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      Team creates map for production of eco-friendly metals

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      In work that could usher in more efficient, eco-friendly processes for producing important metals like lithium, iron, and cobalt, researchers from MIT and the SLAC National Accelerator Laboratory have mapped what is happening at the atomic level behind a particularly promising approach called metal electrolysis.

      By creating maps for a wide range of metals, they not only determined which metals should be easiest to produce using this approach, but also identified fundamental barriers behind the efficient production of others. As a result, the researchers’ map could become an important design tool for optimizing the production of all these metals.

      The work could also aid the development of metal-air batteries, cousins of the lithium-ion batteries used in today’s electric vehicles.

      Most of the metals key to society today are produced using fossil fuels. These fuels generate the high temperatures necessary to convert the original ore into its purified metal. But that process is a significant source of greenhouse gases — steel alone accounts for some 7 percent of carbon dioxide emissions globally. As a result, researchers from around the world are working to identify more eco-friendly ways for the production of metals.

      One promising approach is metal electrolysis, in which a metal oxide, the ore, is zapped with electricity to create pure metal with oxygen as the byproduct. That is the reaction explored at the atomic level in new research reported in the April 8 issue of the journal Chemistry of Materials.

      Donald Siegel is department chair and professor of mechanical engineering at the University of Texas at Austin. Says Siegel, who was not involved in the Chemistry of Materials study: “This work is an important contribution to improving the efficiency of metal production from metal oxides. It clarifies our understanding of low-carbon electrolysis processes by tracing the underlying thermodynamics back to elementary metal-oxygen interactions. I expect that this work will aid in the creation of design rules that will make these industrially important processes less reliant on fossil fuels.”

      Yang Shao-Horn, the JR East Professor of Engineering in MIT’s Department of Materials Science and Engineering (DMSE) and Department of Mechanical Engineering, is a leader of the current work, with Michal Bajdich of SLAC.

      “Here we aim to establish some basic understanding to predict the efficiency of electrochemical metal production and metal-air batteries from examining computed thermodynamic barriers for the conversion between metal and metal oxides,” says Shao-Horn, who is on the research team for MIT’s new Center for Electrification and Decarbonization of Industry, a winner of the Institute’s first-ever Climate Grand Challenges competition. Shao-Horn is also affiliated with MIT’s Materials Research Laboratory and Research Laboratory of Electronics.

      In addition to Shao-Horn and Bajdich, other authors of the Chemistry of Materials paper are Jaclyn R. Lunger, first author and a DMSE graduate student; mechanical engineering senior Naomi Lutz; and DMSE graduate student Jiayu Peng.

      Other applications

      The work could also aid in developing metal-air batteries such as lithium-air, aluminum-air, and zinc-air batteries. These cousins of the lithium-ion batteries used in today’s electric vehicles have the potential to electrify aviation because their energy densities are much higher. However, they are not yet on the market due to a variety of problems including inefficiency.

      Charging metal-air batteries also involves electrolysis. As a result, the new atomic-level understanding of these reactions could not only help engineers develop efficient electrochemical routes for metal production, but also design more efficient metal-air batteries.

      Learning from water splitting

      Electrolysis is also used to split water into oxygen and hydrogen, which stores the resulting energy. That hydrogen, in turn, could become an eco-friendly alternative to fossil fuels. Since much more is known about water electrolysis, the focus of Bajdich’s work at SLAC, than the electrolysis of metal oxides, the team compared the two processes for the first time.

      The result: “Slowly, we uncovered the elementary steps involved in metal electrolysis,” says Bajdich. The work was challenging, says Lunger, because “it was unclear to us what those steps are. We had to figure out how to get from A to B,” or from a metal oxide to metal and oxygen.

      All of the work was conducted with supercomputer simulations. “It’s like a sandbox of atoms, and then we play with them. It’s a little like Legos,” says Bajdich. More specifically, the team explored different scenarios for the electrolysis of several metals. Each involved different catalysts, molecules that boost the speed of a reaction.

      Says Lunger, “To optimize the reaction, you want to find the catalyst that makes it most efficient.” The team’s map is essentially a guide for designing the best catalysts for each different metal.

      What’s next? Lunger noted that the current work focused on the electrolysis of pure metals. “I’m interested in seeing what happens in more complex systems involving multiple metals. Can you make the reaction more efficient if there’s sodium and lithium present, or cadmium and cesium?”

      This work was supported by a U.S. Department of Energy Office of Science Graduate Student Research award. It was also supported by an MIT Energy Initiative fellowship, the Toyota Research Institute through the Accelerated Materials Design and Discovery Program, the Catalysis Science Program of Department of Energy, Office of Basic Energy Sciences, and by the Differentiate Program through the U.S. Advanced Research Projects Agency — Energy.  More

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

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      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

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      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?

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      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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      New England renewables + Canadian hydropower

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      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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      Ocean vital signs

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      Without the ocean, the climate crisis would be even worse than it is. Each year, the ocean absorbs billions of tons of carbon from the atmosphere, preventing warming that greenhouse gas would otherwise cause. Scientists estimate about 25 to 30 percent of all carbon released into the atmosphere by both human and natural sources is absorbed by the ocean.

      “But there’s a lot of uncertainty in that number,” says Ryan Woosley, a marine chemist and a principal research scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at MIT. Different parts of the ocean take in different amounts of carbon depending on many factors, such as the season and the amount of mixing from storms. Current models of the carbon cycle don’t adequately capture this variation.

      To close the gap, Woosley and a team of other MIT scientists developed a research proposal for the MIT Climate Grand Challenges competition — an Institute-wide campaign to catalyze and fund innovative research addressing the climate crisis. The team’s proposal, “Ocean Vital Signs,” involves sending a fleet of sailing drones to cruise the oceans taking detailed measurements of how much carbon the ocean is really absorbing. Those data would be used to improve the precision of global carbon cycle models and improve researchers’ ability to verify emissions reductions claimed by countries.

      “If we start to enact mitigation strategies—either through removing CO2 from the atmosphere or reducing emissions — we need to know where CO2 is going in order to know how effective they are,” says Woosley. Without more precise models there’s no way to confirm whether observed carbon reductions were thanks to policy and people, or thanks to the ocean.

      “So that’s the trillion-dollar question,” says Woosley. “If countries are spending all this money to reduce emissions, is it enough to matter?”

      In February, the team’s Climate Grand Challenges proposal was named one of 27 finalists out of the almost 100 entries submitted. From among this list of finalists, MIT will announce in April the selection of five flagship projects to receive further funding and support.

      Woosley is leading the team along with Christopher Hill, a principal research engineer in EAPS. The team includes physical and chemical oceanographers, marine microbiologists, biogeochemists, and experts in computational modeling from across the department, in addition to collaborators from the Media Lab and the departments of Mathematics, Aeronautics and Astronautics, and Electrical Engineering and Computer Science.

      Today, data on the flux of carbon dioxide between the air and the oceans are collected in a piecemeal way. Research ships intermittently cruise out to gather data. Some commercial ships are also fitted with sensors. But these present a limited view of the entire ocean, and include biases. For instance, commercial ships usually avoid storms, which can increase the turnover of water exposed to the atmosphere and cause a substantial increase in the amount of carbon absorbed by the ocean.

      “It’s very difficult for us to get to it and measure that,” says Woosley. “But these drones can.”

      If funded, the team’s project would begin by deploying a few drones in a small area to test the technology. The wind-powered drones — made by a California-based company called Saildrone — would autonomously navigate through an area, collecting data on air-sea carbon dioxide flux continuously with solar-powered sensors. This would then scale up to more than 5,000 drone-days’ worth of observations, spread over five years, and in all five ocean basins.

      Those data would be used to feed neural networks to create more precise maps of how much carbon is absorbed by the oceans, shrinking the uncertainties involved in the models. These models would continue to be verified and improved by new data. “The better the models are, the more we can rely on them,” says Woosley. “But we will always need measurements to verify the models.”

      Improved carbon cycle models are relevant beyond climate warming as well. “CO2 is involved in so much of how the world works,” says Woosley. “We’re made of carbon, and all the other organisms and ecosystems are as well. What does the perturbation to the carbon cycle do to these ecosystems?”

      One of the best understood impacts is ocean acidification. Carbon absorbed by the ocean reacts to form an acid. A more acidic ocean can have dire impacts on marine organisms like coral and oysters, whose calcium carbonate shells and skeletons can dissolve in the lower pH. Since the Industrial Revolution, the ocean has become about 30 percent more acidic on average.

      “So while it’s great for us that the oceans have been taking up the CO2, it’s not great for the oceans,” says Woosley. “Knowing how this uptake affects the health of the ocean is important as well.” More

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      in Environment

      Using nature’s structures in wooden buildings

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      Concern about climate change has focused significant attention on the buildings sector, in particular on the extraction and processing of construction materials. The concrete and steel industries together are responsible for as much as 15 percent of global carbon dioxide emissions. In contrast, wood provides a natural form of carbon sequestration, so there’s a move to use timber instead. Indeed, some countries are calling for public buildings to be made at least partly from timber, and large-scale timber buildings have been appearing around the world.

      Observing those trends, Caitlin Mueller ’07, SM ’14, PhD ’14, an associate professor of architecture and of civil and environmental engineering in the Building Technology Program at MIT, sees an opportunity for further sustainability gains. As the timber industry seeks to produce wooden replacements for traditional concrete and steel elements, the focus is on harvesting the straight sections of trees. Irregular sections such as knots and forks are turned into pellets and burned, or ground up to make garden mulch, which will decompose within a few years; both approaches release the carbon trapped in the wood to the atmosphere.

      For the past four years, Mueller and her Digital Structures research group have been developing a strategy for “upcycling” those waste materials by using them in construction — not as cladding or finishes aimed at improving appearance, but as structural components. “The greatest value you can give to a material is to give it a load-bearing role in a structure,” she says. But when builders use virgin materials, those structural components are the most emissions-intensive parts of buildings due to their large volume of high-strength materials. Using upcycled materials in place of those high-carbon systems is therefore especially impactful in reducing emissions.

      Mueller and her team focus on tree forks — that is, spots where the trunk or branch of a tree divides in two, forming a Y-shaped piece. In architectural drawings, there are many similar Y-shaped nodes where straight elements come together. In such cases, those units must be strong enough to support critical loads.

      “Tree forks are naturally engineered structural connections that work as cantilevers in trees, which means that they have the potential to transfer force very efficiently thanks to their internal fiber structure,” says Mueller. “If you take a tree fork and slice it down the middle, you see an unbelievable network of fibers that are intertwining to create these often three-dimensional load transfer points in a tree. We’re starting to do the same thing using 3D printing, but we’re nowhere near what nature does in terms of complex fiber orientation and geometry.”

      She and her team have developed a five-step “design-to-fabrication workflow” that combines natural structures such as tree forks with the digital and computational tools now used in architectural design. While there’s long been a “craft” movement to use natural wood in railings and decorative features, the use of computational tools makes it possible to use wood in structural roles — without excessive cutting, which is costly and may compromise the natural geometry and internal grain structure of the wood.

      Given the wide use of digital tools by today’s architects, Mueller believes that her approach is “at least potentially scalable and potentially achievable within our industrialized materials processing systems.” In addition, by combining tree forks with digital design tools, the novel approach can also support the trend among architects to explore new forms. “Many iconic buildings built in the past two decades have unexpected shapes,” says Mueller. “Tree branches have a very specific geometry that sometimes lends itself to an irregular or nonstandard architectural form — driven not by some arbitrary algorithm but by the material itself.”

      Step 0: Find a source, set goals

      Before starting their design-to-fabrication process, the researchers needed to locate a source of tree forks. Mueller found help in the Urban Forestry Division of the City of Somerville, Massachusetts, which maintains a digital inventory of more than 2,000 street trees — including more than 20 species — and records information about the location, approximate trunk diameter, and condition of each tree.

      With permission from the forestry division, the team was on hand in 2018 when a large group of trees was cut down near the site of the new Somerville High School. Among the heavy equipment on site was a chipper, poised to turn all the waste wood into mulch. Instead, the workers obligingly put the waste wood into the researchers’ truck to be brought to MIT.

      In their project, the MIT team sought not only to upcycle that waste material but also to use it to create a structure that would be valued by the public. “Where I live, the city has had to take down a lot of trees due to damage from an invasive species of beetle,” Mueller explains. “People get really upset — understandably. Trees are an important part of the urban fabric, providing shade and beauty.” She and her team hoped to reduce that animosity by “reinstalling the removed trees in the form of a new functional structure that would recreate the atmosphere and spatial experience previously provided by the felled trees.”

      With their source and goals identified, the researchers were ready to demonstrate the five steps in their design-to-fabrication workflow for making spatial structures using an inventory of tree forks.

      Step 1: Create a digital material library

      The first task was to turn their collection of tree forks into a digital library. They began by cutting off excess material to produce isolated tree forks. They then created a 3D scan of each fork. Mueller notes that as a result of recent progress in photogrammetry (measuring objects using photographs) and 3D scanning, they could create high-resolution digital representations of the individual tree forks with relatively inexpensive equipment, even using apps that run on a typical smartphone.

      In the digital library, each fork is represented by a “skeletonized” version showing three straight bars coming together at a point. The relative geometry and orientation of the branches are of particular interest because they determine the internal fiber orientation that gives the component its strength.

      Step 2: Find the best match between the initial design and the material library

      Like a tree, a typical architectural design is filled with Y-shaped nodes where three straight elements meet up to support a critical load. The goal was therefore to match the tree forks in the material library with the nodes in a sample architectural design.

      First, the researchers developed a “mismatch metric” for quantifying how well the geometries of a particular tree fork aligned with a given design node. “We’re trying to line up the straight elements in the structure with where the branches originally were in the tree,” explains Mueller. “That gives us the optimal orientation for load transfer and maximizes use of the inherent strength of the wood fiber.” The poorer the alignment, the higher the mismatch metric.

      The goal was to get the best overall distribution of all the tree forks among the nodes in the target design. Therefore, the researchers needed to try different fork-to-node distributions and, for each distribution, add up the individual fork-to-node mismatch errors to generate an overall, or global, matching score. The distribution with the best matching score would produce the most structurally efficient use of the total tree fork inventory.

      Since performing that process manually would take far too long to be practical, they turned to the “Hungarian algorithm,” a technique developed in 1955 for solving such problems. “The brilliance of the algorithm is solving that [matching] problem very quickly,” Mueller says. She notes that it’s a very general-use algorithm. “It’s used for things like marriage match-making. It can be used any time you have two collections of things that you’re trying to find unique matches between. So, we definitely didn’t invent the algorithm, but we were the first to identify that it could be used for this problem.”

      The researchers performed repeated tests to show possible distributions of the tree forks in their inventory and found that the matching score improved as the number of forks available in the material library increased — up to a point. In general, the researchers concluded that the mismatch score was lowest, and thus best, when there were about three times as many forks in the material library as there were nodes in the target design.

      Step 3: Balance designer intention with structural performance

      The next step in the process was to incorporate the intention or preference of the designer. To permit that flexibility, each design includes a limited number of critical parameters, such as bar length and bending strain. Using those parameters, the designer can manually change the overall shape, or geometry, of the design or can use an algorithm that automatically changes, or “morphs,” the geometry. And every time the design geometry changes, the Hungarian algorithm recalculates the optimal fork-to-node matching.

      “Because the Hungarian algorithm is extremely fast, all the morphing and the design updating can be really fluid,” notes Mueller. In addition, any change to a new geometry is followed by a structural analysis that checks the deflections, strain energy, and other performance measures of the structure. On occasion, the automatically generated design that yields the best matching score may deviate far from the designer’s initial intention. In such cases, an alternative solution can be found that satisfactorily balances the design intention with a low matching score.

      Step 4: Automatically generate the machine code for fast cutting

      When the structural geometry and distribution of tree forks have been finalized, it’s time to think about actually building the structure. To simplify assembly and maintenance, the researchers prepare the tree forks by recutting their end faces to better match adjoining straight timbers and cutting off any remaining bark to reduce susceptibility to rot and fire.

      To guide that process, they developed a custom algorithm that automatically computes the cuts needed to make a given tree fork fit into its assigned node and to strip off the bark. The goal is to remove as little material as possible but also to avoid a complex, time-consuming machining process. “If we make too few cuts, we’ll cut off too much of the critical structural material. But we don’t want to make a million tiny cuts because it will take forever,” Mueller explains.

      The team uses facilities at the Autodesk Boston Technology Center Build Space, where the robots are far larger than any at MIT and the processing is all automated. To prepare each tree fork, they mount it on a robotic arm that pushes the joint through a traditional band saw in different orientations, guided by computer-generated instructions. The robot also mills all the holes for the structural connections. “That’s helpful because it ensures that everything is aligned the way you expect it to be,” says Mueller.

      Step 5: Assemble the available forks and linear elements to build the structure

      The final step is to assemble the structure. The tree-fork-based joints are all irregular, and combining them with the precut, straight wooden elements could be difficult. However, they’re all labeled. “All the information for the geometry is embedded in the joint, so the assembly process is really low-tech,” says Mueller. “It’s like a child’s toy set. You just follow the instructions on the joints to put all the pieces together.”

      They installed their final structure temporarily on the MIT campus, but Mueller notes that it was only a portion of the structure they plan to eventually build. “It had 12 nodes that we designed and fabricated using our process,” she says, adding that the team’s work was “a little interrupted by the pandemic.” As activity on campus resumes, the researchers plan to finish designing and building the complete structure, which will include about 40 nodes and will be installed as an outdoor pavilion on the site of the felled trees in Somerville.

      In addition, they will continue their research. Plans include working with larger material libraries, some with multibranch forks, and replacing their 3D-scanning technique with computerized tomography scanning technologies that can automatically generate a detailed geometric representation of a tree fork, including its precise fiber orientation and density. And in a parallel project, they’ve been exploring using their process with other sources of materials, with one case study focusing on using material from a demolished wood-framed house to construct more than a dozen geodesic domes.

      To Mueller, the work to date already provides new guidance for the architectural design process. With digital tools, it has become easy for architects to analyze the embodied carbon or future energy use of a design option. “Now we have a new metric of performance: How well am I using available resources?” she says. “With the Hungarian algorithm, we can compute that metric basically in real time, so we can work rapidly and creatively with that as another input to the design process.”

      This research was supported by MIT’s School of Architecture and Planning via the HASS Award.

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

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      in Energy

      Preparing global online learners for the clean energy transition

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      After a career devoted to making the electric power system more efficient and resilient, Marija Ilic came to MIT in 2018 eager not just to extend her research in new directions, but to prepare a new generation for the challenges of the clean-energy transition.

      To that end, Ilic, a senior research scientist in MIT’s Laboratory for Information and Decisions Systems (LIDS) and a senior staff member at Lincoln Laboratory in the Energy Systems Group, designed an edX course that captures her methods and vision: Principles of Modeling, Simulation, and Control for Electric Energy Systems.

      EdX is a provider of massive open online courses produced in partnership with MIT, Harvard University, and other leading universities. Ilic’s class made its online debut in June 2021, running for 12 weeks, and it is one of an expanding set of online courses funded by the MIT Energy Initiative (MITEI) to provide global learners with a view of the shifting energy landscape.

      Ilic first taught a version of the class while a professor at Carnegie Mellon University, rolled out a second iteration at MIT just as the pandemic struck, and then revamped the class for its current online presentation. But no matter the course location, Ilic focuses on a central theme: “With the need for decarbonization, which will mean accommodating new energy sources such as solar and wind, we must rethink how we operate power systems,” she says. “This class is about how to pose and solve the kinds of problems we will face during this transformation.”

      Hot global topic

      The edX class has been designed to welcome a broad mix of students. In summer 2021, more than 2,000 signed up from 109 countries, ranging from high school students to retirees. In surveys, some said they were drawn to the class by the opportunity to advance their knowledge of modeling. Many others hoped to learn about the move to decarbonize energy systems.

      “The energy transition is a hot topic everywhere in the world, not just in the U.S.,” says teaching assistant Miroslav Kosanic. “In the class, there were veterans of the oil industry and others working in investment and finance jobs related to energy who wanted to understand the potential impacts of changes in energy systems, as well as students from different fields and professors seeking to update their curricula — all gathered into a community.”

      Kosanic, who is currently a PhD student at MIT in electrical engineering and computer science, had taken this class remotely in the spring semester of 2021, while he was still in college in Serbia. “I knew I was interested in power systems, but this course was eye-opening for me, showing how to apply control theory and to model different components of these systems,” he says. “I finished the course and thought, this is just the beginning, and I’d like to learn a lot more.” Kosanic performed so well online that Ilic recruited him to MIT, as a LIDS researcher and edX course teaching assistant, where he grades homework assignments and moderates a lively learner community forum.

      A platform for problem-solving

      The course starts with fundamental concepts in electric power systems operations and management, and it steadily adds layers of complexity, posing real-world problems along the way. Ilic explains how voltage travels from point to point across transmission lines and how grid managers modulate systems to ensure that enough, but not too much, electricity flows. “To deliver power from one location to the next one, operators must constantly make adjustments to ensure that the receiving end can handle the voltage transmitted, optimizing voltage to avoid overheating the wires,” she says.

      In her early lectures, Ilic notes the fundamental constraints of current grid operations, organized around a hierarchy of regional managers dealing with a handful of very large oil, gas, coal, and nuclear power plants, and occupied primarily with the steady delivery of megawatt-hours to far-flung customers. But historically, this top-down structure doesn’t do a good job of preventing loss of energy due to sub-optimal transmission conditions or due to outages related to extreme weather events.

      These issues promise to grow for grid operators as distributed resources such as solar and wind enter the picture, Ilic tells students. In the United States, under new rules dictated by the Federal Energy Regulatory Commission, utilities must begin to integrate the distributed, intermittent electricity produced by wind farms, solar complexes, and even by homes and cars, which flows at voltages much lower than electricity produced by large power plants.

      Finding ways to optimize existing energy systems and to accommodate low- and zero-carbon energy sources requires powerful new modes of analysis and problem-solving. This is where Ilic’s toolbox comes in: a mathematical modeling strategy and companion software that simplifies the input and output of electrical systems, no matter how large or how small. “In the last part of the course, we take up modeling different solutions to electric service in a way that is technology-agnostic, where it only matters how much a black-box energy source produces, and the rates of production and consumption,” says Ilic.

      This black-box modeling approach, which Ilic pioneered in her research, enables students to see, for instance, “what is happening with their own household consumption, and how it affects the larger system,” says Rupamathi Jaddivada PhD ’20, a co-instructor of the edX class and a postdoc in electrical engineering and computer science. “Without getting lost in details of current or voltage, or how different components work, we think about electric energy systems as dynamical components interacting with each other, at different spatial scales.” This means that with just a basic knowledge of physical laws, high school and undergraduate students can take advantage of the course “and get excited about cleaner and more reliable energy,” adds Ilic.

      What Jaddivada and Ilic describe as “zoom in, zoom out” systems thinking leverages the ubiquity of digital communications and the so-called “internet of things.” Energy devices of all scales can link directly to other devices in a network instead of just to a central operations hub, allowing for real-time adjustments in voltage, for instance, vastly improving the potential for optimizing energy flows.

      “In the course, we discuss how information exchange will be key to integrating new end-to-end energy resources and, because of this interactivity, how we can model better ways of controlling entire energy networks,” says Ilic. “It’s a big lesson of the course to show the value of information and software in enabling us to decarbonize the system and build resilience, rather than just building hardware.”

      By the end of the course, students are invited to pursue independent research projects. Some might model the impact of a new energy source on a local grid or investigate different options for reducing energy loss in transmission lines.

      “It would be nice if they see that we don’t have to rely on hardware or large-scale solutions to bring about improved electric service and a clean and resilient grid, but instead on information technologies such as smart components exchanging data in real time, or microgrids in neighborhoods that sustain themselves even when they lose power,” says Ilic. “I hope students walk away convinced that it does make sense to rethink how we operate our basic power systems and that with systematic, physics-based modeling and IT methods we can enable better, more flexible operation in the future.”

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