In an unprecedented use of federal authority, President Donald Trump’s administration has invoked emergency powers to force a series of retiring coal plants to stay open.

Utilities, states and grid operators have said the aging plants are expensive, in bad repair and no longer needed to meet regional energy needs. But Trump’s efforts to save the dwindling coal industry have forced plant operators to continue investing in the facilities — a move that some consumer advocates fear could mean billions of dollars in added costs for customers in dozens of states.

Trump has long positioned himself as a champion of coal, making it a centerpiece of his “energy dominance” agenda. The emergency orders issued by his administration claim that the grid is at risk of energy shortfalls, and the coal plants are needed to ensure a reliable power supply.

But state officials in many places affected by the orders say that’s not true.

“Rather than allowing the realities on the ground, the regulators and the utilities to make rational decisions about how to meet energy needs, we have the Trump administration trying to do Soviet-style central planning to push an ideological agenda that will drive costs to customers,” said Will Toor, executive director of the Colorado Energy Office.

Under Trump, the U.S. Department of Energy has issued emergency orders to block the retirements of coal plants in Colorado, Indiana, Michigan and Washington state. Secretary of Energy Chris Wright has claimed that the power demands in various regions require the plants to stay operational.

Observers expect similar orders to be issued for most, if not all, of the dozens of coal-fired units slated for retirement during the remainder of Trump’s term. Utilities subject to the orders have said they will increase costs for ratepayers, and argue those costs should be borne by the multistate region to which they provide power, rather than just their local customers.

Despite their costs, three of the five plants being blocked from retirement haven’t produced electricity since the emergency orders went into effect, either because they need extensive repairs or because power demands have been met without them.

Section 202(c) of the Federal Power Act gives the secretary broad authority to take temporary control of the U.S. electricity system during emergency situations. Until now, that authority had only been invoked during wartime or natural disasters. All of the Trump administration’s orders were issued before the war with Iran. Consumer advocates say Trump’s use of the act to overturn long-planned facility retirements is unprecedented, and likely illegal.

State officials, utilities and environmental groups have challenged all of the orders.

While such emergency orders can be issued only for 90-day periods, Wright has repeatedly renewed the orders before they expire.

The Department of Energy did not respond to a Stateline interview request.

Keeping coal online

Last May, Wright issued the first emergency order to prevent the shutdown of the J.H. Campbell Generating Plant in Michigan, just days before it was scheduled to retire. The plant has remained open since then, accruing $135 million in net costs through December. Consumers Energy, the utility operating the plant, is seeking to charge ratepayers in 11 states to recoup those costs.

Michigan Democratic Attorney General Dana Nessel has appealed the order, while a coalition of environmental groups has filed a lawsuit seeking to overturn it, arguing that the feds have failed to demonstrate a true emergency. That case is currently in the D.C. Circuit Court of Appeals awaiting oral arguments, which may take place in May.

State leaders in Colorado have appealed an order to keep a plant there open, while Washington state Attorney General Nick Brown, a Democrat, has sued the federal agency. Environmental groups have filed a lawsuit challenging the order in Indiana. Energy analysts say the Michigan case will likely be resolved first, and is expected to have major implications for the emergency orders elsewhere.

Douglas Jester, a former state energy official in Michigan, noted that Consumers Energy has had to pay extra to bring back staff, establish new delivery contracts for coal and catch up on maintenance. Jester now serves as managing partner at 5 Lakes Energy, a clean energy consulting group.

In his emergency order, Wright said the plant was needed to ensure energy reliability and reduce the risk of blackouts. His agency, in a statement issued last month, said the coal plants kept open by the emergency orders helped keep the power system online during Winter Storm Fern.

Coal industry leaders have made a similar argument, saying that growing energy demands require more baseload power, as opposed to intermittent renewables such as wind and solar.

The emergency orders are “very much needed,” said Emily Arthun, CEO of the American Coal Council, an industry trade group, “so that we can continue to have the energy just for our day-to-day lives,” said Emily Arthun, CEO of the American Coal Council, an industry trade group. “Coal plants, baseload plants, are critical to the well-being of our grid. Coal is needed at critical moments for energy.”

Some labor unions have also praised the orders as beneficial to their workforce.

But state leaders and consumer advocates argue that utilities and regulators have already completed detailed plans to replace the power the aging coal plants provided, through a mix of renewables, natural gas plants and battery storage.

“If you were to believe the Department of Energy, you would believe that more than half the country is experiencing an emergency around the clock,” said Michael Lenoff, senior attorney at Earthjustice, an environmental group that is suing the Trump administration to overturn the orders. “It costs a lot of money to make sure that an old, decrepit coal plant is available to operate.”

Lenoff and other environmental advocates have said the coal plants ran during the winter storm because the government forced them to, not because the grid needed them to meet power demands.

Even as his administration has declared an energy shortage emergency, Trump has tried to block new renewable projects from being built, including several offshore wind farms that East Coast states are relying on to meet their power demands.

Meanwhile, the administration has also authorized power generators to export electricity to Mexico and Canada, which may happen only when regulators have determined the U.S. has sufficient energy supply to meet its own needs.

“How can you authorize the export of energy to Canada from a Western market that you just declared is in an emergency status with shortages?” said Tyson Slocum, energy program director at Public Citizen, a consumer advocacy nonprofit. “It’s complete incoherence.”

Aging plants

Three of the five plants being blocked from retirement have yet to even produce electricity since the emergency orders went into effect.

The plant in Colorado suffered a failure in a steam valve that was not repaired because it was on the verge of retiring. The federal order has forced the Tri-State Generation and Transmission Association to invest in repairing the plant, and the costs to keep the plant operational could reach $80 million a year even if it never produces power, said Toor, with the Colorado Energy Office.

“It’s very unlikely to actually operate even with this order,” he said.

Tri-State and the other utilities that own the plant have requested a rehearing of the emergency order, saying that keeping the plant open will be costly for their ratepayers.

In Indiana, one of the two plants targeted by the feds has suffered mechanical failures that would require extensive repairs.

“(The order) doesn’t even make sense because it’s not even really open,” said Ben Inskeep, program director at the Citizens Action Coalition, an Indiana-based consumer advocacy group. “You don’t want to throw good money after a plant you’re about to retire.”

Unlike the Democratic-led states subject to the other orders, Indiana’s leaders have welcomed the federal intervention. Republican Gov. Mike Braun issued his own executive order soon after the Department of Energy announcement directing state officials to evaluate ways to extend the life of the state’s remaining coal plants.

Meanwhile, the TransAlta Centralia coal plant in Washington state, while remaining in operational mode, has not supplied power to the grid since January, as the state’s energy needs have been met by more affordable sources elsewhere.

Democratic state Sen. Marko Liias sponsored a bill, signed into law earlier this month, that rolls back tax and regulatory exemptions that were granted to TransAlta under a 2011 agreement to gradually phase out the plant. The compliance burden will make it economically infeasible for the plant to operate again, he said.

“It’s crystal clear to the market that we’re not going backwards, we’re slamming the door and nailing it shut,” Liias said.

Consumer costs

While some states have pushed to close coal plants due to climate goals and pollution concerns, market forces have largely driven the coal industry’s decline. According to a 2025 analysis by the financial advisory firm Lazard, electricity from coal-fired power plants costs an average of $122 per megawatt-hour. That same amount of power can be produced for $78 from natural gas plants, $61 from onshore wind and $58 from utility-scale solar.

Some energy analysts say Trump’s efforts to keep fossil fuel-powered plants open could become very costly to ratepayers. A report published by Grid Strategies LLC, a consulting firm, found that as many as 90 aging plants could be subject to similar emergency orders during the remainder of Trump’s term. The analysis found that keeping those plants open could cost ratepayers anywhere from $3 billion to $6 billion a year.

“What the Department of Energy is doing is picking losers, the uneconomical plants that the utilities, the regulators, everybody involved agreed need to retire and be replaced with something cheaper and more efficient,” said Michael Goggin, who authored the report, which was commissioned on behalf of Earthjustice and other environmental groups.

Meanwhile, some consumer advocates say the orders have created chaos for utilities and energy planners. The operators of plants scheduled for retirement in the coming years no longer know if it’s safe to cancel their coal contracts, transition their workforce or defer maintenance on their facilities. And financiers may be wary of investing in new, cheaper energy projects that could be sidelined by orders to keep coal online.

“The administration has made clear that they’re not going to allow a coal-fired power plant to retire, regardless of whether or not it’s absurdly expensive to operate, whether it’s contaminating soil, air and water in that community, they literally don’t care,” said Slocum, of Public Citizen.

Stateline reporter Alex Brown can be reached at abrown@stateline.org. 

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.”

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