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    Energy storage important to creating affordable, reliable, deeply decarbonized electricity systems

    In deeply decarbonized energy systems utilizing high penetrations of variable renewable energy (VRE), energy storage is needed to keep the lights on and the electricity flowing when the sun isn’t shining and the wind isn’t blowing — when generation from these VRE resources is low or demand is high. The MIT Energy Initiative’s Future of Energy Storage study makes clear the need for energy storage and explores pathways using VRE resources and storage to reach decarbonized electricity systems efficiently by 2050.

    “The Future of Energy Storage,” a new multidisciplinary report from the MIT Energy Initiative (MITEI), urges government investment in sophisticated analytical tools for planning, operation, and regulation of electricity systems in order to deploy and use storage efficiently. Because storage technologies will have the ability to substitute for or complement essentially all other elements of a power system, including generation, transmission, and demand response, these tools will be critical to electricity system designers, operators, and regulators in the future. The study also recommends additional support for complementary staffing and upskilling programs at regulatory agencies at the state and federal levels. 

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    Why is energy storage so important?

    The MITEI report shows that energy storage makes deep decarbonization of reliable electric power systems affordable. “Fossil fuel power plant operators have traditionally responded to demand for electricity — in any given moment — by adjusting the supply of electricity flowing into the grid,” says MITEI Director Robert Armstrong, the Chevron Professor of Chemical Engineering and chair of the Future of Energy Storage study. “But VRE resources such as wind and solar depend on daily and seasonal variations as well as weather fluctuations; they aren’t always available to be dispatched to follow electricity demand. Our study finds that energy storage can help VRE-dominated electricity systems balance electricity supply and demand while maintaining reliability in a cost-effective manner — that in turn can support the electrification of many end-use activities beyond the electricity sector.”

    The three-year study is designed to help government, industry, and academia chart a path to developing and deploying electrical energy storage technologies as a way of encouraging electrification and decarbonization throughout the economy, while avoiding excessive or inequitable burdens.

    Focusing on three distinct regions of the United States, the study shows the need for a varied approach to energy storage and electricity system design in different parts of the country. Using modeling tools to look out to 2050, the study team also focuses beyond the United States, to emerging market and developing economy (EMDE) countries, particularly as represented by India. The findings highlight the powerful role storage can play in EMDE nations. These countries are expected to see massive growth in electricity demand over the next 30 years, due to rapid overall economic expansion and to increasing adoption of electricity-consuming technologies such as air conditioning. In particular, the study calls attention to the pivotal role battery storage can play in decarbonizing grids in EMDE countries that lack access to low-cost gas and currently rely on coal generation.

    The authors find that investment in VRE combined with storage is favored over new coal generation over the medium and long term in India, although existing coal plants may linger unless forced out by policy measures such as carbon pricing. 

    “Developing countries are a crucial part of the global decarbonization challenge,” says Robert Stoner, the deputy director for science and technology at MITEI and one of the report authors. “Our study shows how they can take advantage of the declining costs of renewables and storage in the coming decades to become climate leaders without sacrificing economic development and modernization.”

    The study examines four kinds of storage technologies: electrochemical, thermal, chemical, and mechanical. Some of these technologies, such as lithium-ion batteries, pumped storage hydro, and some thermal storage options, are proven and available for commercial deployment. The report recommends that the government focus R&D efforts on other storage technologies, which will require further development to be available by 2050 or sooner — among them, projects to advance alternative electrochemical storage technologies that rely on earth-abundant materials. It also suggests government incentives and mechanisms that reward success but don’t interfere with project management. The report calls for the federal government to change some of the rules governing technology demonstration projects to enable more projects on storage. Policies that require cost-sharing in exchange for intellectual property rights, the report argues, discourage the dissemination of knowledge. The report advocates for federal requirements for demonstration projects that share information with other U.S. entities.

    The report says many existing power plants that are being shut down can be converted to useful energy storage facilities by replacing their fossil fuel boilers with thermal storage and new steam generators. This retrofit can be done using commercially available technologies and may be attractive to plant owners and communities — using assets that would otherwise be abandoned as electricity systems decarbonize.  

    The study also looks at hydrogen and concludes that its use for storage will likely depend on the extent to which hydrogen is used in the overall economy. That broad use of hydrogen, the report says, will be driven by future costs of hydrogen production, transportation, and storage — and by the pace of innovation in hydrogen end-use applications. 

    The MITEI study predicts the distribution of hourly wholesale prices or the hourly marginal value of energy will change in deeply decarbonized power systems — with many more hours of very low prices and more hours of high prices compared to today’s wholesale markets. So the report recommends systems adopt retail pricing and retail load management options that reward all consumers for shifting electricity use away from times when high wholesale prices indicate scarcity, to times when low wholesale prices signal abundance. 

    The Future of Energy Storage study is the ninth in MITEI’s “Future of” series, exploring complex and vital issues involving energy and the environment. Previous studies have focused on nuclear power, solar energy, natural gas, geothermal energy, and coal (with capture and sequestration of carbon dioxide emissions), as well as on systems such as the U.S. electric power grid. The Alfred P. Sloan Foundation and the Heising-Simons Foundation provided core funding for MITEI’s Future of Energy Storage study. MITEI members Equinor and Shell provided additional support.  More

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    MIT expands research collaboration with Commonwealth Fusion Systems to build net energy fusion machine, SPARC

    MIT’s Plasma Science and Fusion Center (PSFC) will substantially expand its fusion energy research and education activities under a new five-year agreement with Institute spinout Commonwealth Fusion Systems (CFS).

    “This expanded relationship puts MIT and PSFC in a prime position to be an even stronger academic leader that can help deliver the research and education needs of the burgeoning fusion energy industry, in part by utilizing the world’s first burning plasma and net energy fusion machine, SPARC,” says PSFC director Dennis Whyte. “CFS will build SPARC and develop a commercial fusion product, while MIT PSFC will focus on its core mission of cutting-edge research and education.”

    Commercial fusion energy has the potential to play a significant role in combating climate change, and there is a concurrent increase in interest from the energy sector, governments, and foundations. The new agreement, administered by the MIT Energy Initiative (MITEI), where CFS is a startup member, will help PSFC expand its fusion technology efforts with a wider variety of sponsors. The collaboration enables rapid execution at scale and technology transfer into the commercial sector as soon as possible.

    This new agreement doubles CFS’ financial commitment to PSFC, enabling greater recruitment and support of students, staff, and faculty. “We’ll significantly increase the number of graduate students and postdocs, and just as important they will be working on a more diverse set of fusion science and technology topics,” notes Whyte. It extends the collaboration between PSFC and CFS that resulted in numerous advances toward fusion power plants, including last fall’s demonstration of a high-temperature superconducting (HTS) fusion electromagnet with record-setting field strength of 20 tesla.

    The combined magnetic fusion efforts at PSFC will surpass those in place during the operations of the pioneering Alcator C-Mod tokamak device that operated from 1993 to 2016. This increase in activity reflects a moment when multiple fusion energy technologies are seeing rapidly accelerating development worldwide, and the emergence of a new fusion energy industry that would require thousands of trained people.

    MITEI director Robert Armstrong adds, “Our goal from the beginning was to create a membership model that would allow startups who have specific research challenges to leverage the MITEI ecosystem, including MIT faculty, students, and other MITEI members. The team at the PSFC and MITEI have worked seamlessly to support CFS, and we are excited for this next phase of the relationship.”

    PSFC is supporting CFS’ efforts toward realizing the SPARC fusion platform, which facilitates rapid development and refinement of elements (including HTS magnets) needed to build ARC, a compact, modular, high-field fusion power plant that would set the stage for commercial fusion energy production. The concepts originated in Whyte’s nuclear science and engineering class 22.63 (Principles of Fusion Engineering) and have been carried forward by students and PSFC staff, many of whom helped found CFS; the new activity will expand research into advanced technologies for the envisioned pilot plant.

    “This has been an incredibly effective collaboration that has resulted in a major breakthrough for commercial fusion with the successful demonstration of revolutionary fusion magnet technology that will enable the world’s first commercially relevant net energy fusion device, SPARC, currently under construction,” says Bob Mumgaard SM ’15, PhD ’15, CEO of Commonwealth Fusion Systems. “We look forward to this next phase in the collaboration with MIT as we tackle the critical research challenges ahead for the next steps toward fusion power plant development.”

    In the push for commercial fusion energy, the next five years are critical, requiring intensive work on materials longevity, heat transfer, fuel recycling, maintenance, and other crucial aspects of power plant development. It will need innovation from almost every engineering discipline. “Having great teams working now, it will cut the time needed to move from SPARC to ARC, and really unleash the creativity. And the thing MIT does so well is cut across disciplines,” says Whyte.

    “To address the climate crisis, the world needs to deploy existing clean energy solutions as widely and as quickly as possible, while at the same time developing new technologies — and our goal is that those new technologies will include fusion power,” says Maria T. Zuber, MIT’s vice president for research. “To make new climate solutions a reality, we need focused, sustained collaborations like the one between MIT and Commonwealth Fusion Systems. Delivering fusion power onto the grid is a monumental challenge, and the combined capabilities of these two organizations are what the challenge demands.”

    On a strategic level, climate change and the imperative need for widely implementable carbon-free energy have helped orient the PSFC team toward scalability. “Building one or 10 fusion plants doesn’t make a difference — we have to build thousands,” says Whyte. “The design decisions we make will impact the ability to do that down the road. The real enemy here is time, and we want to remove as many impediments as possible and commit to funding a new generation of scientific leaders. Those are critically important in a field with as much interdisciplinary integration as fusion.” More

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    Absent legislative victory, the president can still meet US climate goals

    The most recent United Nations climate change report indicates that without significant action to mitigate global warming, the extent and magnitude of climate impacts — from floods to droughts to the spread of disease — could outpace the world’s ability to adapt to them. The latest effort to introduce meaningful climate legislation in the United States Congress, the Build Back Better bill, has stalled. The climate package in that bill — $555 billion in funding for climate resilience and clean energy — aims to reduce U.S. greenhouse gas emissions by about 50 percent below 2005 levels by 2030, the nation’s current Paris Agreement pledge. With prospects of passing a standalone climate package in the Senate far from assured, is there another pathway to fulfilling that pledge?

    Recent detailed legal analysis shows that there is at least one viable option for the United States to achieve the 2030 target without legislative action. Under Section 115 on International Air Pollution of the Clean Air Act, the U.S. Environmental Protection Agency (EPA) could assign emissions targets to the states that collectively meet the national goal. The president could simply issue an executive order to empower the EPA to do just that. But would that be prudent?

    A new study led by researchers at the MIT Joint Program on the Science and Policy of Global Change explores how, under a federally coordinated carbon dioxide emissions cap-and-trade program aligned with the U.S. Paris Agreement pledge and implemented through Section 115 of the Clean Air Act, the EPA might allocate emissions cuts among states. Recognizing that the Biden or any future administration considering this strategy would need to carefully weigh its benefits against its potential political risks, the study highlights the policy’s net economic benefits to the nation.

    The researchers calculate those net benefits by combining the estimated total cost of carbon dioxide emissions reduction under the policy with the corresponding estimated expenditures that would be avoided as a result of the policy’s implementation — expenditures on health care due to particulate air pollution, and on society at large due to climate impacts.

    Assessing three carbon dioxide emissions allocation strategies (each with legal precedent) for implementing Section 115 to return cap-and-trade program revenue to the states and distribute it to state residents on an equal per-capita basis, the study finds that at the national level, the economic net benefits are substantial, ranging from $70 to $150 billion in 2030. The results appear in the journal Environmental Research Letters.

    “Our findings not only show significant net gains to the U.S. economy under a national emissions policy implemented through the Clean Air Act’s Section 115,” says Mei Yuan, a research scientist at the MIT Joint Program and lead author of the study. “They also show the policy impact on consumer costs may differ across states depending on the choice of allocation strategy.”

    The national price on carbon needed to achieve the policy’s emissions target, as well as the policy’s ultimate cost to consumers, are substantially lower than those found in studies a decade earlier, although in line with other recent studies. The researchers speculate that this is largely due to ongoing expansion of ambitious state policies in the electricity sector and declining renewable energy costs. The policy is also progressive, consistent with earlier studies, in that equal lump-sum distribution of allowance revenue to state residents generally leads to net benefits to lower-income households. Regional disparities in consumer costs can be moderated by the allocation of allowances among states.

    State-by-state emissions estimates for the study are derived from MIT’s U.S. Regional Energy Policy model, with electricity sector detail of the Renewable Energy Development System model developed by the U.S. National Renewable Energy Laboratory; air quality benefits are estimated using U.S. EPA and other models; and the climate benefits estimate is based on the social cost of carbon, the U.S. federal government’s assessment of the economic damages that would result from emitting one additional ton of carbon dioxide into the atmosphere (currently $51/ton, adjusted for inflation). 

    “In addition to illustrating the economic, health, and climate benefits of a Section 115 implementation, our study underscores the advantages of a policy that imposes a uniform carbon price across all economic sectors,” says John Reilly, former co-director of the MIT Joint Program and a study co-author. “A national carbon price would serve as a major incentive for all sectors to decarbonize.” More

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    Machine learning, harnessed to extreme computing, aids fusion energy development

    MIT research scientists Pablo Rodriguez-Fernandez and Nathan Howard have just completed one of the most demanding calculations in fusion science — predicting the temperature and density profiles of a magnetically confined plasma via first-principles simulation of plasma turbulence. Solving this problem by brute force is beyond the capabilities of even the most advanced supercomputers. Instead, the researchers used an optimization methodology developed for machine learning to dramatically reduce the CPU time required while maintaining the accuracy of the solution.

    Fusion energyFusion offers the promise of unlimited, carbon-free energy through the same physical process that powers the sun and the stars. It requires heating the fuel to temperatures above 100 million degrees, well above the point where the electrons are stripped from their atoms, creating a form of matter called plasma. On Earth, researchers use strong magnetic fields to isolate and insulate the hot plasma from ordinary matter. The stronger the magnetic field, the better the quality of the insulation that it provides.

    Rodriguez-Fernandez and Howard have focused on predicting the performance expected in the SPARC device, a compact, high-magnetic-field fusion experiment, currently under construction by the MIT spin-out company Commonwealth Fusion Systems (CFS) and researchers from MIT’s Plasma Science and Fusion Center. While the calculation required an extraordinary amount of computer time, over 8 million CPU-hours, what was remarkable was not how much time was used, but how little, given the daunting computational challenge.

    The computational challenge of fusion energyTurbulence, which is the mechanism for most of the heat loss in a confined plasma, is one of the science’s grand challenges and the greatest problem remaining in classical physics. The equations that govern fusion plasmas are well known, but analytic solutions are not possible in the regimes of interest, where nonlinearities are important and solutions encompass an enormous range of spatial and temporal scales. Scientists resort to solving the equations by numerical simulation on computers. It is no accident that fusion researchers have been pioneers in computational physics for the last 50 years.

    One of the fundamental problems for researchers is reliably predicting plasma temperature and density given only the magnetic field configuration and the externally applied input power. In confinement devices like SPARC, the external power and the heat input from the fusion process are lost through turbulence in the plasma. The turbulence itself is driven by the difference in the extremely high temperature of the plasma core and the relatively cool temperatures of the plasma edge (merely a few million degrees). Predicting the performance of a self-heated fusion plasma therefore requires a calculation of the power balance between the fusion power input and the losses due to turbulence.

    These calculations generally start by assuming plasma temperature and density profiles at a particular location, then computing the heat transported locally by turbulence. However, a useful prediction requires a self-consistent calculation of the profiles across the entire plasma, which includes both the heat input and turbulent losses. Directly solving this problem is beyond the capabilities of any existing computer, so researchers have developed an approach that stitches the profiles together from a series of demanding but tractable local calculations. This method works, but since the heat and particle fluxes depend on multiple parameters, the calculations can be very slow to converge.

    However, techniques emerging from the field of machine learning are well suited to optimize just such a calculation. Starting with a set of computationally intensive local calculations run with the full-physics, first-principles CGYRO code (provided by a team from General Atomics led by Jeff Candy) Rodriguez-Fernandez and Howard fit a surrogate mathematical model, which was used to explore and optimize a search within the parameter space. The results of the optimization were compared to the exact calculations at each optimum point, and the system was iterated to a desired level of accuracy. The researchers estimate that the technique reduced the number of runs of the CGYRO code by a factor of four.

    New approach increases confidence in predictionsThis work, described in a recent publication in the journal Nuclear Fusion, is the highest fidelity calculation ever made of the core of a fusion plasma. It refines and confirms predictions made with less demanding models. Professor Jonathan Citrin, of the Eindhoven University of Technology and leader of the fusion modeling group for DIFFER, the Dutch Institute for Fundamental Energy Research, commented: “The work significantly accelerates our capabilities in more routinely performing ultra-high-fidelity tokamak scenario prediction. This algorithm can help provide the ultimate validation test of machine design or scenario optimization carried out with faster, more reduced modeling, greatly increasing our confidence in the outcomes.” 

    In addition to increasing confidence in the fusion performance of the SPARC experiment, this technique provides a roadmap to check and calibrate reduced physics models, which run with a small fraction of the computational power. Such models, cross-checked against the results generated from turbulence simulations, will provide a reliable prediction before each SPARC discharge, helping to guide experimental campaigns and improving the scientific exploitation of the device. It can also be used to tweak and improve even simple data-driven models, which run extremely quickly, allowing researchers to sift through enormous parameter ranges to narrow down possible experiments or possible future machines.

    The research was funded by CFS, with computational support from the National Energy Research Scientific Computing Center, a U.S. Department of Energy Office of Science User Facility. More

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    What choices does the world need to make to keep global warming below 2 C?

    When the 2015 Paris Agreement set a long-term goal of keeping global warming “well below 2 degrees Celsius, compared to pre-industrial levels” to avoid the worst impacts of climate change, it did not specify how its nearly 200 signatory nations could collectively achieve that goal. Each nation was left to its own devices to reduce greenhouse gas emissions in alignment with the 2 C target. Now a new modeling strategy developed at the MIT Joint Program on the Science and Policy of Global Change that explores hundreds of potential future development pathways provides new insights on the energy and technology choices needed for the world to meet that target.

    Described in a study appearing in the journal Earth’s Future, the new strategy combines two well-known computer modeling techniques to scope out the energy and technology choices needed over the coming decades to reduce emissions sufficiently to achieve the Paris goal.

    The first technique, Monte Carlo analysis, quantifies uncertainty levels for dozens of energy and economic indicators including fossil fuel availability, advanced energy technology costs, and population and economic growth; feeds that information into a multi-region, multi-economic-sector model of the world economy that captures the cross-sectoral impacts of energy transitions; and runs that model hundreds of times to estimate the likelihood of different outcomes. The MIT study focuses on projections through the year 2100 of economic growth and emissions for different sectors of the global economy, as well as energy and technology use.

    The second technique, scenario discovery, uses machine learning tools to screen databases of model simulations in order to identify outcomes of interest and their conditions for occurring. The MIT study applies these tools in a unique way by combining them with the Monte Carlo analysis to explore how different outcomes are related to one another (e.g., do low-emission outcomes necessarily involve large shares of renewable electricity?). This approach can also identify individual scenarios, out of the hundreds explored, that result in specific combinations of outcomes of interest (e.g., scenarios with low emissions, high GDP growth, and limited impact on electricity prices), and also provide insight into the conditions needed for that combination of outcomes.

    Using this unique approach, the MIT Joint Program researchers find several possible patterns of energy and technology development under a specified long-term climate target or economic outcome.

    “This approach shows that there are many pathways to a successful energy transition that can be a win-win for the environment and economy,” says Jennifer Morris, an MIT Joint Program research scientist and the study’s lead author. “Toward that end, it can be used to guide decision-makers in government and industry to make sound energy and technology choices and avoid biases in perceptions of what ’needs’ to happen to achieve certain outcomes.”

    For example, while achieving the 2 C goal, the global level of combined wind and solar electricity generation by 2050 could be less than three times or more than 12 times the current level (which is just over 2,000 terawatt hours). These are very different energy pathways, but both can be consistent with the 2 C goal. Similarly, there are many different energy mixes that can be consistent with maintaining high GDP growth in the United States while also achieving the 2 C goal, with different possible roles for renewables, natural gas, carbon capture and storage, and bioenergy. The study finds renewables to be the most robust electricity investment option, with sizable growth projected under each of the long-term temperature targets explored.

    The researchers also find that long-term climate targets have little impact on economic output for most economic sectors through 2050, but do require each sector to significantly accelerate reduction of its greenhouse gas emissions intensity (emissions per unit of economic output) so as to reach near-zero levels by midcentury.

    “Given the range of development pathways that can be consistent with meeting a 2 degrees C goal, policies that target only specific sectors or technologies can unnecessarily narrow the solution space, leading to higher costs,” says former MIT Joint Program Co-Director John Reilly, a co-author of the study. “Our findings suggest that policies designed to encourage a portfolio of technologies and sectoral actions can be a wise strategy that hedges against risks.”

    The research was supported by the U.S. Department of Energy Office of Science. More

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    Engineers enlist AI to help scale up advanced solar cell manufacturing

    Perovskites are a family of materials that are currently the leading contender to potentially replace today’s silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle.

    Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.

    The system, developed by researchers at MIT and Stanford University over the last few years, makes it possible to integrate data from prior experiments, and information based on personal observations by experienced workers, into the machine learning process. This makes the outcomes more accurate and has already led to the manufacturing of perovskite cells with an energy conversion efficiency of 18.5 percent, a competitive level for today’s market.

    The research is reported today in the journal Joule, in a paper by MIT professor of mechanical engineering Tonio Buonassisi, Stanford professor of materials science and engineering Reinhold Dauskardt, recent MIT research assistant Zhe Liu, Stanford doctoral graduate Nicholas Rolston, and three others.

    Perovskites are a group of layered crystalline compounds defined by the configuration of the atoms in their crystal lattice. There are thousands of such possible compounds and many different ways of making them. While most lab-scale development of perovskite materials uses a spin-coating technique, that’s not practical for larger-scale manufacturing, so companies and labs around the world have been searching for ways of translating these lab materials into a practical, manufacturable product.

    “There’s always a big challenge when you’re trying to take a lab-scale process and then transfer it to something like a startup or a manufacturing line,” says Rolston, who is now an assistant professor at Arizona State University. The team looked at a process that they felt had the greatest potential, a method called rapid spray plasma processing, or RSPP.

    The manufacturing process would involve a moving roll-to-roll surface, or series of sheets, on which the precursor solutions for the perovskite compound would be sprayed or ink-jetted as the sheet rolled by. The material would then move on to a curing stage, providing a rapid and continuous output “with throughputs that are higher than for any other photovoltaic technology,” Rolston says.

    “The real breakthrough with this platform is that it would allow us to scale in a way that no other material has allowed us to do,” he adds. “Even materials like silicon require a much longer timeframe because of the processing that’s done. Whereas you can think of [this approach as more] like spray painting.”

    Within that process, at least a dozen variables may affect the outcome, some of them more controllable than others. These include the composition of the starting materials, the temperature, the humidity, the speed of the processing path, the distance of the nozzle used to spray the material onto a substrate, and the methods of curing the material. Many of these factors can interact with each other, and if the process is in open air, then humidity, for example, may be uncontrolled. Evaluating all possible combinations of these variables through experimentation is impossible, so machine learning was needed to help guide the experimental process.

    But while most machine-learning systems use raw data such as measurements of the electrical and other properties of test samples, they don’t typically incorporate human experience such as qualitative observations made by the experimenters of the visual and other properties of the test samples, or information from other experiments reported by other researchers. So, the team found a way to incorporate such outside information into the machine learning model, using a probability factor based on a mathematical technique called Bayesian Optimization.

    Using the system, he says, “having a model that comes from experimental data, we can find out trends that we weren’t able to see before.” For example, they initially had trouble adjusting for uncontrolled variations in humidity in their ambient setting. But the model showed them “that we could overcome our humidity challenges by changing the temperature, for instance, and by changing some of the other knobs.”

    The system now allows experimenters to much more rapidly guide their process in order to optimize it for a given set of conditions or required outcomes. In their experiments, the team focused on optimizing the power output, but the system could also be used to simultaneously incorporate other criteria, such as cost and durability — something members of the team are continuing to work on, Buonassisi says.

    The researchers were encouraged by the Department of Energy, which sponsored the work, to commercialize the technology, and they’re currently focusing on tech transfer to existing perovskite manufacturers. “We are reaching out to companies now,” Buonassisi says, and the code they developed has been made freely available through an open-source server. “It’s now on GitHub, anyone can download it, anyone can run it,” he says. “We’re happy to help companies get started in using our code.”

    Already, several companies are gearing up to produce perovskite-based solar panels, even though they are still working out the details of how to produce them, says Liu, who is now at the Northwestern Polytechnical University in Xi’an, China. He says companies there are not yet doing large-scale manufacturing, but instead starting with smaller, high-value applications such as building-integrated solar tiles where appearance is important. Three of these companies “are on track or are being pushed by investors to manufacture 1 meter by 2-meter rectangular modules [comparable to today’s most common solar panels], within two years,” he says.

    ‘The problem is, they don’t have a consensus on what manufacturing technology to use,” Liu says. The RSPP method, developed at Stanford, “still has a good chance” to be competitive, he says. And the machine learning system the team developed could prove to be important in guiding the optimization of whatever process ends up being used.

    “The primary goal was to accelerate the process, so it required less time, less experiments, and less human hours to develop something that is usable right away, for free, for industry,” he says.

    “Existing work on machine-learning-driven perovskite PV fabrication largely focuses on spin-coating, a lab-scale technique,” says Ted Sargent, University Professor at the University of Toronto, who was not associated with this work, which he says demonstrates “a workflow that is readily adapted to the deposition techniques that dominate the thin-film industry. Only a handful of groups have the simultaneous expertise in engineering and computation to drive such advances.” Sargent adds that this approach “could be an exciting advance for the manufacture of a broader family of materials” including LEDs, other PV technologies, and graphene, “in short, any industry that uses some form of vapor or vacuum deposition.” 

    The team also included Austin Flick and Thomas Colburn at Stanford and Zekun Ren at the Singapore-MIT Alliance for Science and Technology (SMART). In addition to the Department of Energy, the work was supported by a fellowship from the MIT Energy Initiative, the Graduate Research Fellowship Program from the National Science Foundation, and the SMART program. More

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    New England renewables + Canadian hydropower

    The urgent need to cut carbon emissions has prompted a growing number of U.S. states to commit to achieving 100 percent clean electricity by 2040 or 2050. But figuring out how to meet those commitments and still have a reliable and affordable power system is a challenge. Wind and solar installations will form the backbone of a carbon-free power system, but what technologies can meet electricity demand when those intermittent renewable sources are not adequate?

    In general, the options being discussed include nuclear power, natural gas with carbon capture and storage (CCS), and energy storage technologies such as new and improved batteries and chemical storage in the form of hydrogen. But in the northeastern United States, there is one more possibility being proposed: electricity imported from hydropower plants in the neighboring Canadian province of Quebec.

    The proposition makes sense. Those plants can produce as much electricity as about 40 large nuclear power plants, and some power generated in Quebec already comes to the Northeast. So, there could be abundant additional supply to fill any shortfall when New England’s intermittent renewables underproduce. However, U.S. wind and solar investors view Canadian hydropower as a competitor and argue that reliance on foreign supply discourages further U.S. investment.

    Two years ago, three researchers affiliated with the MIT Center for Energy and Environmental Policy Research (CEEPR) — Emil Dimanchev SM ’18, now a PhD candidate at the Norwegian University of Science and Technology; Joshua Hodge, CEEPR’s executive director; and John Parsons, a senior lecturer in the MIT Sloan School of Management — began wondering whether viewing Canadian hydro as another source of electricity might be too narrow. “Hydropower is a more-than-hundred-year-old technology, and plants are already built up north,” says Dimanchev. “We might not need to build something new. We might just need to use those plants differently or to a greater extent.”

    So the researchers decided to examine the potential role and economic value of Quebec’s hydropower resource in a future low-carbon system in New England. Their goal was to help inform policymakers, utility decision-makers, and others about how best to incorporate Canadian hydropower into their plans and to determine how much time and money New England should spend to integrate more hydropower into its system. What they found out was surprising, even to them.

    The analytical methods

    To explore possible roles for Canadian hydropower to play in New England’s power system, the MIT researchers first needed to predict how the regional power system might look in 2050 — both the resources in place and how they would be operated, given any policy constraints. To perform that analysis, they used GenX, a modeling tool originally developed by Jesse Jenkins SM ’14, PhD ’18 and Nestor Sepulveda SM ’16, PhD ’20 while they were researchers at the MIT Energy Initiative (MITEI).

    The GenX model is designed to support decision-making related to power system investment and real-time operation and to examine the impacts of possible policy initiatives on those decisions. Given information on current and future technologies — different kinds of power plants, energy storage technologies, and so on — GenX calculates the combination of equipment and operating conditions that can meet a defined future demand at the lowest cost. The GenX modeling tool can also incorporate specified policy constraints, such as limits on carbon emissions.

    For their study, Dimanchev, Hodge, and Parsons set parameters in the GenX model using data and assumptions derived from a variety of sources to build a representation of the interconnected power systems in New England, New York, and Quebec. (They included New York to account for that state’s existing demand on the Canadian hydro resources.) For data on the available hydropower, they turned to Hydro-Québec, the public utility that owns and operates most of the hydropower plants in Quebec.

    It’s standard in such analyses to include real-world engineering constraints on equipment, such as how quickly certain power plants can be ramped up and down. With help from Hydro-Québec, the researchers also put hour-to-hour operating constraints on the hydropower resource.

    Most of Hydro-Québec’s plants are “reservoir hydropower” systems. In them, when power isn’t needed, the flow on a river is restrained by a dam downstream of a reservoir, and the reservoir fills up. When power is needed, the dam is opened, and the water in the reservoir runs through downstream pipes, turning turbines and generating electricity. Proper management of such a system requires adhering to certain operating constraints. For example, to prevent flooding, reservoirs must not be allowed to overfill — especially prior to spring snowmelt. And generation can’t be increased too quickly because a sudden flood of water could erode the river edges or disrupt fishing or water quality.

    Based on projections from the National Renewable Energy Laboratory and elsewhere, the researchers specified electricity demand for every hour of the year 2050, and the model calculated the cost-optimal mix of technologies and system operating regime that would satisfy that hourly demand, including the dispatch of the Hydro-Québec hydropower system. In addition, the model determined how electricity would be traded among New England, New York, and Quebec.

    Effects of decarbonization limits on technology mix and electricity trading

    To examine the impact of the emissions-reduction mandates in the New England states, the researchers ran the model assuming reductions in carbon emissions between 80 percent and 100 percent relative to 1990 levels. The results of those runs show that, as emissions limits get more stringent, New England uses more wind and solar and extends the lifetime of its existing nuclear plants. To balance the intermittency of the renewables, the region uses natural gas plants, demand-side management, battery storage (modeled as lithium-ion batteries), and trading with Quebec’s hydropower-based system. Meanwhile, the optimal mix in Quebec is mostly composed of existing hydro generation. Some solar is added, but new reservoirs are built only if renewable costs are assumed to be very high.

    The most significant — and perhaps surprising — outcome is that in all the scenarios, the hydropower-based system of Quebec is not only an exporter but also an importer of electricity, with the direction of flow on the Quebec-New England transmission lines changing over time.

    Historically, energy has always flowed from Quebec to New England. The model results for 2018 show electricity flowing from north to south, with the quantity capped by the current transmission capacity limit of 2,225 megawatts (MW).

    An analysis for 2050, assuming that New England decarbonizes 90 percent and the capacity of the transmission lines remains the same, finds electricity flows going both ways. Flows from north to south still dominate. But for nearly 3,500 of the 8,760 hours of the year, electricity flows in the opposite direction — from New England to Quebec. And for more than 2,200 of those hours, the flow going north is at the maximum the transmission lines can carry.

    The direction of flow is motivated by economics. When renewable generation is abundant in New England, prices are low, and it’s cheaper for Quebec to import electricity from New England and conserve water in its reservoirs. Conversely, when New England’s renewables are scarce and prices are high, New England imports hydro-generated electricity from Quebec.

    So rather than delivering electricity, Canadian hydro provides a means of storing the electricity generated by the intermittent renewables in New England.

    “We see this in our modeling because when we tell the model to meet electricity demand using these resources, the model decides that it is cost-optimal to use the reservoirs to store energy rather than anything else,” says Dimanchev. “We should be sending the energy back and forth, so the reservoirs in Quebec are in essence a battery that we use to store some of the electricity produced by our intermittent renewables and discharge it when we need it.”

    Given that outcome, the researchers decided to explore the impact of expanding the transmission capacity between New England and Quebec. Building transmission lines is always contentious, but what would be the impact if it could be done?

    Their model results shows that when transmission capacity is increased from 2,225 MW to 6,225 MW, flows in both directions are greater, and in both cases the flow is at the new maximum for more than 1,000 hours.

    Results of the analysis thus confirm that the economic response to expanded transmission capacity is more two-way trading. To continue the battery analogy, more transmission capacity to and from Quebec effectively increases the rate at which the battery can be charged and discharged.

    Effects of two-way trading on the energy mix

    What impact would the advent of two-way trading have on the mix of energy-generating sources in New England and Quebec in 2050?

    Assuming current transmission capacity, in New England, the change from one-way to two-way trading increases both wind and solar power generation and to a lesser extent nuclear; it also decreases the use of natural gas with CCS. The hydro reservoirs in Canada can provide long-duration storage — over weeks, months, and even seasons — so there is less need for natural gas with CCS to cover any gaps in supply. The level of imports is slightly lower, but now there are also exports. Meanwhile, in Quebec, two-way trading reduces solar power generation, and the use of wind disappears. Exports are roughly the same, but now there are imports as well. Thus, two-way trading reallocates renewables from Quebec to New England, where it’s more economical to install and operate solar and wind systems.

    Another analysis examined the impact on the energy mix of assuming two-way trading plus expanded transmission capacity. For New England, greater transmission capacity allows wind, solar, and nuclear to expand further; natural gas with CCS all but disappears; and both imports and exports increase significantly. In Quebec, solar decreases still further, and both exports and imports of electricity increase.

    Those results assume that the New England power system decarbonizes by 99 percent in 2050 relative to 1990 levels. But at 90 percent and even 80 percent decarbonization levels, the model concludes that natural gas capacity decreases with the addition of new transmission relative to the current transmission scenario. Existing plants are retired, and new plants are not built as they are no longer economically justified. Since natural gas plants are the only source of carbon emissions in the 2050 energy system, the researchers conclude that the greater access to hydro reservoirs made possible by expanded transmission would accelerate the decarbonization of the electricity system.

    Effects of transmission changes on costs

    The researchers also explored how two-way trading with expanded transmission capacity would affect costs in New England and Quebec, assuming 99 percent decarbonization in New England. New England’s savings on fixed costs (investments in new equipment) are largely due to a decreased need to invest in more natural gas with CCS, and its savings on variable costs (operating costs) are due to a reduced need to run those plants. Quebec’s savings on fixed costs come from a reduced need to invest in solar generation. The increase in cost — borne by New England — reflects the construction and operation of the increased transmission capacity. The net benefit for the region is substantial.

    Thus, the analysis shows that everyone wins as transmission capacity increases — and the benefit grows as the decarbonization target tightens. At 99 percent decarbonization, the overall New England-Quebec region pays about $21 per megawatt-hour (MWh) of electricity with today’s transmission capacity but only $18/MWh with expanded transmission. Assuming 100 percent reduction in carbon emissions, the region pays $29/MWh with current transmission capacity and only $22/MWh with expanded transmission.

    Addressing misconceptions

    These results shed light on several misconceptions that policymakers, supporters of renewable energy, and others tend to have.

    The first misconception is that the New England renewables and Canadian hydropower are competitors. The modeling results instead show that they’re complementary. When the power systems in New England and Quebec work together as an integrated system, the Canadian reservoirs are used part of the time to store the renewable electricity. And with more access to hydropower storage in Quebec, there’s generally more renewable investment in New England.

    The second misconception arises when policymakers refer to Canadian hydro as a “baseload resource,” which implies a dependable source of electricity — particularly one that supplies power all the time. “Our study shows that by viewing Canadian hydropower as a baseload source of electricity — or indeed a source of electricity at all — you’re not taking full advantage of what that resource can provide,” says Dimanchev. “What we show is that Quebec’s reservoir hydro can provide storage, specifically for wind and solar. It’s a solution to the intermittency problem that we foresee in carbon-free power systems for 2050.”

    While the MIT analysis focuses on New England and Quebec, the researchers believe that their results may have wider implications. As power systems in many regions expand production of renewables, the value of storage grows. Some hydropower systems have storage capacity that has not yet been fully utilized and could be a good complement to renewable generation. Taking advantage of that capacity can lower the cost of deep decarbonization and help move some regions toward a decarbonized supply of electricity.

    This research was funded by the MIT Center for Energy and Environmental Policy Research, which is supported in part by a consortium of industry and government associates.

    This article appears in the Autumn 2021 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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    Finding the questions that guide MIT fusion research

    “One of the things I learned was, doing good science isn’t so much about finding the answers as figuring out what the important questions are.”

    As Martin Greenwald retires from the responsibilities of senior scientist and deputy director of the MIT Plasma Science and Fusion Center (PSFC), he reflects on his almost 50 years of science study, 43 of them as a researcher at MIT, pursuing the question of how to make the carbon-free energy of fusion a reality.

    Most of Greenwald’s important questions about fusion began after graduating from MIT with a BS in both physics and chemistry. Beginning graduate work at the University of California at Berkeley, he felt compelled to learn more about fusion as an energy source that could have “a real societal impact.” At the time, researchers were exploring new ideas for devices that could create and confine fusion plasmas. Greenwald worked on Berkeley’s “alternate concept” TORMAC, a Toroidal Magnetic Cusp. “It didn’t work out very well,” he laughs. “The first thing I was known for was making the measurements that shut down the program.”

    Believing the temperature of the plasma generated by the device would not be as high as his group leader expected, Greenwald developed hardware that could measure the low temperatures predicted by his own “back of the envelope calculations.” As he anticipated, his measurements showed that “this was not a fusion plasma; this was hardly a confined plasma at all.”

    With a PhD from Berkeley, Greenwald returned to MIT for a research position at the PSFC, attracted by the center’s “esprit de corps.”

    He arrived in time to participate in the final experiments on Alcator A, the first in a series of tokamaks built at MIT, all characterized by compact size and featuring high-field magnets. The tokamak design was then becoming favored as the most effective route to fusion: its doughnut-shaped vacuum chamber, surrounded by electromagnets, could confine the turbulent plasma long enough, while increasing its heat and density, to make fusion occur.

    Alcator A showed that the energy confinement time improves in relation to increasing plasma density. MIT’s succeeding device, Alcator C, was designed to use higher magnetic fields, boosting expectations that it would reach higher densities and better confinement. To attain these goals, however, Greenwald had to pursue a new technique that increased density by injecting pellets of frozen fuel into the plasma, a method he likens to throwing “snowballs in hell.” This work was notable for the creation of a new regime of enhanced plasma confinement on Alcator C. In those experiments, a confined plasma surpassed for the first time one of the two Lawson criteria — the minimum required value for the product of the plasma density and confinement time — for making net power from fusion. This had been a milestone for fusion research since their publication by John Lawson in 1957.

    Greenwald continued to make a name for himself as part of a larger study into the physics of the Compact Ignition Tokamak — a high-field burning plasma experiment that the U.S. program was proposing to build in the late 1980s. The result, unexpectedly, was a new scaling law, later known as the “Greenwald Density Limit,” and a new theory for the mechanism of the limit. It has been used to accurately predict performance on much larger machines built since.

    The center’s next tokamak, Alcator C-Mod, started operation in 1993 and ran for more than 20 years, with Greenwald as the chair of its Experimental Program Committee. Larger than Alcator C, the new device supported a highly shaped plasma, strong radiofrequency heating, and an all-metal plasma-facing first wall. All of these would eventually be required in a fusion power system.

    C-Mod proved to be MIT’s most enduring fusion experiment to date, producing important results for 20 years. During that time Greenwald contributed not only to the experiments, but to mentoring the next generation. Research scientist Ryan Sweeney notes that “Martin quickly gained my trust as a mentor, in part due to his often casual dress and slightly untamed hair, which are embodiments of his transparency and his focus on what matters. He can quiet a room of PhDs and demand attention not by intimidation, but rather by his calmness and his ability to bring clarity to complicated problems, be they scientific or human in nature.”

    Greenwald worked closely with the group of students who, in PSFC Director Dennis Whyte’s class, came up with the tokamak concept that evolved into SPARC. MIT is now pursuing this compact, high-field tokamak with Commonwealth Fusion Systems, a startup that grew out of the collective enthusiasm for this concept, and the growing realization it could work. Greenwald now heads the Physics Group for the SPARC project at MIT. He has helped confirm the device’s physics basis in order to predict performance and guide engineering decisions.

    “Martin’s multifaceted talents are thoroughly embodied by, and imprinted on, SPARC” says Whyte. “First, his leadership in its plasma confinement physics validation and publication place SPARC on a firm scientific footing. Secondly, the impact of the density limit he discovered, which shows that fuel density increases with magnetic field and decreasing the size of the tokamak, is critical in obtaining high fusion power density not just in SPARC, but in future power plants. Third, and perhaps most impressive, is Martin’s mentorship of the SPARC generation of leadership.”

    Greenwald’s expertise and easygoing personality have made him an asset as head of the PSFC Office for Computer Services and group leader for data acquisition and computing, and sought for many professional committees. He has been an APS Fellow since 2000, and was an APS Distinguished Lecturer in Plasma Physics (2001-02). He was also presented in 2014 with a Leadership Award from Fusion Power Associates. He is currently an associate editor for Physics of Plasmas and a member of the Lawrence Livermore National Laboratory Physical Sciences Directorate External Review Committee.

    Although leaving his full-time responsibilities, Greenwald will remain at MIT as a visiting scientist, a role he says will allow him to “stick my nose into everything without being responsible for anything.”

    “At some point in the race you have to hand off the baton,“ he says. “And it doesn’t mean you’re not interested in the outcome; and it doesn’t mean you’re just going to walk away into the stands. I want to be there at the end when we succeed.” More