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    Collaboration between MIT and GE Vernova aims to develop and scale sustainable energy systems

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    MIT and GE Vernova today announced the creation of the MIT-GE Vernova Energy and Climate Alliance to help develop and scale sustainable energy systems across the globe.The alliance launches a five-year collaboration between MIT and GE Vernova, a global energy company that spun off from General Electric’s energy business in 2024. The endeavor will encompass research, education, and career opportunities for students, faculty, and staff across MIT’s five schools and the MIT Schwarzman College of Computing. It will focus on three main themes: decarbonization, electrification, and renewables acceleration.“This alliance will provide MIT students and researchers with a tremendous opportunity to work on energy solutions that could have real-world impact,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer and dean of the School of Engineering. “GE Vernova brings domain knowledge and expertise deploying these at scale. When our researchers develop new innovative technologies, GE Vernova is strongly positioned to bring them to global markets.”Through the alliance, GE Vernova is sponsoring research projects at MIT and providing philanthropic support for MIT research fellowships. The company will also engage with MIT’s community through participation in corporate membership programs and professional education.“It’s a privilege to combine forces with MIT’s world-class faculty and students as we work together to realize an optimistic, innovation-driven approach to solving the world’s most pressing challenges,” says Scott Strazik, GE Vernova CEO. “Through this alliance, we are proud to be able to help drive new technologies while at the same time inspire future leaders to play a meaningful role in deploying technology to improve the planet at companies like GE Vernova.”“This alliance embodies the spirit of the MIT Climate Project — combining cutting-edge research, a shared drive to tackle today’s toughest energy challenges, and a deep sense of optimism about what we can achieve together,” says Sally Kornbluth, president of MIT. “With the combined strengths of MIT and GE Vernova, we have a unique opportunity to make transformative progress in the flagship areas of electrification, decarbonization, and renewables acceleration.”The alliance, comprising a $50 million commitment, will operate within MIT’s Office of Innovation and Strategy. It will fund approximately 12 annual research projects relating to the three themes, as well as three master’s student projects in MIT’s Technology and Policy Program. The research projects will address challenges like developing and storing clean energy, as well as the creation of robust system architectures that help sustainable energy sources like solar, wind, advanced nuclear reactors, green hydrogen, and more compete with carbon-emitting sources.The projects will be selected by a joint steering committee composed of representatives from MIT and GE Vernova, following an annual Institute-wide call for proposals.The collaboration will also create approximately eight endowed GE Vernova research fellowships for MIT students, to be selected by faculty and beginning in the fall. There will also be 10 student internships that will span GE Vernova’s global operations, and GE Vernova will also sponsor programming through MIT’s New Engineering Education Transformation (NEET), which equips students with career-oriented experiential opportunities. Additionally, the alliance will create professional education programming for GE Vernova employees.“The internships and fellowships will be designed to bring students into our ecosystem,” says GE Vernova Chief Corporate Affairs Officer Roger Martella. “Students will walk our factory floor, come to our labs, be a part of our management teams, and see how we operate as business leaders. They’ll get a sense for how what they’re learning in the classroom is being applied in the real world.”Philanthropic support from GE Vernova will also support projects in MIT’s Human Insight Collaborative (MITHIC), which launched last fall to elevate human-centered research and teaching. The projects will allow faculty to explore how areas like energy and cybersecurity influence human behavior and experiences.In connection with the alliance, GE Vernova is expected to join several MIT consortia and membership programs, helping foster collaborations and dialogue between industry experts and researchers and educators across campus.With operations across more than 100 countries, GE Vernova designs, manufactures, and services technologies to generate, transfer, and store electricity with a mission to decarbonize the world. The company is headquartered in Kendall Square, right down the road from MIT, which its leaders say is not a coincidence.“We’re really good at taking proven technologies and commercializing them and scaling them up through our labs,” Martella says. “MIT excels at coming up with those ideas and being a sort of time machine that thinks outside the box to create the future. That’s why this such a great fit: We both have a commitment to research, innovation, and technology.”The alliance is the latest in MIT’s rapidly growing portfolio of research and innovation initiatives around sustainable energy systems, which also includes the Climate Project at MIT. Separate from, but complementary to, the MIT-GE Vernova Alliance, the Climate Project is a campus-wide effort to develop technological, behavioral, and policy solutions to some of the toughest problems impeding an effective global climate response. More

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      MIT Maritime Consortium sets sail

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      Around 11 billion tons of goods, or about 1.5 tons per person worldwide, are transported by sea each year, representing about 90 percent of global trade by volume. Internationally, the merchant shipping fleet numbers around 110,000 vessels. These ships, and the ports that service them, are significant contributors to the local and global economy — and they’re significant contributors to greenhouse gas emissions.A new consortium, formalized in a signing ceremony at MIT last week, aims to address climate-harming emissions in the maritime shipping industry, while supporting efforts for environmentally friendly operation in compliance with the decarbonization goals set by the International Maritime Organization.“This is a timely collaboration with key stakeholders from the maritime industry with a very bold and interdisciplinary research agenda that will establish new technologies and evidence-based standards,” says Themis Sapsis, the William Koch Professor of Marine Technology at MIT and the director of MIT’s Center for Ocean Engineering. “It aims to bring the best from MIT in key areas for commercial shipping, such as nuclear technology for commercial settings, autonomous operation and AI methods, improved hydrodynamics and ship design, cybersecurity, and manufacturing.” Co-led by Sapsis and Fotini Christia, the Ford International Professor of the Social Sciences; director of the Institute for Data, Systems, and Society (IDSS); and director of the MIT Sociotechnical Systems Research Center, the newly-launched MIT Maritime Consortium (MC) brings together MIT collaborators from across campus, including the Center for Ocean Engineering, which is housed in the Department of Mechanical Engineering; IDSS, which is housed in the MIT Schwarzman College of Computing; the departments of Nuclear Science and Engineering and Civil and Environmental Engineering; MIT Sea Grant; and others, with a national and an international community of industry experts.The Maritime Consortium’s founding members are the American Bureau of Shipping (ABS), Capital Clean Energy Carriers Corp., and HD Korea Shipbuilding and Offshore Engineering. Innovation members are Foresight-Group, Navios Maritime Partners L.P., Singapore Maritime Institute, and Dorian LPG.“The challenges the maritime industry faces are challenges that no individual company or organization can address alone,” says Christia. “The solution involves almost every discipline from the School of Engineering, as well as AI and data-driven algorithms, and policy and regulation — it’s a true MIT problem.”Researchers will explore new designs for nuclear systems consistent with the techno-economic needs and constraints of commercial shipping, economic and environmental feasibility of alternative fuels, new data-driven algorithms and rigorous evaluation criteria for autonomous platforms in the maritime space, cyber-physical situational awareness and anomaly detection, as well as 3D printing technologies for onboard manufacturing. Collaborators will also advise on research priorities toward evidence-based standards related to MIT presidential priorities around climate, sustainability, and AI.MIT has been a leading center of ship research and design for over a century, and is widely recognized for contributions to hydrodynamics, ship structural mechanics and dynamics, propeller design, and overall ship design, and its unique educational program for U.S. Navy Officers, the Naval Construction and Engineering Program. Research today is at the forefront of ocean science and engineering, with significant efforts in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. The consortium’s academic home at MIT also opens the door to cross-departmental collaboration across the Institute.The MC will launch multiple research projects designed to tackle challenges from a variety of angles, all united by cutting-edge data analysis and computation techniques. Collaborators will research new designs and methods that improve efficiency and reduce greenhouse gas emissions, explore feasibility of alternative fuels, and advance data-driven decision-making, manufacturing and materials, hydrodynamic performance, and cybersecurity.“This consortium brings a powerful collection of significant companies that, together, has the potential to be a global shipping shaper in itself,” says Christopher J. Wiernicki SM ’85, chair and chief executive officer of ABS. “The strength and uniqueness of this consortium is the members, which are all world-class organizations and real difference makers. The ability to harness the members’ experience and know-how, along with MIT’s technology reach, creates real jet fuel to drive progress,” Wiernicki says. “As well as researching key barriers, bottlenecks, and knowledge gaps in the emissions challenge, the consortium looks to enable development of the novel technology and policy innovation that will be key. Long term, the consortium hopes to provide the gravity we will need to bend the curve.” More

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      J-WAFS: Supporting food and water research across MIT

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      MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) has transformed the landscape of water and food research at MIT, driving faculty engagement and catalyzing new research and innovation in these critical areas. With philanthropic, corporate, and government support, J-WAFS’ strategic approach spans the entire research life cycle, from support for early-stage research to commercialization grants for more advanced projects.Over the past decade, J-WAFS has invested approximately $25 million in direct research funding to support MIT faculty pursuing transformative research with the potential for significant impact. “Since awarding our first cohort of seed grants in 2015, it’s remarkable to look back and see that over 10 percent of the MIT faculty have benefited from J-WAFS funding,” observes J-WAFS Executive Director Renee J. Robins ’83. “Many of these professors hadn’t worked on water or food challenges before their first J-WAFS grant.” By fostering interdisciplinary collaborations and supporting high-risk, high-reward projects, J-WAFS has amplified the capacity of MIT faculty to pursue groundbreaking research that addresses some of the world’s most pressing challenges facing our water and food systems.Drawing MIT faculty to water and food researchJ-WAFS open calls for proposals enable faculty to explore bold ideas and develop impactful approaches to tackling critical water and food system challenges. Professor Patrick Doyle’s work in water purification exemplifies this impact. “Without J-WAFS, I would have never ventured into the field of water purification,” Doyle reflects. While previously focused on pharmaceutical manufacturing and drug delivery, exposure to J-WAFS-funded peers led him to apply his expertise in soft materials to water purification. “Both the funding and the J-WAFS community led me to be deeply engaged in understanding some of the key challenges in water purification and water security,” he explains.Similarly, Professor Otto Cordero of the Department of Civil and Environmental Engineering (CEE) leveraged J-WAFS funding to pivot his research into aquaculture. Cordero explains that his first J-WAFS seed grant “has been extremely influential for my lab because it allowed me to take a step in a new direction, with no preliminary data in hand.” Cordero’s expertise is in microbial communities. He was previous unfamiliar with aquaculture, but he saw the relevance of microbial communities the health of farmed aquatic organisms.Supporting early-career facultyNew assistant professors at MIT have particularly benefited from J-WAFS funding and support. J-WAFS has played a transformative role in shaping the careers and research trajectories of many new faculty members by encouraging them to explore novel research areas, and in many instances providing their first MIT research grant.Professor Ariel Furst reflects on how pivotal J-WAFS’ investment has been in advancing her research. “This was one of the first grants I received after starting at MIT, and it has truly shaped the development of my group’s research program,” Furst explains. With J-WAFS’ backing, her lab has achieved breakthroughs in chemical detection and remediation technologies for water. “The support of J-WAFS has enabled us to develop the platform funded through this work beyond the initial applications to the general detection of environmental contaminants and degradation of those contaminants,” she elaborates. Karthish Manthiram, now a professor of chemical engineering and chemistry at Caltech, explains how J-WAFS’ early investment enabled him and other young faculty to pursue ambitious ideas. “J-WAFS took a big risk on us,” Manthiram reflects. His research on breaking the nitrogen triple bond to make ammonia for fertilizer was initially met with skepticism. However, J-WAFS’ seed funding allowed his lab to lay the groundwork for breakthroughs that later attracted significant National Science Foundation (NSF) support. “That early funding from J-WAFS has been pivotal to our long-term success,” he notes. These stories underscore the broad impact of J-WAFS’ support for early-career faculty, and its commitment to empowering them to address critical global challenges and innovate boldly.Fueling follow-on funding J-WAFS seed grants enable faculty to explore nascent research areas, but external funding for continued work is usually necessary to achieve the full potential of these novel ideas. “It’s often hard to get funding for early stage or out-of-the-box ideas,” notes J-WAFS Director Professor John H. Lienhard V. “My hope, when I founded J-WAFS in 2014, was that seed grants would allow PIs [principal investigators] to prove out novel ideas so that they would be attractive for follow-on funding. And after 10 years, J-WAFS-funded research projects have brought more than $21 million in subsequent awards to MIT.”Professor Retsef Levi led a seed study on how agricultural supply chains affect food safety, with a team of faculty spanning the MIT schools Engineering and Science as well as the MIT Sloan School of Management. The team parlayed their seed grant research into a multi-million-dollar follow-on initiative. Levi reflects, “The J-WAFS seed funding allowed us to establish the initial credibility of our team, which was key to our success in obtaining large funding from several other agencies.”Dave Des Marais was an assistant professor in the Department of CEE when he received his first J-WAFS seed grant. The funding supported his research on how plant growth and physiology are controlled by genes and interact with the environment. The seed grant helped launch his lab’s work addressing enhancing climate change resilience in agricultural systems. The work led to his Faculty Early Career Development (CAREER) Award from the NSF, a prestigious honor for junior faculty members. Now an associate professor, Des Marais’ ongoing project to further investigate the mechanisms and consequences of genomic and environmental interactions is supported by the five-year, $1,490,000 NSF grant. “J-WAFS providing essential funding to get my new research underway,” comments Des Marais.Stimulating interdisciplinary collaborationDes Marais’ seed grant was also key to developing new collaborations. He explains, “the J-WAFS grant supported me to develop a collaboration with Professor Caroline Uhler in EECS/IDSS [the Department of Electrical Engineering and Computer Science/Institute for Data, Systems, and Society] that really shaped how I think about framing and testing hypotheses. One of the best things about J-WAFS is facilitating unexpected connections among MIT faculty with diverse yet complementary skill sets.”Professors A. John Hart of the Department of Mechanical Engineering and Benedetto Marelli of CEE also launched a new interdisciplinary collaboration with J-WAFS funding. They partnered to join expertise in biomaterials, microfabrication, and manufacturing, to create printed silk-based colorimetric sensors that detect food spoilage. “The J-WAFS Seed Grant provided a unique opportunity for multidisciplinary collaboration,” Hart notes.Professors Stephen Graves in the MIT Sloan School of Management and Bishwapriya Sanyal in the Department of Urban Studies and Planning (DUSP) partnered to pursue new research on agricultural supply chains. With field work in Senegal, their J-WAFS-supported project brought together international development specialists and operations management experts to study how small firms and government agencies influence access to and uptake of irrigation technology by poorer farmers. “We used J-WAFS to spur a collaboration that would have been improbable without this grant,” they explain. Being part of the J-WAFS community also introduced them to researchers in Professor Amos Winter’s lab in the Department of Mechanical Engineering working on irrigation technologies for low-resource settings. DUSP doctoral candidate Mark Brennan notes, “We got to share our understanding of how irrigation markets and irrigation supply chains work in developing economies, and then we got to contrast that with their understanding of how irrigation system models work.”Timothy Swager, professor of chemistry, and Rohit Karnik, professor of mechanical engineering and J-WAFS associate director, collaborated on a sponsored research project supported by Xylem, Inc. through the J-WAFS Research Affiliate program. The cross-disciplinary research, which targeted the development of ultra-sensitive sensors for toxic PFAS chemicals, was conceived following a series of workshops hosted by J-WAFS. Swager and Karnik were two of the participants, and their involvement led to the collaborative proposal that Xylem funded. “J-WAFS funding allowed us to combine Swager lab’s expertise in sensing with my lab’s expertise in microfluidics to develop a cartridge for field-portable detection of PFAS,” says Karnik. “J-WAFS has enriched my research program in so many ways,” adds Swager, who is now working to commercialize the technology.Driving global collaboration and impactJ-WAFS has also helped MIT faculty establish and advance international collaboration and impactful global research. By funding and supporting projects that connect MIT researchers with international partners, J-WAFS has not only advanced technological solutions, but also strengthened cross-cultural understanding and engagement.Professor Matthew Shoulders leads the inaugural J-WAFS Grand Challenge project. In response to the first J-WAFS call for “Grand Challenge” proposals, Shoulders assembled an interdisciplinary team based at MIT to enhance and provide climate resilience to agriculture by improving the most inefficient aspect of photosynthesis, the notoriously-inefficient carbon dioxide-fixing plant enzyme RuBisCO. J-WAFS funded this high-risk/high-reward project following a competitive process that engaged external reviewers through a several rounds of iterative proposal development. The technical feedback to the team led them to researchers with complementary expertise from the Australian National University. “Our collaborative team of biochemists and synthetic biologists, computational biologists, and chemists is deeply integrated with plant biologists and field trial experts, yielding a robust feedback loop for enzyme engineering,” Shoulders says. “Together, this team will be able to make a concerted effort using the most modern, state-of-the-art techniques to engineer crop RuBisCO with an eye to helping make meaningful gains in securing a stable crop supply, hopefully with accompanying improvements in both food and water security.”Professor Leon Glicksman and Research Engineer Eric Verploegen’s team designed a low-cost cooling chamber to preserve fruits and vegetables harvested by smallholder farmers with no access to cold chain storage. J-WAFS’ guidance motivated the team to prioritize practical considerations informed by local collaborators, ensuring market competitiveness. “As our new idea for a forced-air evaporative cooling chamber was taking shape, we continually checked that our solution was evolving in a direction that would be competitive in terms of cost, performance, and usability to existing commercial alternatives,” explains Verploegen. Following the team’s initial seed grant, the team secured a J-WAFS Solutions commercialization grant, which Verploegen say “further motivated us to establish partnerships with local organizations capable of commercializing the technology earlier in the project than we might have done otherwise.” The team has since shared an open-source design as part of its commercialization strategy to maximize accessibility and impact.Bringing corporate sponsored research opportunities to MIT facultyJ-WAFS also plays a role in driving private partnerships, enabling collaborations that bridge industry and academia. Through its Research Affiliate Program, for example, J-WAFS provides opportunities for faculty to collaborate with industry on sponsored research, helping to convert scientific discoveries into licensable intellectual property (IP) that companies can turn into commercial products and services.J-WAFS introduced professor of mechanical engineering Alex Slocum to a challenge presented by its research affiliate company, Xylem: how to design a more energy-efficient pump for fluctuating flows. With centrifugal pumps consuming an estimated 6 percent of U.S. electricity annually, Slocum and his then-graduate student Hilary Johnson SM ’18, PhD ’22 developed an innovative variable volute mechanism that reduces energy usage. “Xylem envisions this as the first in a new category of adaptive pump geometry,” comments Johnson. The research produced a pump prototype and related IP that Xylem is working on commercializing. Johnson notes that these outcomes “would not have been possible without J-WAFS support and facilitation of the Xylem industry partnership.” Slocum adds, “J-WAFS enabled Hilary to begin her work on pumps, and Xylem sponsored the research to bring her to this point … where she has an opportunity to do far more than the original project called for.”Swager speaks highly of the impact of corporate research sponsorship through J-WAFS on his research and technology translation efforts. His PFAS project with Karnik described above was also supported by Xylem. “Xylem was an excellent sponsor of our research. Their engagement and feedback were instrumental in advancing our PFAS detection technology, now on the path to commercialization,” Swager says.Looking forwardWhat J-WAFS has accomplished is more than a collection of research projects; a decade of impact demonstrates how J-WAFS’ approach has been transformative for many MIT faculty members. As Professor Mathias Kolle puts it, his engagement with J-WAFS “had a significant influence on how we think about our research and its broader impacts.” He adds that it “opened my eyes to the challenges in the field of water and food systems and the many different creative ideas that are explored by MIT.” This thriving ecosystem of innovation, collaboration, and academic growth around water and food research has not only helped faculty build interdisciplinary and international partnerships, but has also led to the commercialization of transformative technologies with real-world applications. C. Cem Taşan, the POSCO Associate Professor of Metallurgy who is leading a J-WAFS Solutions commercialization team that is about to launch a startup company, sums it up by noting, “Without J-WAFS, we wouldn’t be here at all.”  As J-WAFS looks to the future, its continued commitment — supported by the generosity of its donors and partners — builds on a decade of success enabling MIT faculty to advance water and food research that addresses some of the world’s most pressing challenges. More

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      in Energy

      MIT Climate and Energy Ventures class spins out entrepreneurs — and successful companies

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      In 2014, a team of MIT students in course 15.366 (Climate and Energy Ventures) developed a plan to commercialize MIT research on how to move information between chips with light instead of electricity, reducing energy usage.After completing the class, which challenges students to identify early customers and pitch their business plan to investors, the team went on to win both grand prizes at the MIT Clean Energy Prize. Today the company, Ayar Labs, has raised a total of $370 million from a group including chip leaders AMD, Intel, and NVIDIA, to scale the manufacturing of its optical chip interconnects.Ayar Labs is one of many companies whose roots can be traced back to 15.366. In fact, more than 150 companies have been founded by alumni of the class since its founding in 2007.In the class, student teams select a technology or idea and determine the best path for its commercialization. The semester-long project, which is accompanied by lectures and mentoring, equips students with real-world experience in launching a business.“The goal is to educate entrepreneurs on how to start companies in the climate and energy space,” says Senior Lecturer Tod Hynes, who co-founded the course and has been teaching since 2008. “We do that through hands-on experience. We require students to engage with customers, talk to potential suppliers, partners, investors, and to practice their pitches to learn from that feedback.”The class attracts hundreds of student applications each year. As one of the catalysts for MIT spinoffs, it is also one reason a 2015 report found that MIT alumni-founded companies had generated roughly $1.9 trillion in annual revenues. If MIT were a country, that figure that would make it the 10th largest economy in the world, according to the report.“’Mens et manus’ (‘mind and hand’) is MIT’s motto, and the hands-on experience we try to provide in this class is hard to beat,” Hynes says. “When you actually go through the process of commercialization in the real world, you learn more and you’re in a better spot. That experiential learning approach really aligns with MIT’s approach.”Simulating a startupThe course was started by Bill Aulet, a professor of the practice at the MIT Sloan School of Management and the managing director of the Martin Trust Center for MIT Entrepreneurship. After serving as an advisor the first year and helping Aulet launch the class, Hynes began teaching the class with Aulet in the fall of 2008. The pair also launched the Climate and Energy Prize around the same time, which continues today and recently received over 150 applications from teams from around the world.A core feature of the class is connecting students in different academic fields. Each year, organizers aim to enroll students with backgrounds in science, engineering, business, and policy.“The class is meant to be accessible to anybody at MIT,” Hynes says, noting the course has also since opened to students from Harvard University. “We’re trying to pull across disciplines.”The class quickly grew in popularity around campus. Over the last few years, the course has had about 150 students apply for 50 spots.“I mentioned Climate and Energy Ventures in my application to MIT,” says Chris Johnson, a second-year graduate student in the Leaders for Global Operations (LGO) Program. “Coming into MIT, I was very interested in sustainability, and energy in particular, and also in startups. I had heard great things about the class, and I waited until my last semester to apply.”The course’s organizers select mostly graduate students, whom they prefer to be in the final year of their program so they can more easily continue working on the venture after the class is finished.“Whether or not students stick with the project from the class, it’s a great experience that will serve them in their careers,” says Jennifer Turliuk, the practice leader for climate and energy artificial intelligence at the Martin Trust Center for Entrepreneurship, who helped teach the class this fall.Hynes describes the course as a venture-building simulation. Before it begins, organizers select up to 30 technologies and ideas that are in the right stage for commercialization. Students can also come into the class with ideas or technologies they want to work on.After a few weeks of introductions and lectures, students form into multidisciplinary teams of about five and begin going through each of the 24 steps of building a startup described in Aulet’s book “Disciplined Entrepreneurship,” which includes things like engaging with potential early customers, quantifying a value proposition, and establishing a business model. Everything builds toward a one-hour final presentation that’s designed to simulate a pitch to investors or government officials.“It’s a lot of work, and because it’s a team-based project, your grade is highly dependent on your team,” Hynes says. “You also get graded by your team; that’s about 10 percent of your grade. We try to encourage people to be proactive and supportive teammates.”Students say the process is fast-paced but rewarding.“It’s definitely demanding,” says Sofie Netteberg, a graduate student who is also in the LGO program at MIT. “Depending on where you’re at with your technology, you can be moving very quickly. That’s the stage that I was in, which I found really engaging. We basically just had a lab technology, and it was like, ‘What do we do next?’ You also get a ton of support from the professors.”From the classroom to the worldThis fall’s final presentations took place at the headquarters of the MIT-affiliated venture firm The Engine in front of an audience of professors, investors, members of foundations supporting entrepreneurship, and more.“We got to hear feedback from people who would be the real next step for the technology if the startup gets up and running,” said Johnson, whose team was commercializing a method for storing energy in concrete. “That was really valuable. We know that these are not only people we might see in the next month or the next funding rounds, but they’re also exactly the type of people that are going to give us the questions we should be thinking about. It was clarifying.”Throughout the semester, students treated the project like a real venture they’d be working on well beyond the length of the class.“No one’s really thinking about this class for the grade; it’s about the learning,” says Netteberg, whose team was encouraged to keep working on their electrolyzer technology designed to more efficiently produce green hydrogen. “We’re not stressed about getting an A. If we want to keep working on this, we want real feedback: What do you think we did well? What do we need to keep working on?”Hynes says several investors expressed interest in supporting the businesses coming out of the class. Moving forward, he hopes students embrace the test-bed environment his team has created for them and try bold new things.“People have been very pragmatic over the years, which is good, but also potentially limiting,” Hynes says. “This is also an opportunity to do something that’s a little further out there — something that has really big potential impact if it comes together. This is the time where students get to experiment, so why not try something big?” More

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      in Energy

      Explained: Generative AI’s environmental impact

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      In a two-part series, MIT News explores the environmental implications of generative AI. In this article, we look at why this technology is so resource-intensive. A second piece will investigate what experts are doing to reduce genAI’s carbon footprint and other impacts.The excitement surrounding potential benefits of generative AI, from improving worker productivity to advancing scientific research, is hard to ignore. While the explosive growth of this new technology has enabled rapid deployment of powerful models in many industries, the environmental consequences of this generative AI “gold rush” remain difficult to pin down, let alone mitigate.The computational power required to train generative AI models that often have billions of parameters, such as OpenAI’s GPT-4, can demand a staggering amount of electricity, which leads to increased carbon dioxide emissions and pressures on the electric grid.Furthermore, deploying these models in real-world applications, enabling millions to use generative AI in their daily lives, and then fine-tuning the models to improve their performance draws large amounts of energy long after a model has been developed.Beyond electricity demands, a great deal of water is needed to cool the hardware used for training, deploying, and fine-tuning generative AI models, which can strain municipal water supplies and disrupt local ecosystems. The increasing number of generative AI applications has also spurred demand for high-performance computing hardware, adding indirect environmental impacts from its manufacture and transport.“When we think about the environmental impact of generative AI, it is not just the electricity you consume when you plug the computer in. There are much broader consequences that go out to a system level and persist based on actions that we take,” says Elsa A. Olivetti, professor in the Department of Materials Science and Engineering and the lead of the Decarbonization Mission of MIT’s new Climate Project.Olivetti is senior author of a 2024 paper, “The Climate and Sustainability Implications of Generative AI,” co-authored by MIT colleagues in response to an Institute-wide call for papers that explore the transformative potential of generative AI, in both positive and negative directions for society.Demanding data centersThe electricity demands of data centers are one major factor contributing to the environmental impacts of generative AI, since data centers are used to train and run the deep learning models behind popular tools like ChatGPT and DALL-E.A data center is a temperature-controlled building that houses computing infrastructure, such as servers, data storage drives, and network equipment. For instance, Amazon has more than 100 data centers worldwide, each of which has about 50,000 servers that the company uses to support cloud computing services.While data centers have been around since the 1940s (the first was built at the University of Pennsylvania in 1945 to support the first general-purpose digital computer, the ENIAC), the rise of generative AI has dramatically increased the pace of data center construction.“What is different about generative AI is the power density it requires. Fundamentally, it is just computing, but a generative AI training cluster might consume seven or eight times more energy than a typical computing workload,” says Noman Bashir, lead author of the impact paper, who is a Computing and Climate Impact Fellow at MIT Climate and Sustainability Consortium (MCSC) and a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL).Scientists have estimated that the power requirements of data centers in North America increased from 2,688 megawatts at the end of 2022 to 5,341 megawatts at the end of 2023, partly driven by the demands of generative AI. Globally, the electricity consumption of data centers rose to 460 terawatts in 2022. This would have made data centers the 11th largest electricity consumer in the world, between the nations of Saudi Arabia (371 terawatts) and France (463 terawatts), according to the Organization for Economic Co-operation and Development.By 2026, the electricity consumption of data centers is expected to approach 1,050 terawatts (which would bump data centers up to fifth place on the global list, between Japan and Russia).While not all data center computation involves generative AI, the technology has been a major driver of increasing energy demands.“The demand for new data centers cannot be met in a sustainable way. The pace at which companies are building new data centers means the bulk of the electricity to power them must come from fossil fuel-based power plants,” says Bashir.The power needed to train and deploy a model like OpenAI’s GPT-3 is difficult to ascertain. In a 2021 research paper, scientists from Google and the University of California at Berkeley estimated the training process alone consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide.While all machine-learning models must be trained, one issue unique to generative AI is the rapid fluctuations in energy use that occur over different phases of the training process, Bashir explains.Power grid operators must have a way to absorb those fluctuations to protect the grid, and they usually employ diesel-based generators for that task.Increasing impacts from inferenceOnce a generative AI model is trained, the energy demands don’t disappear.Each time a model is used, perhaps by an individual asking ChatGPT to summarize an email, the computing hardware that performs those operations consumes energy. Researchers have estimated that a ChatGPT query consumes about five times more electricity than a simple web search.“But an everyday user doesn’t think too much about that,” says Bashir. “The ease-of-use of generative AI interfaces and the lack of information about the environmental impacts of my actions means that, as a user, I don’t have much incentive to cut back on my use of generative AI.”With traditional AI, the energy usage is split fairly evenly between data processing, model training, and inference, which is the process of using a trained model to make predictions on new data. However, Bashir expects the electricity demands of generative AI inference to eventually dominate since these models are becoming ubiquitous in so many applications, and the electricity needed for inference will increase as future versions of the models become larger and more complex.Plus, generative AI models have an especially short shelf-life, driven by rising demand for new AI applications. Companies release new models every few weeks, so the energy used to train prior versions goes to waste, Bashir adds. New models often consume more energy for training, since they usually have more parameters than their predecessors.While electricity demands of data centers may be getting the most attention in research literature, the amount of water consumed by these facilities has environmental impacts, as well.Chilled water is used to cool a data center by absorbing heat from computing equipment. It has been estimated that, for each kilowatt hour of energy a data center consumes, it would need two liters of water for cooling, says Bashir.“Just because this is called ‘cloud computing’ doesn’t mean the hardware lives in the cloud. Data centers are present in our physical world, and because of their water usage they have direct and indirect implications for biodiversity,” he says.The computing hardware inside data centers brings its own, less direct environmental impacts.While it is difficult to estimate how much power is needed to manufacture a GPU, a type of powerful processor that can handle intensive generative AI workloads, it would be more than what is needed to produce a simpler CPU because the fabrication process is more complex. A GPU’s carbon footprint is compounded by the emissions related to material and product transport.There are also environmental implications of obtaining the raw materials used to fabricate GPUs, which can involve dirty mining procedures and the use of toxic chemicals for processing.Market research firm TechInsights estimates that the three major producers (NVIDIA, AMD, and Intel) shipped 3.85 million GPUs to data centers in 2023, up from about 2.67 million in 2022. That number is expected to have increased by an even greater percentage in 2024.The industry is on an unsustainable path, but there are ways to encourage responsible development of generative AI that supports environmental objectives, Bashir says.He, Olivetti, and their MIT colleagues argue that this will require a comprehensive consideration of all the environmental and societal costs of generative AI, as well as a detailed assessment of the value in its perceived benefits.“We need a more contextual way of systematically and comprehensively understanding the implications of new developments in this space. Due to the speed at which there have been improvements, we haven’t had a chance to catch up with our abilities to measure and understand the tradeoffs,” Olivetti says. More

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      in Energy

      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 Energy

      Helping students bring about decarbonization, from benchtop to global energy marketplace

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      MIT students are adept at producing research and innovations at the cutting edge of their fields. But addressing a problem as large as climate change requires understanding the world’s energy landscape, as well as the ways energy technologies evolve over time.Since 2010, the course IDS.521/IDS.065 (Energy Systems for Climate Change Mitigation) has equipped students with the skills they need to evaluate the various energy decarbonization pathways available to the world. The work is designed to help them maximize their impact on the world’s emissions by making better decisions along their respective career paths.“The question guiding my teaching and research is how do we solve big societal challenges with technology, and how can we be more deliberate in developing and supporting technologies to get us there?” says Professor Jessika Trancik, who started the course to help fill a gap in knowledge about the ways technologies evolve and scale over time.Since its inception in 2010, the course has attracted graduate students from across MIT’s five schools. The course has also recently opened to undergraduate students and been adapted to an online course for professionals.Class sessions alternate between lectures and student discussions that lead up to semester-long projects in which groups of students explore specific strategies and technologies for reducing global emissions. This year’s projects span several topics, including how quickly transmission infrastructure is expanding, the relationship between carbon emissions and human development, and how to decarbonize the production of key chemicals.The curriculum is designed to help students identify the most promising ways to mitigate climate change whether they plan to be scientists, engineers, policymakers, investors, urban planners, or just more informed citizens.“We’re coming at this issue from both sides,” explains Trancik, who is part of MIT’s Institute for Data, Systems, and Society. “Engineers are used to designing a technology to work as well as possible here and now, but not always thinking over a longer time horizon about a technology evolving and succeeding in the global marketplace. On the flip side, for students at the macro level, often studies in policy and economics of technological change don’t fully account for the physical and engineering constraints of rates of improvement. But all of that information allows you to make better decisions.”Bridging the gapAs a young researcher working on low-carbon polymers and electrode materials for solar cells, Trancik always wondered how the materials she worked on would scale in the real world. They might achieve promising performance benchmarks in the lab, but would they actually make a difference in mitigating climate change? Later, she began focusing increasingly on developing methods for predicting how technologies might evolve.“I’ve always been interested in both the macro and the micro, or even nano, scales,” Trancik says. “I wanted to know how to bridge these new technologies we’re working on with the big picture of where we want to go.”Trancik’ described her technology-grounded approach to decarbonization in a paper that formed the basis for IDS.065. In the paper, she presented a way to evaluate energy technologies against climate-change mitigation goals while focusing on the technology’s evolution.“That was a departure from previous approaches, which said, given these technologies with fixed characteristics and assumptions about their rates of change, how do I choose the best combination?” Trancik explains. “Instead we asked: Given a goal, how do we develop the best technologies to meet that goal? That inverts the problem in a way that’s useful to engineers developing these technologies, but also to policymakers and investors that want to use the evolution of technologies as a tool for achieving their objectives.”This past semester, the class took place every Tuesday and Thursday in a classroom on the first floor of the Stata Center. Students regularly led discussions where they reflected on the week’s readings and offered their own insights.“Students always share their takeaways and get to ask open questions of the class,” says Megan Herrington, a PhD candidate in the Department of Chemical Engineering. “It helps you understand the readings on a deeper level because people with different backgrounds get to share their perspectives on the same questions and problems. Everybody comes to class with their own lens, and the class is set up to highlight those differences.”The semester begins with an overview of climate science, the origins of emissions reductions goals, and technology’s role in achieving those goals. Students then learn how to evaluate technologies against decarbonization goals.But technologies aren’t static, and neither is the world. Later lessons help students account for the change of technologies over time, identifying the mechanisms for that change and even forecasting rates of change.Students also learn about the role of government policy. This year, Trancik shared her experience traveling to the COP29 United Nations Climate Change Conference.“It’s not just about technology,” Trancik says. “It’s also about the behaviors that we engage in and the choices we make. But technology plays a major role in determining what set of choices we can make.”From the classroom to the worldStudents in the class say it has given them a new perspective on climate change mitigation.“I have really enjoyed getting to see beyond the research people are doing at the benchtop,” says Herrington. “It’s interesting to see how certain materials or technologies that aren’t scalable yet may fit into a larger transformation in energy delivery and consumption. It’s also been interesting to pull back the curtain on energy systems analysis to understand where the metrics we cite in energy-related research originate from, and to anticipate trajectories of emerging technologies.”Onur Talu, a first-year master’s student in the Technology and Policy Program, says the class has made him more hopeful.“I came into this fairly pessimistic about the climate,” says Talu, who has worked for clean technology startups in the past. “This class has taught me different ways to look at the problem of climate change mitigation and developing renewable technologies. It’s also helped put into perspective how much we’ve accomplished so far.”Several student projects from the class over the years have been developed into papers published in peer-reviewed journals. They have also been turned into tools, like carboncounter.com, which plots the emissions and costs of cars and has been featured in The New York Times.Former class students have also launched startups; Joel Jean SM ’13, PhD ’17, for example, started Swift Solar. Others have drawn on the course material to develop impactful careers in government and academia, such as Patrick Brown PhD ’16 at the National Renewable Energy Laboratory and Leah Stokes SM ’15, PhD ’15 at the University of California at Santa Barbara.Overall, students say the course helps them take a more informed approach to applying their skills toward addressing climate change.“It’s not enough to just know how bad climate change could be,” says Yu Tong, a first-year master’s student in civil and environmental engineering. “It’s also important to understand how technology can work to mitigate climate change from both a technological and market perspective. It’s about employing technology to solve these issues rather than just working in a vacuum.” More

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      in Environment

      In a unique research collaboration, students make the case for less e-waste

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      Brought together as part of the Social and Ethical Responsibilities of Computing (SERC) initiative within the MIT Schwarzman College of Computing, a community of students known as SERC Scholars is collaborating to examine the most urgent problems humans face in the digital landscape.Each semester, students from all levels from across MIT are invited to join a different topical working group led by a SERC postdoctoral associate. Each group delves into a specific issue — such as surveillance or data ownership — culminating in a final project presented at the end of the term.Typically, students complete the program with hands-on experience conducting research in a new cross-disciplinary field. However, one group of undergraduate and graduate students recently had the unique opportunity to enhance their resume by becoming published authors of a case study about the environmental and climate justice implications of the electronics hardware life cycle.Although it’s not uncommon for graduate students to co-author case studies, it’s unusual for undergraduates to earn this opportunity — and for their audience to be other undergraduates around the world.“Our team was insanely interdisciplinary,” says Anastasia Dunca, a junior studying computer science and one of the co-authors. “I joined the SERC Scholars Program because I liked the idea of being part of a cohort from across MIT working on a project that utilized all of our skillsets. It also helps [undergraduates] learn the ins and outs of computing ethics research.”Case study co-author Jasmin Liu, an MBA student in the MIT Sloan School of Management, sees the program as a platform to learn about the intersection of technology, society, and ethics: “I met team members spanning computer science, urban planning, to art/culture/technology. I was excited to work with a diverse team because I know complex problems must be approached with many different perspectives. Combining my background in humanities and business with the expertise of others allowed us to be more innovative and comprehensive.”Christopher Rabe, a former SERC postdoc who facilitated the group, says, “I let the students take the lead on identifying the topic and conducting the research.” His goal for the group was to challenge students across disciplines to develop a working definition of climate justice.From mining to e-wasteThe SERC Scholars’ case study, “From Mining to E-waste: The Environmental and Climate Justice Implications of the Electronics Hardware Life Cycle,” was published by the MIT Case Studies in Social and Ethical Responsibilities of Computing.The ongoing case studies series, which releases new issues twice a year on an open-source platform, is enabling undergraduate instructors worldwide to incorporate research-based education materials on computing ethics into their existing class syllabi.This particular case study broke down the electronics life cycle from mining to manufacturing, usage, and disposal. It offered an in-depth look at how this cycle promotes inequity in the Global South. Mining for the average of 60 minerals that power everyday devices lead to illegal deforestation, compromising air quality in the Amazon, and triggering armed conflict in Congo. Manufacturing leads to proven health risks for both formal and informal workers, some of whom are child laborers.Life cycle assessment and circular economy are proposed as mechanisms for analyzing environmental and climate justice issues in the electronics life cycle. Rather than posing solutions, the case study offers readers entry points for further discussion and for assessing their own individual responsibility as producers of e-waste.Crufting and crafting a case studyDunca joined Rabe’s working group, intrigued by the invitation to conduct a rigorous literature review examining issues like data center resource and energy use, manufacturing waste, ethical issues with AI, and climate change. Rabe quickly realized that a common thread among all participants was an interest in understanding and reducing e-waste and its impact on the environment.“I came in with the idea of us co-authoring a case study,” Rabe said. However, the writing-intensive process was initially daunting to those students who were used to conducting applied research. Once Rabe created sub-groups with discrete tasks, the steps for researching, writing, and iterating a case study became more approachable.For Ellie Bultena, an undergraduate student studying linguistics and philosophy and a contributor to the study, that meant conducting field research on the loading dock of MIT’s Stata Center, where students and faculty go “crufting” through piles of clunky printers, broken computers, and used lab equipment discarded by the Institute’s labs, departments, and individual users.Although not a formally sanctioned activity on-campus, “crufting” is the act of gleaning usable parts from these junk piles to be repurposed into new equipment or art. Bultena’s respondents, who opted to be anonymous, said that MIT could do better when it comes to the amount of e-waste generated and suggested that formal strategies could be implemented to encourage community members to repair equipment more easily or recycle more formally.Rabe, now an education program director at the MIT Environmental Solutions Initiative, is hopeful that through the Zero-Carbon Campus Initiative, which commits MIT to eliminating all direct emissions by 2050, MIT will ultimately become a model for other higher education institutions.Although the group lacked the time and resources to travel to communities in the Global South that they profiled in their case study, members leaned into exhaustive secondary research, collecting data on how some countries are irresponsibly dumping e-waste. In contrast, others have developed alternative solutions that can be duplicated elsewhere and scaled.“We source materials, manufacture them, and then throw them away,” Lelia Hampton says. A PhD candidate in electrical engineering and computer science and another co-author, Hampton jumped at the opportunity to serve in a writing role, bringing together the sub-groups research findings. “I’d never written a case study, and it was exciting. Now I want to write 10 more.”The content directly informed Hampton’s dissertation research, which “looks at applying machine learning to climate justice issues such as urban heat islands.” She said that writing a case study that is accessible to general audiences upskilled her for the non-profit organization she’s determined to start. “It’s going to provide communities with free resources and data needed to understand how they are impacted by climate change and begin to advocate against injustice,” Hampton explains.Dunca, Liu, Rabe, Bultena, and Hampton are joined on the case study by fellow authors Mrinalini Singha, a graduate student in the Art, Culture, and Technology program; Sungmoon Lim, a graduate student in urban studies and planning and EECS; Lauren Higgins, an undergraduate majoring in political science; and Madeline Schlegal, a Northeastern University co-op student.Taking the case study to classrooms around the worldAlthough PhD candidates have contributed to previous case studies in the series, this publication is the first to be co-authored with MIT undergraduates. Like any other peer-reviewed journal, before publication, the SERC Scholars’ case study was anonymously reviewed by senior scholars drawn from various fields.The series editor, David Kaiser, also served as one of SERC’s inaugural associate deans and helped shape the program. “The case studies, by design, are short, easy to read, and don’t take up lots of time,” Kaiser explained. “They are gateways for students to explore, and instructors can cover a topic that has likely already been on their mind.” This semester, Kaiser, the Germeshausen Professor of the History of Science and a professor of physics, is teaching STS.004 (Intersections: Science, Technology, and the World), an undergraduate introduction to the field of science, technology, and society. The last month of the semester has been dedicated wholly to SERC case studies, one of which is: “From Mining to E-Waste.”Hampton was visibly moved to hear that the case study is being used at MIT but also by some of the 250,000 visitors to the SERC platform, many of whom are based in the Global South and directly impacted by the issues she and her cohort researched. “Many students are focused on climate, whether through computer science, data science, or mechanical engineering. I hope that this case study educates them on environmental and climate aspects of e-waste and computing.” More

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      in Energy

      So you want to build a solar or wind farm? Here’s how to decide where.

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      Deciding where to build new solar or wind installations is often left up to individual developers or utilities, with limited overall coordination. But a new study shows that regional-level planning using fine-grained weather data, information about energy use, and energy system modeling can make a big difference in the design of such renewable power installations. This also leads to more efficient and economically viable operations.The findings show the benefits of coordinating the siting of solar farms, wind farms, and storage systems, taking into account local and temporal variations in wind, sunlight, and energy demand to maximize the utilization of renewable resources. This approach can reduce the need for sizable investments in storage, and thus the total system cost, while maximizing availability of clean power when it’s needed, the researchers found.The study, appearing today in the journal Cell Reports Sustainability, was co-authored by Liying Qiu and Rahman Khorramfar, postdocs in MIT’s Department of Civil and Environmental Engineering, and professors Saurabh Amin and Michael Howland.Qiu, the lead author, says that with the team’s new approach, “we can harness the resource complementarity, which means that renewable resources of different types, such as wind and solar, or different locations can compensate for each other in time and space. This potential for spatial complementarity to improve system design has not been emphasized and quantified in existing large-scale planning.”Such complementarity will become ever more important as variable renewable energy sources account for a greater proportion of power entering the grid, she says. By coordinating the peaks and valleys of production and demand more smoothly, she says, “we are actually trying to use the natural variability itself to address the variability.”Typically, in planning large-scale renewable energy installations, Qiu says, “some work on a country level, for example saying that 30 percent of energy should be wind and 20 percent solar. That’s very general.” For this study, the team looked at both weather data and energy system planning modeling on a scale of less than 10-kilometer (about 6-mile) resolution. “It’s a way of determining where should we, exactly, build each renewable energy plant, rather than just saying this city should have this many wind or solar farms,” she explains.To compile their data and enable high-resolution planning, the researchers relied on a variety of sources that had not previously been integrated. They used high-resolution meteorological data from the National Renewable Energy Laboratory, which is publicly available at 2-kilometer resolution but rarely used in a planning model at such a fine scale. These data were combined with an energy system model they developed to optimize siting at a sub-10-kilometer resolution. To get a sense of how the fine-scale data and model made a difference in different regions, they focused on three U.S. regions — New England, Texas, and California — analyzing up to 138,271 possible siting locations simultaneously for a single region.By comparing the results of siting based on a typical method vs. their high-resolution approach, the team showed that “resource complementarity really helps us reduce the system cost by aligning renewable power generation with demand,” which should translate directly to real-world decision-making, Qiu says. “If an individual developer wants to build a wind or solar farm and just goes to where there is the most wind or solar resource on average, it may not necessarily guarantee the best fit into a decarbonized energy system.”That’s because of the complex interactions between production and demand for electricity, as both vary hour by hour, and month by month as seasons change. “What we are trying to do is minimize the difference between the energy supply and demand rather than simply supplying as much renewable energy as possible,” Qiu says. “Sometimes your generation cannot be utilized by the system, while at other times, you don’t have enough to match the demand.”In New England, for example, the new analysis shows there should be more wind farms in locations where there is a strong wind resource during the night, when solar energy is unavailable. Some locations tend to be windier at night, while others tend to have more wind during the day.These insights were revealed through the integration of high-resolution weather data and energy system optimization used by the researchers. When planning with lower resolution weather data, which was generated at a 30-kilometer resolution globally and is more commonly used in energy system planning, there was much less complementarity among renewable power plants. Consequently, the total system cost was much higher. The complementarity between wind and solar farms was enhanced by the high-resolution modeling due to improved representation of renewable resource variability.The researchers say their framework is very flexible and can be easily adapted to any region to account for the local geophysical and other conditions. In Texas, for example, peak winds in the west occur in the morning, while along the south coast they occur in the afternoon, so the two naturally complement each other.Khorramfar says that this work “highlights the importance of data-driven decision making in energy planning.” The work shows that using such high-resolution data coupled with carefully formulated energy planning model “can drive the system cost down, and ultimately offer more cost-effective pathways for energy transition.”One thing that was surprising about the findings, says Amin, who is a principal investigator in the MIT Laboratory of Information and Data Systems, is how significant the gains were from analyzing relatively short-term variations in inputs and outputs that take place in a 24-hour period. “The kind of cost-saving potential by trying to harness complementarity within a day was not something that one would have expected before this study,” he says.In addition, Amin says, it was also surprising how much this kind of modeling could reduce the need for storage as part of these energy systems. “This study shows that there is actually a hidden cost-saving potential in exploiting local patterns in weather, that can result in a monetary reduction in storage cost.”The system-level analysis and planning suggested by this study, Howland says, “changes how we think about where we site renewable power plants and how we design those renewable plants, so that they maximally serve the energy grid. It has to go beyond just driving down the cost of energy of individual wind or solar farms. And these new insights can only be realized if we continue collaborating across traditional research boundaries, by integrating expertise in fluid dynamics, atmospheric science, and energy engineering.”The research was supported by the MIT Climate and Sustainability Consortium and MIT Climate Grand Challenges. More