hours when a bus is in the yard and potentially charging,” she added. “Luckily, transportation directors are already accustomed to navigating the impacts of electric outages on their fueling capabilities through their experience with liquid fossil fuel pumps, which also needs electricity to function.”

Levinson acknowledged change can be “scary” and the transition to electric school buses requires a shift in logistics but should not be a problem in and of itself and as with all logistics comes down to planning.

Overnight and midday down times of most school buses offer substantial opportunities for directors to charge batteries in advance of any conditions that might indicate higher grid risks, such as extreme weather events, she added.

However, others warn that even a short outage will greatly disrupt transportation operations. The DOE’s predicted blackout rate “introduces serious questions about how to keep buses moving in the face of growing grid instability,” noted Joel Stutheit, senior manager of autogas business development at the Propane Education & Research Council (PERC).

“The school day is built around a routine,” he continued. “Imagine what happens to that routine if the grid goes down as often as this DOE report suggests. If a transportation director is relying on an electric school bus fleet, blackouts could leave them unable to charge buses and reliably transport students. Even a short-term outage could introduce last-minute scheduling changes, rerouting [of] buses, and adding extra pressure on drivers and operations teams.”

Transportation directors need to shift from thinking about the electric grid as a guarantee to thinking about it as a variable for which they must plan, Stutheit said.

Ewan Pritchard, the chief subject matter expert on school bus electrification for consultant Energetics, said he believes the intent of the report was to make electric vehicles look bad.

“The DOE’s report is politically charged,” he shared. “My company is the evaluator for the electric vehicle infrastructure program for the state of California. My team is collecting data from all the vehicle charging stations across the state of California that are put in by the electric utilities. We track the time of usage of all of those stations, and we issue a report annually on the progress.”

Related: EPA Proposal Seeks to Eliminate GHG Regulations for Vehicles, Engines
Related: EPA Provides Update on Clean School Bus Program
Related: Previous Lion Electric School Bus Warranties Voided by Company Sale
Related: Propane School Buses Save Districts 50% on Total Cost of Ownership
Related: Roundup: Informative Green Bus Summit Held at STN EXPO West

The team’s work, he said, demonstrates electric school buses can benefit the utility grid — a shoring-up effect in the sense that it depends on when a school bus is plugged in.

For example, it can be a problem if school districts charge electric vehicles between 4 p.m. to 9 p.m., actively drawing power from the utility grid during peak demand times when usage and prices are highest, he noted.

Instead, Pritchard recommended school transportation departments would do well to use charge management systems, which essentially keep track of the strain on the utility grid, the cost of electricity and carbon production.

Doing so saves districts money, he added.

“We’re seeing tremendous change in the way people are charging vehicles, especially when it comes to school buses, because school buses have a very predictable schedule,” Pritchard said. “There’s plenty of time between 9 p.m. and 6 a.m. to recharge their vehicles.”

A Back Up Plan?

The challenge of student safety is “likely not as extreme as the report makes it seem,” Levinson agreed.

“If operators have not charged their vehicles ahead of a significant outage event, battery capacities may be low or zero, meaning this particular type of transport would not be able to run its typical route,” she pointed out. “School may not be in session in the event of such a significant outage.”

Alternatively, schools districts may find that electric buses can provide an additional level of safety and resiliency for students and communities during extreme events when the larger grid is out, Levinson said.

“Localized microgrid capabilities that connect bi-directional buses and essential school or community facilities are especially relevant in situations where extreme weather conditions isolate people and businesses,” she added.

PERC’s Stutheit, who previously was the director of transportation for Bethel School District in Washington, noted students are immediately impacted if buses can’t operate due to a power outage as “many students rely on transportation to and from school not only for their education, but to access meals and other essential services.”

If the grid goes down due to severe weather, the stakes are even higher for transportation directors to provide evacuations or emergency transportation, Stutheit said, adding student transporters need reliably-powered school buses that can respond quickly to keep students safe.

“Propane autogas buses provide that layer of resiliency,” he argued. “These buses can operate and refuel even when the grid is down. In the event of an emergency evacuation or shelter-in-place situation, propane autogas buses allow districts to respond without waiting on fuel deliveries or power restoration. That kind of reliability supports student safety.”

Pritchard noted most schools have backup generators if power goes out. He said the real student safety issue is when the tailpipe of a combustion vehicle is putting out emissions at that student’s height, adding studies show the concentration of pollutants inside of a vehicle are worse than the concentration outside of a vehicle when it comes to school buses.

“I think it’s more of a student safety issue to not electrify your fleet,” he added.

And then there is the possibility of using electric school buses to power microgrids available to provide surplus power to school buildings.

Getting Smart

To mitigate challenges, school districts should implement smart charging strategies and familiarize themselves with charge management tools and capabilities, Levinson said, adding it is best to charge when the grid is least constrained, such as overnight or midday when there is the most solar production.

“School districts can also create standard operating procedures and emergency management procedures. They can also conduct emergency preparedness drills to practice for such scenarios and identify places for procedural improvements,” she added.

Other steps include identifying additional charging locations beyond the primary charging yard and installing site-level resilience via batteries, solar and/or generators.

Stutheit shared that propane also complements EVs as part of a multi-fuel strategy, as it can be go-to energy in emergency situations when the grid is down. It can also provide transportation directors with an affordable option that won’t need infrastructure updates to keep up with grid instability.

There are ways to lessen the risk from outages that apply to both diesel and electric school buses, involving alternative power from outside the grid, Levinson said, adding grid outages affect all functions, not just charging buses.

“In cases in which electric school buses are vehicle-to-load or vehicle-to-building capable, they can be a potential asset to provide site power to run phones, computers, and HVAC systems during an outage. Increasingly electric vehicles, such as electric school buses, can be part of the grid support solution.”

The post Safety Concerns of the Electric Grid? appeared first on School Transportation News.