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    How artificial intelligence can help achieve a clean energy future

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    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 operationsCustomers 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 futureGrid 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 contributionsAt 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.” More

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      MIT Energy Initiative conference spotlights research priorities amidst a changing energy landscape

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      “We’re here to talk about really substantive changes, and we want you to be a participant in that,” said Desirée Plata, the School of Engineering Distinguished Professor of Climate and Energy in MIT’s Department of Civil and Environmental Engineering, at Energizing@MIT: the MIT Energy Initiative’s (MITEI) Annual Research Conference that was held on Sept. 9-10.Plata’s words resonated with the 150-plus participants from academia, industry, and government meeting in Cambridge for the conference, whose theme was “tackling emerging energy challenges.” Meeting such challenges and ultimately altering the trajectory of global climate outcomes requires partnerships, speakers agreed.“We have to be humble and open,” said Giacomo Silvestri, chair of Eniverse Ventures at Eni, in a shared keynote address. “We cannot develop innovation just focusing on ourselves and our competencies … so we need to partner with startups, venture funds, universities like MIT and other public and private institutions.” Added his Eni colleague, Annalisa Muccioli, head of research and technology, “The energy transition is a race we can win only by combining mature solutions ready to deploy, together with emerging technologies that still require acceleration and risk management.”Research targetsIn a conference that showcased a suite of research priorities MITEI has identified as central to ensuring a low-carbon energy future, participants shared both promising discoveries and strategies for advancing proven technologies in the face of shifting political winds and policy uncertainties.One panel focused on grid resiliency — a topic that has moved from the periphery to the center of energy discourse as climate-driven disruptions, cyber threats, and the integration of renewables challenge legacy systems. A dramatic case in point: the April 2025 outage in Spain and Portugal that left millions without power for eight to 15 hours. “I want to emphasize that this failure was about more than the power system,” said MITEI research scientist Pablo Duenas-Martinez. While he pinpointed technical problems with reactive power and voltage control behind the system collapse, Duenas-Martinez also called out a lack of transmission capacity with Central Europe and out-of-date operating procedures, and recommended better preparation and communication among transmission systems and utility operators.“You can’t plan for every single eventuality, which means we need to broaden the portfolio of extreme events we prepare for,” noted Jennifer Pearce, vice president at energy company Avangrid. “We are making the system smarter, stronger, and more resilient to better protect from a wide range of threats such as storms, flooding, and extreme heat events.” Pearce noted that Avangrid’s commitment to deliver safe, reliable power to its customers necessitates “meticulous emergency planning procedures.”The resiliency of the electric grid under greatly increased demand is an important motivation behind MITEI’s September 2025 launch of the Data Center Power Forum, which was also announced during the annual research conference. The forum will include research projects, webinars, and other content focused on energy supply and storage, grid design and management, infrastructure, and public and economic policy related to data centers. The forum’s members include MITEI companies that also participate in MIT’s Center for Environmental and Energy Policy Research (CEEPR).Storage and transportation: Staggering challengesMeeting climate goals to decarbonize the world by 2050 requires building around 300 terawatt-hours of storage, according to Asegun Henry, a professor in the MIT Department of Mechanical Engineering. “It’s an unbelievably enormous problem people have to wrap their minds around,” he said. Henry has been developing a high-temperature thermal energy storage system he has nicknamed “sun in a box.” His system uses liquid metal and graphite to hold electricity as heat and then convert it back to electricity, enabling storage anywhere from five to 500 hours.“At the end of the day, storage provides a service, and the type of technology that you need is a function of the service that you value the most,” said Nestor Sepulveda, commercial lead for advanced energy investments and partnerships at Google. “I don’t think there is one winner-takes-all type of market here.”Another panel explored sustainable fuels that could help decarbonize hard-to-electrify sectors like aviation, shipping, and long-haul trucking. Randall Field, MITEI’s director of research, noted that sustainably produced drop-in fuels — fuels that are largely compatible with existing engines — “could eliminate potentially trillions of dollars of cost for fleet replacement and for infrastructure build-out, while also helping us to accelerate the rate of decarbonization of the transportation sectors.”Erik G. Birkerts is the chief growth officer of LanzaJet, which produces a drop-in, high-energy-density aviation fuel derived from agricultural residue and other waste carbon sources. “The key to driving broad sustainable aviation fuel adoption is solving both the supply-side challenge through more production and the demand-side hurdle by reducing costs,” he said.“We think a good policy framework [for sustainable fuels] would be something that is technology-neutral, does not exclude any pathways to produce, is based on life cycle accounting practices, and on market mechanisms,” said Veronica L. Robertson, energy products technology portfolio manager at ExxonMobil.MITEI plans a major expansion of its research on sustainable fuels, announcing a two-year study, “The future of fuels: Pathways to sustainable transportation,” starting in early 2026. According to Field, the study will analyze and assess biofuels and e-fuels.Solutions from labs big and smallGlobal energy leaders offered glimpses of their research projects. A panel on carbon capture in power generation featured three takes on the topic: Devin Shaw, commercial director of decarbonization technologies at Shell, described post-combustion carbon capture in power plants using steam for heat recovery; Jan Marsh, a global program lead at Siemens Energy, discussed deploying novel materials to capture carbon dioxide directly from the air; and Jeffrey Goldmeer, senior director of technology strategy at GE Vernova, explained integrating carbon capture into gas-powered turbine systems.During a panel on vehicle electrification, Brian Storey, vice president of energy and materials at the Toyota Research Institute, provided an overview of Toyota’s portfolio of projects for decarbonization, including solid-state batteries, flexible manufacturing lines, and grid-forming inverters to support EV charging infrastructure.A session on MITEI seed fund projects revealed promising early-stage research inside MIT’s own labs. A new process for decarbonizing the production of ethylene was presented by Yogesh Surendranath, Donner Professor of Science in the MIT Department of Chemistry. Materials Science and Engineering assistant professor Aristide Gumyusenge also discussed the development of polymers essential for a new kind of sodium-ion battery.Shepherding bold, new technologies like these from academic labs into the real world cannot succeed without ample support and deft management. A panel on paths to commercialization featured the work of Iwnetim Abate, Chipman Career Development Professor and assistant professor in the MIT Department of Materials Science and Engineering, who has spun out a company, Addis Energy, based on a novel geothermal process for harvesting clean hydrogen and ammonia from subsurface, iron-rich rocks. Among his funders: ARPA-E and MIT’s own The Engine Ventures.The panel also highlighted the MIT Proto Ventures Program, an initiative to seize early-stage MIT ideas and unleash them as world-changing startups. “A mere 4.2 percent of all the patents that are actually prosecuted in the world are ever commercialized, which seems like a shocking number,” said Andrew Inglis, an entrepreneur working with Proto Ventures to translate geothermal discoveries into businesses. “Can’t we do this better? Let’s do this better!”Geopolitical hazardsThroughout the conference, participants often voiced concern about the impacts of competition between the United States and China. Kelly Sims Gallagher, dean of the Fletcher School at Tufts University and an expert on China’s energy landscape, delivered the sobering news in her keynote address: “U.S. competitiveness in low-carbon technologies has eroded in nearly every category,” she said. “The Chinese are winning the clean tech race.”China enjoys a 51 percent share in global wind turbine manufacture and 75 percent in solar modules. It also controls low-carbon supply chains that much of the world depends on. “China is getting so dominant that nobody can carve out a comparative advantage in anything,” said Gallagher. “China is just so big, and the scale is so huge that the Chinese can truly conquer markets and make it very hard for potential competitors to find a way in.”And for the United States, the problem is “the seesaw of energy policy,” she says. “It’s incredibly difficult for the private sector to plan and to operate, given the lack of predictability and policy here.”Nevertheless, Gallagher believes the United States still has a chance of at least regaining competitiveness, by setting up a stable, bipartisan energy policy, rebuilding domestic manufacturing and supply chains; providing consistent fiscal incentives; attracting and retaining global talent; and fostering international collaboration.The conference shone a light on one such collaboration: a China-U.S. joint venture to manufacture lithium iron phosphate batteries for commercial vehicles in the United States. The venture brings together Eve Energy, a Chinese battery technology and manufacturing company; Daimler, a global commercial vehicle manufacturer; PACCAR Inc., a U.S.-based truck manufacturer; and Accelera, the zero-emissions business of Cummins Inc. “Manufacturing batteries in the U.S. makes the supply chain more robust and reduces geopolitical risks,” said Mike Gerty, of PACCAR.While she acknowledged the obstacles confronting her colleagues in the room, Plata nevertheless concluded her remarks as a panel moderator with some optimism: “I hope you all leave this conference and look back on it in the future, saying I was in the room when they actually solved some of the challenges standing between now and the future that we all wish to manifest.” More

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      MIT Energy Initiative launches Data Center Power Forum

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      With global power demand from data centers expected to more than double by 2030, the MIT Energy Initiative (MITEI) in September launched an effort that brings together MIT researchers and industry experts to explore innovative solutions for powering the data-driven future. At its annual 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. The Data Center Power Forum builds on lessons from MITEI’s May 2025 symposium on the energy to power the expansion of artificial intelligence (AI) and focus panels related to data centers at the fall 2024 research conference.In the United States, data centers consumed 4 percent of the country’s electricity in 2023, with demand expected to increase to 9 percent by 2030, according to the Electric Power Research Institute. Much of the growth in demand is from the increasing use of AI, which is placing an unprecedented strain on the electric grid. This surge in demand presents a serious challenge for the technology and energy sectors, government policymakers, and everyday consumers, who may see their electric bills skyrocket as a result.“MITEI has long supported research on ways to produce more efficient and cleaner energy and to manage the electric grid. In recent years, MITEI has also funded dozens of research projects relevant to data center energy issues. Building on this history and knowledge base, MITEI’s Data Center Power Forum is convening a specialized community of industry members who have a vital stake in the sustainable growth of AI and the acceleration of solutions for powering data centers and expanding the grid,” says William H. Green, the director of MITEI and the Hoyt C. Hottel Professor of Chemical Engineering.MITEI’s mission is to advance zero- and low-carbon solutions to expand energy access and mitigate climate change. MITEI works with companies from across the energy innovation chain, including in the infrastructure, automotive, electric power, energy, natural resources, and insurance sectors. MITEI member companies have expressed strong interest in the Data Center Power Forum and are committing to support focused research on a wide range of energy issues associated with data center expansion, Green says.MITEI’s Data Center Power Forum will provide its member companies with reliable insights into energy supply, grid load operations and management, the built environment, and electricity market design and regulatory policy for data centers. The forum complements MIT’s deep expertise in adjacent topics such as low-power processors, efficient algorithms, task-specific AI, photonic devices, quantum computing, and the societal consequences of data center expansion. As part of the forum, MITEI’s Future Energy Systems Center is funding projects relevant to data center energy in its upcoming proposal cycles. MITEI Research Scientist Deep Deka has been named the program manager for the forum.“Figuring out how to meet the power demands of data centers is a complicated challenge. Our research is coming at this from multiple directions, from looking at ways to expand transmission capacity within the electrical grid in order to bring power to where it is needed, to ensuring the quality of electrical service for existing users is not diminished when new data centers come online, and to shifting computing tasks to times and places when and where energy is available on the grid,” said Deka.MITEI currently sponsors substantial research related to data center energy topics across several MIT departments. The existing research portfolio includes more than a dozen projects related to data centers, including low- or zero-carbon solutions for energy supply and infrastructure, electrical grid management, and electricity market policy. MIT researchers funded through MITEI’s industry consortium are also designing more energy-efficient power electronics and processors and investigating behind-the-meter low-/no-carbon power plants and energy storage. MITEI-supported experts are studying how to use AI to optimize electrical distribution and the siting of data centers and conducting techno-economic analyses of data center power schemes. MITEI’s consortium projects are also bringing fresh perspectives to data center cooling challenges and considering policy approaches to balance the interests of shareholders. By drawing together industry stakeholders from across the AI and grid value chain, the Data Center Power Forum enables a richer dialog about solutions to power, grid, and carbon management problems in a noncommercial and collaborative setting.“The opportunity to meet and to hold discussions on key data center challenges with other forum members from different sectors, as well as with MIT faculty members and research scientists, is a unique benefit of this MITEI-led effort,” Green says.MITEI addressed the issue of data center power needs with its company members during its fall 2024 Annual Research Conference with a panel session titled, “The extreme challenge of powering data centers in a decarbonized way.” MITEI Director of Research Randall Field led a discussion with representatives from large technology companies Google and Microsoft, known as “hyperscalers,” as well as Madrid-based infrastructure developer Ferrovial S.E. and utility company Exelon Corp. Another conference session addressed the related topic, “Energy storage and grid expansion.” This past spring, MITEI focused its annual Spring Symposium on data centers, hosting faculty members and researchers from MIT and other universities, business leaders, and a representative of the Federal Energy Regulatory Commission for a full day of sessions on the topic, “AI and energy: Peril and promise.”  More

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      Fighting for the health of the planet with AI

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      For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the Department of Electrical Engineering and Computer Science (EECS), a shared position between the MIT Schwarzman College of Computing and EECS, and a principal investigator at the MIT Laboratory for Information and Decision Systems (LIDS) — was struck by the disparities in how people live.“It was very clear to me the extent to which inequity is a rampant issue around the world,” Donti says. “From a young age, I knew that I definitely wanted to address that issue.”That motivation was further stoked by a high school biology teacher, who focused his class on climate and sustainability.“We learned that climate change, this huge, important issue, would exacerbate inequity,” Donti says. “That really stuck with me and put a fire in my belly.”So, when Donti enrolled at Harvey Mudd College, she thought she would direct her energy toward the study of chemistry or materials science to create next-generation solar panels.Those plans, however, were jilted. Donti “fell in love” with computer science, and then discovered work by researchers in the United Kingdom who were arguing that artificial intelligence and machine learning would be essential to help integrate renewables into power grids.“It was the first time I’d seen those two interests brought together,” she says. “I got hooked and have been working on that topic ever since.”Pursuing a PhD at Carnegie Mellon University, Donti was able to design her degree to include computer science and public policy. In her research, she explored the need for fundamental algorithms and tools that could manage, at scale, power grids relying heavily on renewables.“I wanted to have a hand in developing those algorithms and tool kits by creating new machine learning techniques grounded in computer science,” she says. “But I wanted to make sure that the way I was doing the work was grounded both in the actual energy systems domain and working with people in that domain” to provide what was actually needed.While Donti was working on her PhD, she co-founded a nonprofit called Climate Change AI. Her objective, she says, was to help the community of people involved in climate and sustainability — “be they computer scientists, academics, practitioners, or policymakers” — to come together and access resources, connection, and education “to help them along that journey.”“In the climate space,” she says, “you need experts in particular climate change-related sectors, experts in different technical and social science tool kits, problem owners, affected users, policymakers who know the regulations — all of those — to have on-the-ground scalable impact.”When Donti came to MIT in September 2023, it was not surprising that she was drawn by its initiatives directing the application of computer science toward society’s biggest problems, especially the current threat to the health of the planet.“We’re really thinking about where technology has a much longer-horizon impact and how technology, society, and policy all have to work together,” Donti says. “Technology is not just one-and-done and monetizable in the context of a year.”Her work uses deep learning models to incorporate the physics and hard constraints of electric power systems that employ renewables for better forecasting, optimization, and control.“Machine learning is already really widely used for things like solar power forecasting, which is a prerequisite to managing and balancing power grids,” she says. “My focus is, how do you improve the algorithms for actually balancing power grids in the face of a range of time-varying renewables?”Among Donti’s breakthroughs is a promising solution for power grid operators to be able to optimize for cost, taking into account the actual physical realities of the grid, rather than relying on approximations. While the solution is not yet deployed, it appears to work 10 times faster, and far more cheaply, than previous technologies, and has attracted the attention of grid operators.Another technology she is developing works to provide data that can be used in training machine learning systems for power system optimization. In general, much data related to the systems is private, either because it is proprietary or because of security concerns. Donti and her research group are working to create synthetic data and benchmarks that, Donti says, “can help to expose some of the underlying problems” in making power systems more efficient.“The question is,” Donti says, “can we bring our datasets to a point such that they are just hard enough to drive progress?”For her efforts, Donti has been awarded the U.S. Department of Energy Computational Science Graduate Fellowship and the NSF Graduate Research Fellowship. She was recognized as part of MIT Technology Review’s 2021 list of “35 Innovators Under 35” and Vox’s 2023 “Future Perfect 50.”Next spring, Donti will co-teach a class called AI for Climate Action with Sara Beery, EECS assistant professor, whose focus is AI for biodiversity and ecosystems, and Abigail Bodner, assistant professor in the departments of EECS and Earth, Atmospheric and Planetary Sciences, whose focus is AI for climate and Earth science.“We’re all super-excited about it,” Donti says.Coming to MIT, Donti says, “I knew that there would be an ecosystem of people who really cared, not just about success metrics like publications and citation counts, but about the impact of our work on society.” More

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      Using liquid air for grid-scale energy storage

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      As the world moves to reduce carbon emissions, solar and wind power will play an increasing role on electricity grids. But those renewable sources only generate electricity when it’s sunny or windy. So to ensure a reliable power grid — one that can deliver electricity 24/7 — it’s crucial to have a means of storing electricity when supplies are abundant and delivering it later, when they’re not. And sometimes large amounts of electricity will need to be stored not just for hours, but for days, or even longer.Some methods of achieving “long-duration energy storage” are promising. For example, with pumped hydro energy storage, water is pumped from a lake to another, higher lake when there’s extra electricity and released back down through power-generating turbines when more electricity is needed. But that approach is limited by geography, and most potential sites in the United States have already been used. Lithium-ion batteries could provide grid-scale storage, but only for about four hours. Longer than that and battery systems get prohibitively expensive.A team of researchers from MIT and the Norwegian University of Science and Technology (NTNU) has been investigating a less-familiar option based on an unlikely-sounding concept: liquid air, or air that is drawn in from the surroundings, cleaned and dried, and then cooled to the point that it liquefies. “Liquid air energy storage” (LAES) systems have been built, so the technology is technically feasible. Moreover, LAES systems are totally clean and can be sited nearly anywhere, storing vast amounts of electricity for days or longer and delivering it when it’s needed. But there haven’t been conclusive studies of its economic viability. Would the income over time warrant the initial investment and ongoing costs? With funding from the MIT Energy Initiative’s Future Energy Systems Center, the researchers developed a model that takes detailed information on LAES systems and calculates when and where those systems would be economically viable, assuming future scenarios in line with selected decarbonization targets as well as other conditions that may prevail on future energy grids.They found that under some of the scenarios they modeled, LAES could be economically viable in certain locations. Sensitivity analyses showed that policies providing a subsidy on capital expenses could make LAES systems economically viable in many locations. Further calculations showed that the cost of storing a given amount of electricity with LAES would be lower than with more familiar systems such as pumped hydro and lithium-ion batteries. They conclude that LAES holds promise as a means of providing critically needed long-duration storage when future power grids are decarbonized and dominated by intermittent renewable sources of electricity.The researchers — Shaylin A. Cetegen, a PhD candidate in the MIT Department of Chemical Engineering (ChemE); Professor Emeritus Truls Gundersen of the NTNU Department of Energy and Process Engineering; and MIT Professor Emeritus Paul I. Barton of ChemE — describe their model and their findings in a new paper published in the journal Energy.The LAES technology and its benefitsLAES systems consists of three steps: charging, storing, and discharging. When supply on the grid exceeds demand and prices are low, the LAES system is charged. Air is then drawn in and liquefied. A large amount of electricity is consumed to cool and liquefy the air in the LAES process. The liquid air is then sent to highly insulated storage tanks, where it’s held at a very low temperature and atmospheric pressure. When the power grid needs added electricity to meet demand, the liquid air is first pumped to a higher pressure and then heated, and it turns back into a gas. This high-pressure, high-temperature, vapor-phase air expands in a turbine that generates electricity to be sent back to the grid.According to Cetegen, a primary advantage of LAES is that it’s clean. “There are no contaminants involved,” she says. “It takes in and releases only ambient air and electricity, so it’s as clean as the electricity that’s used to run it.” In addition, a LAES system can be built largely from commercially available components and does not rely on expensive or rare materials. And the system can be sited almost anywhere, including near other industrial processes that produce waste heat or cold that can be used by the LAES system to increase its energy efficiency.Economic viabilityIn considering the potential role of LAES on future power grids, the first question is: Will LAES systems be attractive to investors? Answering that question requires calculating the technology’s net present value (NPV), which represents the sum of all discounted cash flows — including revenues, capital expenditures, operating costs, and other financial factors — over the project’s lifetime. (The study assumed a cash flow discount rate of 7 percent.)To calculate the NPV, the researchers needed to determine how LAES systems will perform in future energy markets. In those markets, various sources of electricity are brought online to meet the current demand, typically following a process called “economic dispatch:” The lowest-cost source that’s available is always deployed next. Determining the NPV of liquid air storage therefore requires predicting how that technology will fare in future markets competing with other sources of electricity when demand exceeds supply — and also accounting for prices when supply exceeds demand, so excess electricity is available to recharge the LAES systems.For their study, the MIT and NTNU researchers designed a model that starts with a description of an LAES system, including details such as the sizes of the units where the air is liquefied and the power is recovered, and also capital expenses based on estimates reported in the literature. The model then draws on state-of-the-art pricing data that’s released every year by the National Renewable Energy Laboratory (NREL) and is widely used by energy modelers worldwide. The NREL dataset forecasts prices, construction and retirement of specific types of electricity generation and storage facilities, and more, assuming eight decarbonization scenarios for 18 regions of the United States out to 2050.The new model then tracks buying and selling in energy markets for every hour of every day in a year, repeating the same schedule for five-year intervals. Based on the NREL dataset and details of the LAES system — plus constraints such as the system’s physical storage capacity and how often it can switch between charging and discharging — the model calculates how much money LAES operators would make selling power to the grid when it’s needed and how much they would spend buying electricity when it’s available to recharge their LAES system. In line with the NREL dataset, the model generates results for 18 U.S. regions and eight decarbonization scenarios, including 100 percent decarbonization by 2035 and 95 percent decarbonization by 2050, and other assumptions about future energy grids, including high-demand growth plus high and low costs for renewable energy and for natural gas.Cetegen describes some of their results: “Assuming a 100-megawatt (MW) system — a standard sort of size — we saw economic viability pop up under the decarbonization scenario calling for 100 percent decarbonization by 2035.” So, positive NPVs (indicating economic viability) occurred only under the most aggressive — therefore the least realistic — scenario, and they occurred in only a few southern states, including Texas and Florida, likely because of how those energy markets are structured and operate.The researchers also tested the sensitivity of NPVs to different storage capacities, that is, how long the system could continuously deliver power to the grid. They calculated the NPVs of a 100 MW system that could provide electricity supply for one day, one week, and one month. “That analysis showed that under aggressive decarbonization, weekly storage is more economically viable than monthly storage, because [in the latter case] we’re paying for more storage capacity than we need,” explains Cetegen.Improving the NPV of the LAES systemThe researchers next analyzed two possible ways to improve the NPV of liquid air storage: by increasing the system’s energy efficiency and by providing financial incentives. Their analyses showed that increasing the energy efficiency, even up to the theoretical limit of the process, would not change the economic viability of LAES under the most realistic decarbonization scenarios. On the other hand, a major improvement resulted when they assumed policies providing subsidies on capital expenditures on new installations. Indeed, assuming subsidies of between 40 percent and 60 percent made the NPVs for a 100 MW system become positive under all the realistic scenarios.Thus, their analysis showed that financial incentives could be far more effective than technical improvements in making LAES economically viable. While engineers may find that outcome disappointing, Cetegen notes that from a broader perspective, it’s good news. “You could spend your whole life trying to optimize the efficiency of this process, and it wouldn’t translate to securing the investment needed to scale the technology,” she says. “Policies can take a long time to implement as well. But theoretically you could do it overnight. So if storage is needed [on a future decarbonized grid], then this is one way to encourage adoption of LAES right away.”Cost comparison with other energy storage technologiesCalculating the economic viability of a storage technology is highly dependent on the assumptions used. As a result, a different measure — the “levelized cost of storage” (LCOS) — is typically used to compare the costs of different storage technologies. In simple terms, the LCOS is the cost of storing each unit of energy over the lifetime of a project, not accounting for any income that results.On that measure, the LAES technology excels. The researchers’ model yielded an LCOS for liquid air storage of about $60 per megawatt-hour, regardless of the decarbonization scenario. That LCOS is about a third that of lithium-ion battery storage and half that of pumped hydro. Cetegen cites another interesting finding: the LCOS of their assumed LAES system varied depending on where it’s being used. The standard practice of reporting a single LCOS for a given energy storage technology may not provide the full picture.Cetegen has adapted the model and is now calculating the NPV and LCOS for energy storage using lithium-ion batteries. But she’s already encouraged by the LCOS of liquid air storage. “While LAES systems may not be economically viable from an investment perspective today, that doesn’t mean they won’t be implemented in the future,” she concludes. “With limited options for grid-scale storage expansion and the growing need for storage technologies to ensure energy security, if we can’t find economically viable alternatives, we’ll likely have to turn to least-cost solutions to meet storage needs. This is why the story of liquid air storage is far from over. We believe our findings justify the continued exploration of LAES as a key energy storage solution for the future.” More

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      in Energy

      Taking the “training wheels” off clean energy

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      Renewable power sources have seen unprecedented levels of investment in recent years. But with political uncertainty clouding the future of subsidies for green energy, these technologies must begin to compete with fossil fuels on equal footing, said participants at the 2025 MIT Energy Conference.“What these technologies need less is training wheels, and more of a level playing field,” said Brian Deese, an MIT Institute Innovation Fellow, during a conference-opening keynote panel.The theme of the two-day conference, which is organized each year by MIT students, was “Breakthrough to deployment: Driving climate innovation to market.” Speakers largely expressed optimism about advancements in green technology, balanced by occasional notes of alarm about a rapidly changing regulatory and political environment.Deese defined what he called “the good, the bad, and the ugly” of the current energy landscape. The good: Clean energy investment in the United States hit an all-time high of $272 billion in 2024. The bad: Announcements of future investments have tailed off. And the ugly: Macro conditions are making it more difficult for utilities and private enterprise to build out the clean energy infrastructure needed to meet growing energy demands.“We need to build massive amounts of energy capacity in the United States,” Deese said. “And the three things that are the most allergic to building are high uncertainty, high interest rates, and high tariff rates. So that’s kind of ugly. But the question … is how, and in what ways, that underlying commercial momentum can drive through this period of uncertainty.”A shifting clean energy landscapeDuring a panel on artificial intelligence and growth in electricity demand, speakers said that the technology may serve as a catalyst for green energy breakthroughs, in addition to putting strain on existing infrastructure. “Google is committed to building digital infrastructure responsibly, and part of that means catalyzing the development of clean energy infrastructure that is not only meeting the AI need, but also benefiting the grid as a whole,” said Lucia Tian, head of clean energy and decarbonization technologies at Google.Across the two days, speakers emphasized that the cost-per-unit and scalability of clean energy technologies will ultimately determine their fate. But they also acknowledged the impact of public policy, as well as the need for government investment to tackle large-scale issues like grid modernization.Vanessa Chan, a former U.S. Department of Energy (DoE) official and current vice dean of innovation and entrepreneurship at the University of Pennsylvania School of Engineering and Applied Sciences, warned of the “knock-on” effects of the move to slash National Institutes of Health (NIH) funding for indirect research costs, for example. “In reality, what you’re doing is undercutting every single academic institution that does research across the nation,” she said.During a panel titled “No clean energy transition without transmission,” Maria Robinson, former director of the DoE’s Grid Deployment Office, said that ratepayers alone will likely not be able to fund the grid upgrades needed to meet growing power demand. “The amount of investment we’re going to need over the next couple of years is going to be significant,” she said. “That’s where the federal government is going to have to play a role.”David Cohen-Tanugi, a clean energy venture builder at MIT, noted that extreme weather events have changed the climate change conversation in recent years. “There was a narrative 10 years ago that said … if we start talking about resilience and adaptation to climate change, we’re kind of throwing in the towel or giving up,” he said. “I’ve noticed a very big shift in the investor narrative, the startup narrative, and more generally, the public consciousness. There’s a realization that the effects of climate change are already upon us.”“Everything on the table”The conference featured panels and keynote addresses on a range of emerging clean energy technologies, including hydrogen power, geothermal energy, and nuclear fusion, as well as a session on carbon capture.Alex Creely, a chief engineer at Commonwealth Fusion Systems, explained that fusion (the combining of small atoms into larger atoms, which is the same process that fuels stars) is safer and potentially more economical than traditional nuclear power. Fusion facilities, he said, can be powered down instantaneously, and companies like his are developing new, less-expensive magnet technology to contain the extreme heat produced by fusion reactors.By the early 2030s, Creely said, his company hopes to be operating 400-megawatt power plants that use only 50 kilograms of fuel per year. “If you can get fusion working, it turns energy into a manufacturing product, not a natural resource,” he said.Quinn Woodard Jr., senior director of power generation and surface facilities at geothermal energy supplier Fervo Energy, said his company is making the geothermal energy more economical through standardization, innovation, and economies of scale. Traditionally, he said, drilling is the largest cost in producing geothermal power. Fervo has “completely flipped the cost structure” with advances in drilling, Woodard said, and now the company is focused on bringing down its power plant costs.“We have to continuously be focused on cost, and achieving that is paramount for the success of the geothermal industry,” he said.One common theme across the conference: a number of approaches are making rapid advancements, but experts aren’t sure when — or, in some cases, if — each specific technology will reach a tipping point where it is capable of transforming energy markets.“I don’t want to get caught in a place where we often descend in this climate solution situation, where it’s either-or,” said Peter Ellis, global director of nature climate solutions at The Nature Conservancy. “We’re talking about the greatest challenge civilization has ever faced. We need everything on the table.”The road aheadSeveral speakers stressed the need for academia, industry, and government to collaborate in pursuit of climate and energy goals. Amy Luers, senior global director of sustainability for Microsoft, compared the challenge to the Apollo spaceflight program, and she said that academic institutions need to focus more on how to scale and spur investments in green energy.“The challenge is that academic institutions are not currently set up to be able to learn the how, in driving both bottom-up and top-down shifts over time,” Luers said. “If the world is going to succeed in our road to net zero, the mindset of academia needs to shift. And fortunately, it’s starting to.”During a panel called “From lab to grid: Scaling first-of-a-kind energy technologies,” Hannan Happi, CEO of renewable energy company Exowatt, stressed that electricity is ultimately a commodity. “Electrons are all the same,” he said. “The only thing [customers] care about with regards to electrons is that they are available when they need them, and that they’re very cheap.”Melissa Zhang, principal at Azimuth Capital Management, noted that energy infrastructure development cycles typically take at least five to 10 years — longer than a U.S. political cycle. However, she warned that green energy technologies are unlikely to receive significant support at the federal level in the near future. “If you’re in something that’s a little too dependent on subsidies … there is reason to be concerned over this administration,” she said.World Energy CEO Gene Gebolys, the moderator of the lab-to-grid panel, listed off a number of companies founded at MIT. “They all have one thing in common,” he said. “They all went from somebody’s idea, to a lab, to proof-of-concept, to scale. It’s not like any of this stuff ever ends. It’s an ongoing process.” More

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      How hard is it to prevent recurring blackouts in Puerto Rico?

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      Researchers at MIT’s Laboratory for Information and Decision Systems (LIDS) have shown that using decision-making software and dynamic monitoring of weather and energy use can significantly improve resiliency in the face of weather-related outages, and can also help to efficiently integrate renewable energy sources into the grid.The researchers point out that the system they suggest might have prevented or at least lessened the kind of widespread power outage that Puerto Rico experienced last week by providing analysis to guide rerouting of power through different lines and thus limit the spread of the outage.The computer platform, which the researchers describe as DyMonDS, for Dynamic Monitoring and Decision Systems, can be used to enhance the existing operating and planning practices used in the electric industry. The platform supports interactive information exchange and decision-making between the grid operators and grid-edge users — all the distributed power sources, storage systems and software that contribute to the grid. It also supports optimization of available resources and controllable grid equipment as system conditions vary. It further lends itself to implementing cooperative decision-making by different utility- and non-utility-owned electric power grid users, including portfolios of mixed resources, users, and storage. Operating and planning the interactions of the end-to-end high-voltage transmission grid with local distribution grids and microgrids represents another major potential use of this platform.This general approach was illustrated using a set of publicly-available data on both meteorology and details of electricity production and distribution in Puerto Rico. An extended AC Optimal Power Flow software developed by SmartGridz Inc. is used for system-level optimization of controllable equipment. This provides real-time guidance for deciding how much power, and through which transmission lines, should be channeled by adjusting plant dispatch and voltage-related set points, and in extreme cases, where to reduce or cut power in order to maintain physically-implementable service for as many customers as possible. The team found that the use of such a system can help to ensure that the greatest number of critical services maintain power even during a hurricane, and at the same time can lead to a substantial decrease in the need for construction of new power plants thanks to more efficient use of existing resources.The findings are described in a paper in the journal Foundations and Trends in Electric Energy Systems, by MIT LIDS researchers Marija Ilic and Laurentiu Anton, along with recent alumna Ramapathi Jaddivada.“Using this software,” Ilic says, they show that “even during bad weather, if you predict equipment failures, and by using that information exchange, you can localize the effect of equipment failures and still serve a lot of customers, 50 percent of customers, when otherwise things would black out.”Anton says that “the way many grids today are operated is sub-optimal.” As a result, “we showed how much better they could do even under normal conditions, without any failures, by utilizing this software.” The savings resulting from this optimization, under everyday conditions, could be in the tens of percents, they say.The way utility systems plan currently, Ilic says, “usually the standard is that they have to build enough capacity and operate in real time so that if one large piece of equipment fails, like a large generator or transmission line, you still serve customers in an uninterrupted way. That’s what’s called N-minus-1.” Under this policy, if one major component of the system fails, they should be able to maintain service for at least 30 minutes. That system allows utilities to plan for how much reserve generating capacity they need to have on hand. That’s expensive, Ilic points out, because it means maintaining this reserve capacity all the time, even under normal operating conditions when it’s not needed.In addition, “right now there are no criteria for what I call N-minus-K,” she says. If bad weather causes five pieces of equipment to fail at once, “there is no software to help utilities decide what to schedule” in terms of keeping the most customers, and the most important services such as hospitals and emergency services, provided with power. They showed that even with 50 percent of the infrastructure out of commission, it would still be possible to keep power flowing to a large proportion of customers.Their work on analyzing the power situation in Puerto Rico started after the island had been devastated by hurricanes Irma and Maria. Most of the electric generation capacity is in the south, yet the largest loads are in San Juan, in the north, and Mayaguez in the west. When transmission lines get knocked down, a lot of rerouting of power needs to happen quickly.With the new systems, “the software finds the optimal adjustments for set points,” for example, changing voltages can allow for power to be redirected through less-congested lines, or can be increased to lessen power losses, Anton says.The software also helps in the long-term planning for the grid. As many fossil-fuel power plants are scheduled to be decommissioned soon in Puerto Rico, as they are in many other places, planning for how to replace that power without having to resort to greenhouse gas-emitting sources is a key to achieving carbon-reduction goals. And by analyzing usage patterns, the software can guide the placement of new renewable power sources where they can most efficiently provide power where and when it’s needed.As plants are retired or as components are affected by weather, “We wanted to ensure the dispatchability of power when the load changes,” Anton says, “but also when crucial components are lost, to ensure the robustness at each step of the retirement schedule.”One thing they found was that “if you look at how much generating capacity exists, it’s more than the peak load, even after you retire a few fossil plants,” Ilic says. “But it’s hard to deliver.” Strategic planning of new distribution lines could make a big difference.Jaddivada, director of innovation at SmartGridz, says that “we evaluated different possible architectures in Puerto Rico, and we showed the ability of this software to ensure uninterrupted electricity service. This is the most important challenge utilities have today. They have to go through a computationally tedious process to make sure the grid functions for any possible outage in the system. And that can be done in a much more efficient way through the software that the company  developed.”The project was a collaborative effort between the MIT LIDS researchers and others at MIT Lincoln Laboratory, the Pacific Northwest National Laboratory, with overall help of SmartGridz software.  More

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      in Environment

      Atmospheric observations in China show rise in emissions of a potent greenhouse gas

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      To achieve the aspirational goal of the Paris Agreement on climate change — limiting the increase in global average surface temperature to 1.5 degrees Celsius above preindustrial levels — will require its 196 signatories to dramatically reduce their greenhouse gas (GHG) emissions. Those greenhouse gases differ widely in their global warming potential (GWP), or ability to absorb radiative energy and thereby warm the Earth’s surface. For example, measured over a 100-year period, the GWP of methane is about 28 times that of carbon dioxide (CO2), and the GWP of sulfur hexafluoride (SF6) is 24,300 times that of CO2, according to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. 

      Used primarily in high-voltage electrical switchgear in electric power grids, SF6 is one of the most potent greenhouse gases on Earth. In the 21st century, atmospheric concentrations of SF6 have risen sharply along with global electric power demand, threatening the world’s efforts to stabilize the climate. This heightened demand for electric power is particularly pronounced in China, which has dominated the expansion of the global power industry in the past decade. Quantifying China’s contribution to global SF6 emissions — and pinpointing its sources in the country — could lead that nation to implement new measures to reduce them, and thereby reduce, if not eliminate, an impediment to the Paris Agreement’s aspirational goal. 

      To that end, a new study by researchers at the MIT Joint Program on the Science and Policy of Global Change, Fudan University, Peking University, University of Bristol, and Meteorological Observation Center of China Meteorological Administration determined total SF6 emissions in China over 2011-21 from atmospheric observations collected from nine stations within a Chinese network, including one station from the Advanced Global Atmospheric Gases Experiment (AGAGE) network. For comparison, global total emissions were determined from five globally distributed, relatively unpolluted “background” AGAGE stations, involving additional researchers from the Scripps Institution of Oceanography and CSIRO, Australia’s National Science Agency.

      The researchers found that SF6 emissions in China almost doubled from 2.6 gigagrams (Gg) per year in 2011, when they accounted for 34 percent of global SF6 emissions, to 5.1 Gg per year in 2021, when they accounted for 57 percent of global total SF6 emissions. This increase from China over the 10-year period — some of it emerging from the country’s less-populated western regions — was larger than the global total SF6 emissions rise, highlighting the importance of lowering SF6 emissions from China in the future.

      The open-access study, which appears in the journal Nature Communications, explores prospects for future SF6 emissions reduction in China.

      “Adopting maintenance practices that minimize SF6 leakage rates or using SF6-free equipment or SF6 substitutes in the electric power grid will benefit greenhouse-gas mitigation in China,” says Minde An, a postdoc at the MIT Center for Global Change Science (CGCS) and the study’s lead author. “We see our findings as a first step in quantifying the problem and identifying how it can be addressed.”

      Emissions of SF6 are expected to last more than 1,000 years in the atmosphere, raising the stakes for policymakers in China and around the world.

      “Any increase in SF6 emissions this century will effectively alter our planet’s radiative budget — the balance between incoming energy from the sun and outgoing energy from the Earth — far beyond the multi-decadal time frame of current climate policies,” says MIT Joint Program and CGCS Director Ronald Prinn, a coauthor of the study. “So it’s imperative that China and all other nations take immediate action to reduce, and ultimately eliminate, their SF6 emissions.”

      The study was supported by the National Key Research and Development Program of China and Shanghai B&R Joint Laboratory Project, the U.S. National Aeronautics and Space Administration, and other funding agencies.   More

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      in Energy

      To decarbonize the chemical industry, electrify it

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      The chemical industry is the world’s largest industrial energy consumer and the third-largest source of industrial emissions, according to the International Energy Agency. In 2019, the industrial sector as a whole was responsible for 24 percent of global greenhouse gas emissions. And yet, as the world races to find pathways to decarbonization, the chemical industry has been largely untouched.

      “When it comes to climate action and dealing with the emissions that come from the chemical sector, the slow pace of progress is partly technical and partly driven by the hesitation on behalf of policymakers to overly impact the economic competitiveness of the sector,” says Dharik Mallapragada, a principal research scientist at the MIT Energy Initiative.

      With so many of the items we interact with in our daily lives — from soap to baking soda to fertilizer — deriving from products of the chemical industry, the sector has become a major source of economic activity and employment for many nations, including the United States and China. But as the global demand for chemical products continues to grow, so do the industry’s emissions.

      New sustainable chemical production methods need to be developed and deployed and current emission-intensive chemical production technologies need to be reconsidered, urge the authors of a new paper published in Joule. Researchers from DC-MUSE, a multi-institution research initiative, argue that electrification powered by low-carbon sources should be viewed more broadly as a viable decarbonization pathway for the chemical industry. In this paper, they shine a light on different potential methods to do just that.

      “Generally, the perception is that electrification can play a role in this sector — in a very narrow sense — in that it can replace fossil fuel combustion by providing the heat that the combustion is providing,” says Mallapragada, a member of DC-MUSE. “What we argue is that electrification could be much more than that.”

      The researchers outline four technological pathways — ranging from more mature, near-term options to less technologically mature options in need of research investment — and present the opportunities and challenges associated with each.

      The first two pathways directly replace fossil fuel-produced heat (which facilitates the reactions inherent in chemical production) with electricity or electrochemically generated hydrogen. The researchers suggest that both options could be deployed now and potentially be used to retrofit existing facilities. Electrolytic hydrogen is also highlighted as an opportunity to replace fossil fuel-produced hydrogen (a process that emits carbon dioxide) as a critical chemical feedstock. In 2020, fossil-based hydrogen supplied nearly all hydrogen demand (90 megatons) in the chemical and refining industries — hydrogen’s largest consumers.

      The researchers note that increasing the role of electricity in decarbonizing the chemical industry will directly affect the decarbonization of the power grid. They stress that to successfully implement these technologies, their operation must coordinate with the power grid in a mutually beneficial manner to avoid overburdening it. “If we’re going to be serious about decarbonizing the sector and relying on electricity for that, we have to be creative in how we use it,” says Mallapragada. “Otherwise we run the risk of having addressed one problem, while creating a massive problem for the grid in the process.”

      Electrified processes have the potential to be much more flexible than conventional fossil fuel-driven processes. This can reduce the cost of chemical production by allowing producers to shift electricity consumption to times when the cost of electricity is low. “Process flexibility is particularly impactful during stressed power grid conditions and can help better accommodate renewable generation resources, which are intermittent and are often poorly correlated with daily power grid cycles,” says Yury Dvorkin, an associate research professor at the Johns Hopkins Ralph O’Connor Sustainable Energy Institute. “It’s beneficial for potential adopters because it can help them avoid consuming electricity during high-price periods.”

      Dvorkin adds that some intermediate energy carriers, such as hydrogen, can potentially be used as highly efficient energy storage for day-to-day operations and as long-term energy storage. This would help support the power grid during extreme events when traditional and renewable generators may be unavailable. “The application of long-duration storage is of particular interest as this is a key enabler of a low-emissions society, yet not widespread beyond pumped hydro units,” he says. “However, as we envision electrified chemical manufacturing, it is important to ensure that the supplied electricity is sourced from low-emission generators to prevent emissions leakages from the chemical to power sector.” 

      The next two pathways introduced — utilizing electrochemistry and plasma — are less technologically mature but have the potential to replace energy- and carbon-intensive thermochemical processes currently used in the industry. By adopting electrochemical processes or plasma-driven reactions instead, chemical transformations can occur at lower temperatures and pressures, potentially enhancing efficiency. “These reaction pathways also have the potential to enable more flexible, grid-responsive plants and the deployment of modular manufacturing plants that leverage distributed chemical feedstocks such as biomass waste — further enhancing sustainability in chemical manufacturing,” says Miguel Modestino, the director of the Sustainable Engineering Initiative at the New York University Tandon School of Engineering.

      A large barrier to deep decarbonization of chemical manufacturing relates to its complex, multi-product nature. But, according to the researchers, each of these electricity-driven pathways supports chemical industry decarbonization for various feedstock choices and end-of-life disposal decisions. Each should be evaluated in comprehensive techno-economic and environmental life cycle assessments to weigh trade-offs and establish suitable cost and performance metrics.

      Regardless of the pathway chosen, the researchers stress the need for active research and development and deployment of these technologies. They also emphasize the importance of workforce training and development running in parallel to technology development. As André Taylor, the director of DC-MUSE, explains, “There is a healthy skepticism in the industry regarding electrification and adoption of these technologies, as it involves processing chemicals in a new way.” The workforce at different levels of the industry hasn’t necessarily been exposed to ideas related to the grid, electrochemistry, or plasma. The researchers say that workforce training at all levels will help build greater confidence in these different solutions and support customer-driven industry adoption.

      “There’s no silver bullet, which is kind of the standard line with all climate change solutions,” says Mallapragada. “Each option has pros and cons, as well as unique advantages. But being aware of the portfolio of options in which you can use electricity allows us to have a better chance of success and of reducing emissions — and doing so in a way that supports grid decarbonization.”

      This work was supported, in part, by the Alfred P. Sloan Foundation. More