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      in Environment

      Smart irrigation technology covers “more crop per drop”

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      In agriculture today, robots and drones can monitor fields, temperature and moisture sensors can be automated to meet crop needs, and a host of other systems and devices make farms more efficient, resource-conscious, and profitable. The use of precision agriculture, as these technologies are collectively known, offers significant advantages. However, because the technology can be costly, it remains out of reach for the majority of the world’s farmers.

      “Many of the poor around the world are small, subsistence farmers,” says Susan Amrose, research scientist with the Global Engineering and Research (GEAR) Lab at MIT. “With intensification of food production needs, worsening soil, water scarcity, and smaller plots, these farmers can’t continue with their current practices.”

      By some estimates, the global demand for fresh water will outstrip supply by as much as 40 percent by the end of the decade. Nearly 80 percent of the world’s 570 million farms are classed as smallholder farms, with many located in under-resourced and water-stressed regions. With rapid population growth and climate change driving up demand for food, and with more strain on natural resources, increasing the adoption of sustainable agricultural practices among smallholder farmers is vital. 

      Amrose, who helps lead desalination, drip irrigation, water, and sanitation projects for GEAR Lab, says these small farmers need to move to more mechanized practices. “We’re trying to make it much, much more affordable for farmers to utilize solar-powered irrigation, and to have access to tools that, right now, they’re priced out of,” she says. “More crop per drop, more crop per area, that’s our goal.”

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      No Drop to Spare: MIT creates affordable, user-driven smart irrigation technology | MIT Mechanical Engineering

      Drip irrigation systems release water and nutrients in controlled volumes directly to the root zone of the crop through a network of pipes and emitters. These systems can reduce water consumption by 20 to 60 percent when compared to conventional flood irrigation methods.

      “Agriculture uses 70 percent of the fresh water that’s in use across the globe. Large-scale adoption and correct management of drip irrigation could help to reduce consumption of fresh water, which is especially critical for regions experiencing water shortages or groundwater depletion,” says Carolyn Sheline SM ’19, a PhD student and member of the GEAR Lab’s Drip Irrigation team. “A lot of irrigation technology is developed for larger farms that can put more money into it — but inexpensive doesn’t need to mean ‘not technologically advanced.’”

      GEAR Lab has created several drip irrigation technology solutions to date, including a low-pressure drip emitter that has been shown to reduce pumping energy by more than 50 percent when compared to existing emitters; a systems-level optimization model that analyzes factors like local weather conditions and crop layouts, to cut overall system operation costs by up to 30 percent; and a low-cost precision irrigation controller that optimizes system energy and water use, enabling farmers to operate the system on an ideal schedule given their specific resources, needs, and preferences. The controller has recently been shown to reduce water consumption by over 40 percent when compared to traditional practices.

      To build these new, affordable technologies, the team tapped into a critical knowledge source — the farmers themselves.

      “We didn’t just create technology in isolation — we also advanced our understanding of how people would interact with and value this technology, and we did that before the technology had come to fruition,” says Amos Winter SM ’05, PhD ’11, associate professor of mechanical engineering and MIT GEAR Lab principal investigator. “Getting affirmations that farmers would value what the technology would do before we finished it was incredibly important.”

      The team held “Farmer Field Days” and conducted interviews with more than 200 farmers, suppliers, and industry professionals in Kenya, Morocco, and Jordan, the regions selected to host field pilot test sites. These specific sites were selected for a variety of reasons, including solar availability and water scarcity, and because all were great candidate markets for eventual adoption of the technology.

      “People usually understand their own problems really well, and they’re very good at coming up with solutions to them,” says Fiona Grant ’17, SM ’19, also a PhD candidate with the GEAR Lab Drip Irrigation team. “As designers, our role really is to provide a different set of expertise and another avenue for them to get the tools or the resources that they need.”

      The controller, for example, takes in weather information, like relative humidity, temperature, wind speed values, and precipitation. Then, using artificial intelligence, it calculates and predicts the area’s solar exposure for the day and the exact irrigation needs for the farmer, and sends information to their smartphone. How much, or how little, automation an individual site uses remains up to the farmer. In its first season of operation on a Moroccan test site, GEAR Lab technology reduced water consumption by 44 percent and energy by 38 percent when compared to a neighboring farm using traditional drip irrigation practice.

      “The way you’re going to operate a system is going to have a big impact on the way you design it,” says Grant. “We gained a sense of what farmers would be willing to change, or not, regarding interactions with the system. We found that what we might change, and what would be acceptable to change, were not necessarily the same thing.”

      GEAR Lab alumna Georgia Van de Zande ’15, SM ’18, PhD ’23, concurs. “It’s about more than just delivering a lower-cost system, it’s also about creating something they’re going to want to use and want to trust.”

      In Jordan, researchers at a full-scale test farm are operating a solar-powered drip system with a prototype of the controller and are receiving smartphone commands on when to open and close the manual valves. In Morocco, the controller is operating at a research farm with a fully automated hydraulic system; researchers are monitoring the irrigation and conducting additional agronomic tasks. In Kenya, where precision agriculture and smart irrigation haven’t yet seen very much adoption, a simpler version of the controller serves to provide educational and training information in addition to offering scheduling and control capabilities.

      Knowledge is power for the farmers, and for designers and engineers, too. If an engineer can know a user’s requirements, Winter says, they’re much more likely to create a successful solution.

      “The most powerful tool a designer can have is perspective. I have one perspective — the math and science and tech innovation side — but I don’t know a thing about what it’s like to live every day as a farmer in Jordan or Morocco,” says Winter. “I don’t know what clogs the filters, or who shuts off the water. If you can see the world through the eyes of stakeholders, you’re going to spot requirements and constraints that you wouldn’t have picked up on otherwise.”

      Winter says the technology his team is building is exciting for a lot of reasons.

      “To be in a situation where the world is saying, ‘we need to deal with water stress, we need to deal with climate adaptation, and we need to particularly do this in resource-constrained countries,’ and to be in a position where we can do something about it and produce something of tremendous value and efficacy is incredible,” says Winter. “Solving the right problem at the right time, on a massive scale, is thrilling.” More

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      in Energy

      Cutting urban carbon emissions by retrofitting buildings

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      To support the worldwide struggle to reduce carbon emissions, many cities have made public pledges to cut their carbon emissions in half by 2030, and some have promised to be carbon neutral by 2050. Buildings can be responsible for more than half a municipality’s carbon emissions. Today, new buildings are typically designed in ways that minimize energy use and carbon emissions. So attention focuses on cleaning up existing buildings.

      A decade ago, leaders in some cities took the first step in that process: They quantified their problem. Based on data from their utilities on natural gas and electricity consumption and standard pollutant-emission rates, they calculated how much carbon came from their buildings. They then adopted policies to encourage retrofits, such as adding insulation, switching to double-glazed windows, or installing rooftop solar panels. But will those steps be enough to meet their pledges?

      “In nearly all cases, cities have no clear plan for how they’re going to reach their goal,” says Christoph Reinhart, a professor in the Department of Architecture and director of the Building Technology Program. “That’s where our work comes in. We aim to help them perform analyses so they can say, ‘If we, as a community, do A, B, and C to buildings of a certain type within our jurisdiction, then we are going to get there.’”

      To support those analyses, Reinhart and a team in the MIT Sustainable Design Lab (SDL) — PhD candidate Zachary M. Berzolla SM ’21; former doctoral student Yu Qian Ang PhD ’22, now a research collaborator at the SDL; and former postdoc Samuel Letellier-Duchesne, now a senior building performance analyst at the international building engineering and consulting firm Introba — launched a publicly accessible website providing a series of simulation tools and a process for using them to determine the impacts of planned steps on a specific building stock. Says Reinhart: “The takeaway can be a clear technology pathway — a combination of building upgrades, renewable energy deployments, and other measures that will enable a community to reach its carbon-reduction goals for their built environment.”

      Analyses performed in collaboration with policymakers from selected cities around the world yielded insights demonstrating that reaching current goals will require more effort than city representatives and — in a few cases — even the research team had anticipated.

      Exploring carbon-reduction pathways

      The researchers’ approach builds on a physics-based “building energy model,” or BEM, akin to those that architects use to design high-performance green buildings. In 2013, Reinhart and his team developed a method of extending that concept to analyze a cluster of buildings. Based on publicly available geographic information system (GIS) data, including each building’s type, footprint, and year of construction, the method defines the neighborhood — including trees, parks, and so on — and then, using meteorological data, how the buildings will interact, the airflows among them, and their energy use. The result is an “urban building energy model,” or UBEM, for a neighborhood or a whole city.

      The website developed by the MIT team enables neighborhoods and cities to develop their own UBEM and to use it to calculate their current building energy use and resulting carbon emissions, and then how those outcomes would change assuming different retrofit programs or other measures being implemented or considered. “The website — UBEM.io — provides step-by-step instructions and all the simulation tools that a team will need to perform an analysis,” says Reinhart.

      The website starts by describing three roles required to perform an analysis: a local sustainability champion who is familiar with the municipality’s carbon-reduction efforts; a GIS manager who has access to the municipality’s urban datasets and maintains a digital model of the built environment; and an energy modeler — typically a hired consultant — who has a background in green building consulting and individual building energy modeling.

      The team begins by defining “shallow” and “deep” building retrofit scenarios. To explain, Reinhart offers some examples: “‘Shallow’ refers to things that just happen, like when you replace your old, failing appliances with new, energy-efficient ones, or you install LED light bulbs and weatherstripping everywhere,” he says. “‘Deep’ adds to that list things you might do only every 20 years, such as ripping out walls and putting in insulation or replacing your gas furnace with an electric heat pump.”

      Once those scenarios are defined, the GIS manager uploads to UBEM.io a dataset of information about the city’s buildings, including their locations and attributes such as geometry, height, age, and use (e.g., commercial, retail, residential). The energy modeler then builds a UBEM to calculate the energy use and carbon emissions of the existing building stock. Once that baseline is established, the energy modeler can calculate how specific retrofit measures will change the outcomes.

      Workshop to test-drive the method

      Two years ago, the MIT team set up a three-day workshop to test the website with sample users. Participants included policymakers from eight cities and municipalities around the world: namely, Braga (Portugal), Cairo (Egypt), Dublin (Ireland), Florianopolis (Brazil), Kiel (Germany), Middlebury (Vermont, United States), Montreal (Canada), and Singapore. Taken together, the cities represent a wide range of climates, socioeconomic demographics, cultures, governing structures, and sizes.

      Working with the MIT team, the participants presented their goals, defined shallow- and deep-retrofit scenarios for their city, and selected a limited but representative area for analysis — an approach that would speed up analyses of different options while also generating results valid for the city as a whole.

      They then performed analyses to quantify the impacts of their retrofit scenarios. Finally, they learned how best to present their findings — a critical part of the exercise. “When you do this analysis and bring it back to the people, you can say, ‘This is our homework over the next 30 years. If we do this, we’re going to get there,’” says Reinhart. “That makes you part of the community, so it’s a joint goal.”

      Sample results

      After the close of the workshop, Reinhart and his team confirmed their findings for each city and then added one more factor to the analyses: the state of the city’s electric grid. Several cities in the study had pledged to make their grid carbon-neutral by 2050. Including the grid in the analysis was therefore critical: If a building becomes all-electric and purchases its electricity from a carbon-free grid, then that building will be carbon neutral — even with no on-site energy-saving retrofits.

      The final analysis for each city therefore calculated the total kilograms of carbon dioxide equivalent emitted per square meter of floor space assuming the following scenarios: the baseline; shallow retrofit only; shallow retrofit plus a clean electricity grid; deep retrofit only; deep retrofit plus rooftop photovoltaic solar panels; and deep retrofit plus a clean electricity grid. (Note that “clean electricity grid” is based on the area’s most ambitious decarbonization target for their power grid.)

      The following paragraphs provide highlights of the analyses for three of the eight cities. Included are the city’s setting, emission-reduction goals, current and proposed measures, and calculations of how implementation of those measures would affect their energy use and carbon emissions.

      Singapore

      Singapore is generally hot and humid, and its building energy use is largely in the form of electricity for cooling. The city is dominated by high-rise buildings, so there’s not much space for rooftop solar installations to generate the needed electricity. Therefore, plans for decarbonizing the current building stock must involve retrofits. The shallow-retrofit scenario focuses on installing energy-efficient lighting and appliances. To those steps, the deep-retrofit scenario adds adopting a district cooling system. Singapore’s stated goals are to cut the baseline carbon emissions by about a third by 2030 and to cut it in half by 2050.

      The analysis shows that, with just the shallow retrofits, Singapore won’t achieve its 2030 goal. But with the deep retrofits, it should come close. Notably, decarbonizing the electric grid would enable Singapore to meet and substantially exceed its 2050 target assuming either retrofit scenario.

      Dublin

      Dublin has a mild climate with relatively comfortable summers but cold, humid winters. As a result, the city’s energy use is dominated by fossil fuels, in particular, natural gas for space heating and domestic hot water. The city presented just one target — a 40 percent reduction by 2030.

      Dublin has many neighborhoods made up of Georgian row houses, and, at the time of the workshop, the city already had a program in place encouraging groups of owners to insulate their walls. The shallow-retrofit scenario therefore focuses on weatherization upgrades (adding weatherstripping to windows and doors, insulating crawlspaces, and so on). To that list, the deep-retrofit scenario adds insulating walls and installing upgraded windows. The participants didn’t include electric heat pumps, as the city was then assessing the feasibility of expanding the existing district heating system.

      Results of the analyses show that implementing the shallow-retrofit scenario won’t enable Dublin to meet its 2030 target. But the deep-retrofit scenario will. However, like Singapore, Dublin could make major gains by decarbonizing its electric grid. The analysis shows that a decarbonized grid — with or without the addition of rooftop solar panels where possible — could more than halve the carbon emissions that remain in the deep-retrofit scenario. Indeed, a decarbonized grid plus electrification of the heating system by incorporating heat pumps could enable Dublin to meet a future net-zero target.

      Middlebury

      Middlebury, Vermont, has warm, wet summers and frigid winters. Like Dublin, its energy demand is dominated by natural gas for heating. But unlike Dublin, it already has a largely decarbonized electric grid with a high penetration of renewables.

      For the analysis, the Middlebury team chose to focus on an aging residential neighborhood similar to many that surround the city core. The shallow-retrofit scenario calls for installing heat pumps for space heating, and the deep-retrofit scenario adds improvements in building envelopes (the façade, roof, and windows). The town’s targets are a 40 percent reduction from the baseline by 2030 and net-zero carbon by 2050.

      Results of the analyses showed that implementing the shallow-retrofit scenario won’t achieve the 2030 target. The deep-retrofit scenario would get the city to the 2030 target but not to the 2050 target. Indeed, even with the deep retrofits, fossil fuel use remains high. The explanation? While both retrofit scenarios call for installing heat pumps for space heating, the city would continue to use natural gas to heat its hot water.

      Lessons learned

      For several policymakers, seeing the results of their analyses was a wake-up call. They learned that the strategies they had planned might not be sufficient to meet their stated goals — an outcome that could prove publicly embarrassing for them in the future.

      Like the policymakers, the researchers learned from the experience. Reinhart notes three main takeaways.

      First, he and his team were surprised to find how much of a building’s energy use and carbon emissions can be traced to domestic hot water. With Middlebury, for example, even switching from natural gas to heat pumps for space heating didn’t yield the expected effect: On the bar graphs generated by their analyses, the gray bars indicating carbon from fossil fuel use remained. As Reinhart recalls, “I kept saying, ‘What’s all this gray?’” While the policymakers talked about using heat pumps, they were still going to use natural gas to heat their hot water. “It’s just stunning that hot water is such a big-ticket item. It’s huge,” says Reinhart.

      Second, the results demonstrate the importance of including the state of the local electric grid in this type of analysis. “Looking at the results, it’s clear that if we want to have a successful energy transition, the building sector and the electric grid sector both have to do their homework,” notes Reinhart. Moreover, in many cases, reaching carbon neutrality by 2050 would require not only a carbon-free grid but also all-electric buildings.

      Third, Reinhart was struck by how different the bar graphs presenting results for the eight cities look. “This really celebrates the uniqueness of different parts of the world,” he says. “The physics used in the analysis is the same everywhere, but differences in the climate, the building stock, construction practices, electric grids, and other factors make the consequences of making the same change vary widely.”

      In addition, says Reinhart, “there are sometimes deeply ingrained conflicts of interest and cultural norms, which is why you cannot just say everybody should do this and do this.” For instance, in one case, the city owned both the utility and the natural gas it burned. As a result, the policymakers didn’t consider putting in heat pumps because “the natural gas was a significant source of municipal income, and they didn’t want to give that up,” explains Reinhart.

      Finally, the analyses quantified two other important measures: energy use and “peak load,” which is the maximum electricity demanded from the grid over a specific time period. Reinhart says that energy use “is probably mostly a plausibility check. Does this make sense?” And peak load is important because the utilities need to keep a stable grid.

      Middlebury’s analysis provides an interesting look at how certain measures could influence peak electricity demand. There, the introduction of electric heat pumps for space heating more than doubles the peak demand from buildings, suggesting that substantial additional capacity would have to be added to the grid in that region. But when heat pumps are combined with other retrofitting measures, the peak demand drops to levels lower than the starting baseline.

      The aftermath: An update

      Reinhart stresses that the specific results from the workshop provide just a snapshot in time; that is, where the cities were at the time of the workshop. “This is not the fate of the city,” he says. “If we were to do the same exercise today, we’d no doubt see a change in thinking, and the outcomes would be different.”

      For example, heat pumps are now familiar technology and have demonstrated their ability to handle even bitterly cold climates. And in some regions, they’ve become economically attractive, as the war in Ukraine has made natural gas both scarce and expensive. Also, there’s now awareness of the need to deal with hot water production.

      Reinhart notes that performing the analyses at the workshop did have the intended impact: It brought about change. Two years after the project had ended, most of the cities reported that they had implemented new policy measures or had expanded their analysis across their entire building stock. “That’s exactly what we want,” comments Reinhart. “This is not an academic exercise. It’s meant to change what people focus on and what they do.”

      Designing policies with socioeconomics in mind

      Reinhart notes a key limitation of the UBEM.io approach: It looks only at technical feasibility. But will the building owners be willing and able to make the energy-saving retrofits? Data show that — even with today’s incentive programs and subsidies — current adoption rates are only about 1 percent. “That’s way too low to enable a city to achieve its emission-reduction goals in 30 years,” says Reinhart. “We need to take into account the socioeconomic realities of the residents to design policies that are both effective and equitable.”

      To that end, the MIT team extended their UBEM.io approach to create a socio-techno-economic analysis framework that can predict the rate of retrofit adoption throughout a city. Based on census data, the framework creates a UBEM that includes demographics for the specific types of buildings in a city. Accounting for the cost of making a specific retrofit plus financial benefits from policy incentives and future energy savings, the model determines the economic viability of the retrofit package for representative households.

      Sample analyses for two Boston neighborhoods suggest that high-income households are largely ineligible for need-based incentives or the incentives are insufficient to prompt action. Lower-income households are eligible and could benefit financially over time, but they don’t act, perhaps due to limited access to information, a lack of time or capital, or a variety of other reasons.

      Reinhart notes that their work thus far “is mainly looking at technical feasibility. Next steps are to better understand occupants’ willingness to pay, and then to determine what set of federal and local incentive programs will trigger households across the demographic spectrum to retrofit their apartments and houses, helping the worldwide effort to reduce carbon emissions.”

      This work was supported by Shell through the MIT Energy Initiative. Zachary Berzolla was supported by the U.S. National Science Foundation Graduate Research Fellowship. Samuel Letellier-Duchesne was supported by the postdoctoral fellowship of the Natural Sciences and Engineering Research Council of Canada.

      This article appears in the Spring 2023 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Energy

      A new mathematical “blueprint” is accelerating fusion device development

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      Developing commercial fusion energy requires scientists to understand sustained processes that have never before existed on Earth. But with so many unknowns, how do we make sure we’re designing a device that can successfully harness fusion power?

      We can fill gaps in our understanding using computational tools like algorithms and data simulations to knit together experimental data and theory, which allows us to optimize fusion device designs before they’re built, saving much time and resources.

      Currently, classical supercomputers are used to run simulations of plasma physics and fusion energy scenarios, but to address the many design and operating challenges that still remain, more powerful computers are a necessity, and of great interest to plasma researchers and physicists.

      Quantum computers’ exponentially faster computing speeds have offered plasma and fusion scientists the tantalizing possibility of vastly accelerated fusion device development. Quantum computers could reconcile a fusion device’s many design parameters — for example, vessel shape, magnet spacing, and component placement — at a greater level of detail, while also completing the tasks faster. However, upgrading to a quantum computer is no simple task.

      In a paper, “Dyson maps and unitary evolution for Maxwell equations in tensor dielectric media,” recently published in Physics Review A, Abhay K. Ram, a research scientist at the MIT Plasma Science and Fusion Center (PSFC), and his co-authors Efstratios Koukoutsis, Kyriakos Hizanidis, and George Vahala present a framework that would facilitate the use of quantum computers to study electromagnetic waves in plasma and its manipulation in magnetic confinement fusion devices.

      Quantum computers excel at simulating quantum physics phenomena, but many topics in plasma physics are predicated on the classical physics model. A plasma (which is the “dielectric media” referenced in the paper’s title) consists of many particles — electrons and ions — the collective behaviors of which are effectively described using classic statistical physics. In contrast, quantum effects that influence atomic and subatomic scales are averaged out in classical plasma physics.  

      Furthermore, the descriptive limitations of quantum mechanics aren’t suited to plasma. In a fusion device, plasmas are heated and manipulated using electromagnetic waves, which are one of the most important and ubiquitous occurrences in the universe. The behaviors of electromagnetic waves, including how waves are formed and interact with their surroundings, are described by Maxwell’s equations — a foundational component of classical plasma physics, and of general physics as well. The standard form of Maxwell’s equations is not expressed in “quantum terms,” however, so implementing the equations on a quantum computer is like fitting a square peg in a round hole: it doesn’t work.

      Consequently, for plasma physicists to take advantage of quantum computing’s power for solving problems, classical physics must be translated into the language of quantum mechanics. The researchers tackled this translational challenge, and in their paper, they reveal that a Dyson map can bridge the translational divide between classical physics and quantum mechanics. Maps are mathematical functions that demonstrate how to take an input from one kind of space and transform it to an output that is meaningful in a different kind of space. In the case of Maxwell’s equations, a Dyson map allows classical electromagnetic waves to be studied in the space utilized by quantum computers. In essence, it reconfigures the square peg so it will fit into the round hole without compromising any physics.

      The work also gives a blueprint of a quantum circuit encoded with equations expressed in quantum bits (“qubits”) rather than classical bits so the equations may be used on quantum computers. Most importantly, these blueprints can be coded and tested on classical computers.

      “For years we have been studying wave phenomena in plasma physics and fusion energy science using classical techniques. Quantum computing and quantum information science is challenging us to step out of our comfort zone, thereby ensuring that I have not ‘become comfortably numb,’” says Ram, quoting a Pink Floyd song.

      The paper’s Dyson map and circuits have put quantum computing power within reach, fast-tracking an improved understanding of plasmas and electromagnetic waves, and putting us that much closer to the ideal fusion device design.    More

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      in Energy

      An interdisciplinary approach to fighting climate change through clean energy solutions

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      In early 2021, the U.S. government set an ambitious goal: to decarbonize its power grid, the system that generates and transmits electricity throughout the country, by 2035. It’s an important goal in the fight against climate change, and will require a switch from current, greenhouse-gas producing energy sources (such as coal and natural gas), to predominantly renewable ones (such as wind and solar).

      Getting the power grid to zero carbon will be a challenging undertaking, as Audun Botterud, a principal research scientist at the MIT Laboratory for Information and Decision Systems (LIDS) who has long been interested in the problem, knows well. It will require building lots of renewable energy generators and new infrastructure; designing better technology to capture, store, and carry electricity; creating the right regulatory and economic incentives; and more. Decarbonizing the grid also presents many computational challenges, which is where Botterud’s focus lies. Botterud has modeled different aspects of the grid — the mechanics of energy supply, demand, and storage, and electricity markets — where economic factors can have a huge effect on how quickly renewable solutions get adopted.

      On again, off again

      A major challenge of decarbonization is that the grid must be designed and operated to reliably meet demand. Using renewable energy sources complicates this, as wind and solar power depend on an infamously volatile system: the weather. A sunny day becomes gray and blustery, and wind turbines get a boost but solar farms go idle. This will make the grid’s energy supply variable and hard to predict. Additional resources, including batteries and backup power generators, will need to be incorporated to regulate supply. Extreme weather events, which are becoming more common with climate change, can further strain both supply and demand. Managing a renewables-driven grid will require algorithms that can minimize uncertainty in the face of constant, sometimes random fluctuations to make better predictions of supply and demand, guide how resources are added to the grid, and inform how those resources are committed and dispatched across the entire United States.

      “The problem of managing supply and demand in the grid has to happen every second throughout the year, and given how much we rely on electricity in society, we need to get this right,” Botterud says. “You cannot let the reliability drop as you increase the amount of renewables, especially because I think that will lead to resistance towards adopting renewables.”

      That is why Botterud feels fortunate to be working on the decarbonization problem at LIDS — even though a career here is not something he had originally planned. Botterud’s first experience with MIT came during his time as a graduate student in his home country of Norway, when he spent a year as a visiting student with what is now called the MIT Energy Initiative. He might never have returned, except that while at MIT, Botterud met his future wife, Bilge Yildiz. The pair both ended up working at the Argonne National Laboratory outside of Chicago, with Botterud focusing on challenges related to power systems and electricity markets. Then Yildiz got a faculty position at MIT, where she is a professor of nuclear and materials science and engineering. Botterud moved back to the Cambridge area with her and continued to work for Argonne remotely, but he also kept an eye on local opportunities. Eventually, a position at LIDS became available, and Botterud took it, while maintaining his connections to Argonne.

      “At first glance, it may not be an obvious fit,” Botterud says. “My work is very focused on a specific application, power system challenges, and LIDS tends to be more focused on fundamental methods to use across many different application areas. However, being at LIDS, my lab [the Energy Analytics Group] has access to the most recent advances in these fundamental methods, and we can apply them to power and energy problems. Other people at LIDS are working on energy too, so there is growing momentum to address these important problems.”

      Weather, space, and time

      Much of Botterud’s research involves optimization, using mathematical programming to compare alternatives and find the best solution. Common computational challenges include dealing with large geographical areas that contain regions with different weather, different types and quantities of renewable energy available, and different infrastructure and consumer needs — such as the entire United States. Another challenge is the need for granular time resolution, sometimes even down to the sub-second level, to account for changes in energy supply and demand.

      Often, Botterud’s group will use decomposition to solve such large problems piecemeal and then stitch together solutions. However, it’s also important to consider systems as a whole. For example, in a recent paper, Botterud’s lab looked at the effect of building new transmission lines as part of national decarbonization. They modeled solutions assuming coordination at the state, regional, or national level, and found that the more regions coordinate to build transmission infrastructure and distribute electricity, the less they will need to spend to reach zero carbon.

      In other projects, Botterud uses game theory approaches to study strategic interactions in electricity markets. For example, he has designed agent-based models to analyze electricity markets. These assume each actor will make strategic decisions in their own best interest and then simulate interactions between them. Interested parties can use the models to see what would happen under different conditions and market rules, which may lead companies to make different investment decisions, or governing bodies to issue different regulations and incentives. These choices can shape how quickly the grid gets decarbonized.

      Botterud is also collaborating with researchers in MIT’s chemical engineering department who are working on improving battery storage technologies. Batteries will help manage variable renewable energy supply by capturing surplus energy during periods of high generation to release during periods of insufficient generation. Botterud’s group models the sort of charge cycles that batteries are likely to experience in the power grid, so that chemical engineers in the lab can test their batteries’ abilities in more realistic scenarios. In turn, this also leads to a more realistic representation of batteries in power system optimization models.

      These are only some of the problems that Botterud works on. He enjoys the challenge of tackling a spectrum of different projects, collaborating with everyone from engineers to architects to economists. He also believes that such collaboration leads to better solutions. The problems created by climate change are myriad and complex, and solving them will require researchers to cooperate and explore.

      “In order to have a real impact on interdisciplinary problems like energy and climate,” Botterud says, “you need to get outside of your research sweet spot and broaden your approach.” More

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      in Energy

      Michael Howland gives wind energy a lift

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      Michael Howland was in his office at MIT, watching real-time data from a wind farm 7,000 miles away in northwest India, when he noticed something odd: Some of the turbines weren’t producing the expected amount of electricity.

      Howland, the Esther and Harold E. Edgerton Assistant Professor of Civil and Environmental Engineering, studies the physics of the Earth’s atmosphere and how that information can optimize renewable energy systems. To accomplish this, he and his team develop and use predictive models, supercomputer simulations, and real-life data from wind farms, such as the one in India.

      The global wind power market is one of the most cost-competitive and resilient power sources across the world, the Global Wind Energy Council reported last year. The year 2020 saw record growth in wind power capacity, thanks to a surge of installations in China and the United States. Yet wind power needs to grow three times faster in the coming decade to address the worst impacts of climate change and achieve federal and state climate goals, the report says.

      “Optimal wind farm design and the resulting cost of energy are dependent on the wind,” Howland says. “But wind farms are often sited and designed based on short-term historical climate records.”

      In October 2021, Howland received a Seed Fund grant from the MIT Energy Initiative (MITEI) to account for how climate change might affect the wind of the future. “Our initial results suggest that considering the uncertainty in the winds in the design and operation of wind farms can lead to more reliable energy production,” he says.

      Most recently, Howland and his team came up with a model that predicts the power produced by each individual turbine based on the physics of the wind farm as a whole. The model can inform decisions that may boost a farm’s overall output.

      The state of the planet

      Growing up in a suburb of Philadelphia, the son of neuroscientists, Howland’s childhood wasn’t especially outdoorsy. Later, he’d become an avid hiker with a deep appreciation for nature, but a ninth-grade class assignment made him think about the state of the planet, perhaps for the first time.

      A history teacher had asked the class to write a report on climate change. “I remember arguing with my high school classmates about whether humans were the leading cause of climate change, but the teacher didn’t want to get into that debate,” Howland recalls. “He said climate change was happening, whether or not you accept that it’s anthropogenic, and he wanted us to think about the impacts of global warming, and solutions. I was one of his vigorous defenders.”

      As part of a research internship after his first year of college, Howland visited a wind farm in Iowa, where wind produces more than half of the state’s electricity. “The turbines look tall from the highway, but when you’re underneath them, you’re really struck by their scale,” he says. “That’s where you get a sense of how colossal they really are.” (Not a fan of heights, Howland opted not to climb the turbine’s internal ladder to snap a photo from the top.)

      After receiving an undergraduate degree from Johns Hopkins University and master’s and PhD degrees in mechanical engineering from Stanford University, he joined MIT’s Department of Civil and Environmental Engineering to focus on the intersection of fluid mechanics, weather, climate, and energy modeling. His goal is to enhance renewable energy systems.

      An added bonus to being at MIT is the opportunity to inspire the next generation, much like his ninth-grade history teacher did for him. Howland’s graduate-level introduction to the atmospheric boundary layer is geared primarily to engineers and physicists, but as he sees it, climate change is such a multidisciplinary and complex challenge that “every skill set that exists in human society can be relevant to mitigating it.”

      “There are the physics and engineering questions that our lab primarily works on, but there are also questions related to social sciences, public acceptance, policymaking, and implementation,” he says. “Careers in renewable energy are rapidly growing. There are far more job openings than we can hire for right now. In many areas, we don’t yet have enough people to address the challenges in renewable energy and climate change mitigation that need to be solved.

      “I encourage my students — really, everyone I interact with — to find a way to impact the climate change problem,” he says.

      Unusual conditions

      In fall 2021, Howland was trying to explain the odd data coming in from India.

      Based on sensor feedback, wind turbines’ software-driven control systems constantly tweak the speed and the angle of the blades, and what’s known as yaw — the orientation of the giant blades in relation to the wind direction.

      Existing utility-scale turbines are controlled “greedily,” which means that every turbine in the farm automatically turns into the wind to maximize its own power production.

      Though the turbines in the front row of the Indian wind farm were reacting appropriately to the wind direction, their power output was all over the place. “Not what we would expect based on the existing models,” Howland says.

      These massive turbine towers stood at 100 meters, about the length of a football field, with blades the length of an Olympic swimming pool. At their highest point, the blade tips lunged almost 200 meters into the sky.

      Then there’s the speed of the blades themselves: The tips move many times faster than the wind, around 80 to 100 meters per second — up to a quarter or a third of the speed of sound.

      Using a state-of-the-art sensor that measures the speed of incoming wind before it interacts with the massive rotors, Howland’s team saw an unexpectedly complex airflow effect. He covers the phenomenon in his class. The data coming in from India, he says, displayed “quite remarkable wind conditions stemming from the effects of Earth’s rotation and the physics of buoyancy 
that you don’t always see.”

      Traditionally, wind turbines operate in the lowest 10 percent of the atmospheric boundary layer — the so-called surface layer — which is affected primarily by ground conditions. The Indian turbines, Howland realized, were operating in regions of the atmosphere that turbines haven’t historically accessed.

      Trending taller

      Howland knew that airflow interactions can persist for kilometers. The interaction of high winds with the front-row turbines was generating wakes in the air similar to the way boats generate wakes in the water.

      To address this, Howland’s model trades off the efficiency of upwind turbines to benefit downwind ones. By misaligning some of the upwind turbines in certain conditions, the downwind units experience less wake turbulence, increasing the overall energy output of the wind farm by as much as 1 percent to 3 percent, without requiring additional costs. If a 1.2 percent energy increase was applied to the world’s existing wind farms, it would be the equivalent of adding more than 3,600 new wind turbines — enough to power about 3 million homes.

      Even a modest boost could mean fewer turbines generating the same output, or the ability to place more units into a smaller space, because negative interactions between the turbines can be diminished.

      Howland says the model can predict potential benefits in a variety of scenarios at different types of wind farms. “The part that’s important and exciting is that it’s not just particular to this wind farm. We can apply the collective control method across the wind farm fleet,” he says, which is growing taller and wider.

      By 2035, the average hub height for offshore turbines in the United States is projected to grow from 100 meters to around 150 meters — the height of the Washington Monument.

      “As we continue to build larger wind turbines and larger wind farms, we need to revisit the existing practice for their design and control,” Howland says. “We can use our predictive models to ensure that we build and operate the most efficient renewable generators possible.”

      Looking to the future

      Howland and other climate watchers have reason for optimism with the passage in August 2022 of the Inflation Reduction Act, which calls for a significant investment in domestic energy production and for reducing carbon emissions by roughly 40 percent by 2030.

      But Howland says the act itself isn’t sufficient. “We need to continue pushing the envelope in research and development as well as deployment,” he says. The model he created with his team can help, especially for offshore wind farms experiencing low wind turbulence and larger wake interactions.

      Offshore wind can face challenges of public acceptance. Howland believes that researchers, policymakers, and the energy industry need to do more to get the public on board by addressing concerns through open public dialogue, outreach, and education.

      Howland once wrote and illustrated a children’s book, inspired by Dr. Seuss’s “The Lorax,” that focused on renewable energy. Howland recalls his “really terrible illustrations,” but he believes he was onto something. “I was having some fun helping people interact with alternative energy in a more natural way at an earlier age,” he says, “and recognize that these are not nefarious technologies, but remarkable feats of human ingenuity.” More

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      in Environment

      Engaging enterprises with the climate crisis

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      Almost every large corporation is committed to achieving net zero carbon emissions by 2050 but lacks a roadmap to get there, says John Sterman, professor of management at MIT’s Sloan School of Management, co-director of the MIT Sloan Sustainability Initiative, and leader of its Climate Pathways Project. Sterman and colleagues offer a suite of well-honed strategies to smooth this journey, including a free global climate policy simulator called En-ROADS deployed in workshops that have educated more than 230,000 people, including thousands of senior elected officials and leaders in business and civil society around the world. 

      Running on ordinary laptops, En-ROADS examines how we can reduce carbon emissions to keep global warming under 2 degrees Celsius, Sterman says. Users, expert or not, can easily explore how dozens of policies, such as pricing carbon and electrifying vehicles, can affect hundreds of factors such as temperature, energy prices, and sea level rise. 

      En-ROADs and related work on climate change are just one thread in Sterman’s decades of research to integrate environmental sustainability with business decisions. 

      “There’s a fundamental alignment between a healthy environment, a healthy society, and a healthy economy,” he says. “Destroy the environment and you destroy the economy and society. Likewise, hungry, ill-housed, insecure people, lacking decent jobs and equity in opportunity, will catch the last fish and cut the last tree, destroying the environment and society. Unfortunately, a lot of businesses still see the issue as a trade-off — if we focus on the environment, it will hurt our bottom line; if we improve working conditions, it will raise our labor costs. That turns out not to be true in many, many cases. But how can we help people understand that fundamental alignment? That’s where simulation models can play a big role.”

      Play video

      Learning with management flight simulators 

      “My original field is system dynamics, a method for understanding the complex systems in which we’re embedded—whether those are organizations, companies, markets, society as a whole, or the climate system” Sterman says. “You can build these wonderful, complex simulation models that offer important insights and insight into high-leverage policies so that organizations can make significant improvements.” 

      “But those models don’t do any good at all unless the folks in those organizations can learn for themselves about what those high-leverage opportunities are,” he emphasizes. “You can show people the best scientific evidence, the best data, and it’s not necessarily going to change their minds about what they ought to be doing. You’ve got to create a process that helps smart but busy people learn how they can improve their organizations.” 

      Sterman and his colleagues pioneered management flight simulators — which, like aircraft flight simulators, offer an environment in which you can make decisions, seeing what works and what doesn’t, at low cost with no risk. 

      “People learn best from experience and experiment,” he points out. “But in many of the most important settings that we face today, experience comes too late to be useful, and experiments are impossible. In such settings, simulation becomes the only way people can learn for themselves and gain the confidence to change their behavior in the real world.” 

      “You can’t learn to fly a new jetliner by watching someone else; to learn, one must be at the controls,” Sterman emphasizes. “People don’t change deeply embedded beliefs and behaviors just because somebody tells them that what they’re doing is harmful and there are better options. People have to learn for themselves.”

      Play video

      Learning the business of sustainability 

      His longstanding “laboratory for sustainable business” course lets MIT Sloan School students learn the state of the art in sustainability challenges — not just climate change but microplastics, water shortages, toxins in our food and air, and other crises. As part of the course, students work in teams with organizations on real sustainability challenges. “We’ve had a very wide range of companies and other organizations participate, and many of them come back year after year,” Sterman says. 

      MIT Sloan also offers executive education in sustainability, in both open enrollment and customized programs. “We’ve had all kinds of folks, from all over the world and every industry” he says. 

      In his opening class for executive MBAs, he polls attendees to ask if sustainability is a material issue for their companies, and how actively those companies are addressing that issue. Almost all of the attendees agree that sustainability is a key issue, but nearly all say their companies are not doing enough, with many saying they “comply with all applicable laws and regulations.” 

      “So there’s a huge disconnect,” Sterman points out. “How do you close that gap? How do you take action? How do you break the idea that if you take action to be more sustainable it will hurt your business, when in fact it’s almost always the other way around? And then how can you make the change happen, so that what you’re doing will get implemented and stick?” 

      Simulating policies for sustainability 

      Management flight simulators that offer active learning can provide crucial guidance. In the case of climate change, En-ROADs presents a straightforward interface that lets users adjust sliders to experiment with actions to try to bring down carbon emissions. “Should we have a price on carbon?” Sterman asks. “Should we promote renewables? Should we work on methane? Stop deforestation? You can try anything you want. You get immediate feedback on the likely consequences of your decisions. Often people are surprised as favorite policies — say, planting trees — have only minor impact on global warming. (In the case of trees, because it takes so long for the trees to grow).”

      One En-ROADS alumnus works for a pharmaceutical company that set a target of zero net emissions by mid-century. But, as often observed, measures proposed at the senior corporate level were often resisted by the operating units. The alumnus attacked the problem by bringing workshops with simulations and other sustainability tools to front-line employees in a manufacturing plant he knew well. He asked these employees how they thought they could reduce carbon emissions and what they needed to do so. 

      “It turns out that they had a long list of opportunities to reduce the emissions from this plant,” Sterman says. “But they didn’t have any support to get it done. He helped their ideas get that support, get the resources, come up with ways to monitor their progress, and ways to look for quick wins. It’s been highly successful.” 

      En-ROADS helps people understand that process improvement activity takes resources; you might need to take some equipment offline temporarily, for example, to upgrade or improve it. “There’s a little bit of a worse-before-better trade-off,” he says. “You need to be prepared. The active learning, the use of the simulators, helps people prepare for that journey and overcome the barriers that they will face.” 

      Interactive workshops with En-ROADS and other sustainability tools also brought change to another large corporation, HSBC Bank U.S.A. Like many other financial institutions, HSBC has committed to significantly cut its emissions, but many employees and executives didn’t understand why or what that would entail. For instance, would the bank give up potential business in carbon-intensive industries? 

      Brought to more than 1,000 employees, the En-ROADS workshops let employees surface concerns they might have about continuing to be successful while addressing climate concerns. “It turns out in many cases, there isn’t that much of a trade-off,” Sterman remarks. “Fossil energy projects, for example, are extremely risky. And there are opportunities to improve margins in other businesses where you can help cut their carbon footprint.” 

      The free version of En-ROADS generally satisfies the needs of most organizations, but Sterman and his partners also can augment the model or develop customized workshops to address specific concerns. 

      People who take the workshops emerge with a greater understanding of climate change and its effects, and a deeper knowledge of the high-leverage opportunities to cut emissions. “Even more importantly, they come out with a greater sense of urgency,” he says. “But they also come out with an understanding that it’s not too late. Time is short, but what we do can still make a difference.”  More

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      in Environment

      Improving health outcomes by targeting climate and air pollution simultaneously

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      Climate policies are typically designed to reduce greenhouse gas emissions that result from human activities and drive climate change. The largest source of these emissions is the combustion of fossil fuels, which increases atmospheric concentrations of ozone, fine particulate matter (PM2.5) and other air pollutants that pose public health risks. While climate policies may result in lower concentrations of health-damaging air pollutants as a “co-benefit” of reducing greenhouse gas emissions-intensive activities, they are most effective at improving health outcomes when deployed in tandem with geographically targeted air-quality regulations.

      Yet the computer models typically used to assess the likely air quality/health impacts of proposed climate/air-quality policy combinations come with drawbacks for decision-makers. Atmospheric chemistry/climate models can produce high-resolution results, but they are expensive and time-consuming to run. Integrated assessment models can produce results for far less time and money, but produce results at global and regional scales, rendering them insufficiently precise to obtain accurate assessments of air quality/health impacts at the subnational level.

      To overcome these drawbacks, a team of researchers at MIT and the University of California at Davis has developed a climate/air-quality policy assessment tool that is both computationally efficient and location-specific. Described in a new study in the journal ACS Environmental Au, the tool could enable users to obtain rapid estimates of combined policy impacts on air quality/health at more than 1,500 locations around the globe — estimates precise enough to reveal the equity implications of proposed policy combinations within a particular region.

      “The modeling approach described in this study may ultimately allow decision-makers to assess the efficacy of multiple combinations of climate and air-quality policies in reducing the health impacts of air pollution, and to design more effective policies,” says Sebastian Eastham, the study’s lead author and a principal research scientist at the MIT Joint Program on the Science and Policy of Global Change. “It may also be used to determine if a given policy combination would result in equitable health outcomes across a geographical area of interest.”

      To demonstrate the efficiency and accuracy of their policy assessment tool, the researchers showed that outcomes projected by the tool within seconds were consistent with region-specific results from detailed chemistry/climate models that took days or even months to run. While continuing to refine and develop their approaches, they are now working to embed the new tool into integrated assessment models for direct use by policymakers.

      “As decision-makers implement climate policies in the context of other sustainability challenges like air pollution, efficient modeling tools are important for assessment — and new computational techniques allow us to build faster and more accurate tools to provide credible, relevant information to a broader range of users,” says Noelle Selin, a professor at MIT’s Institute for Data, Systems and Society and Department of Earth, Atmospheric and Planetary Sciences, and supervising author of the study. “We are looking forward to further developing such approaches, and to working with stakeholders to ensure that they provide timely, targeted and useful assessments.”

      The study was funded, in part, by the U.S. Environmental Protection Agency and the Biogen Foundation. More

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      in Environment

      Study: Carbon-neutral pavements are possible by 2050, but rapid policy and industry action are needed

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      Almost 2.8 million lane-miles, or about 4.6 million lane-kilometers, of the United States are paved.

      Roads and streets form the backbone of our built environment. They take us to work or school, take goods to their destinations, and much more.

      However, a new study by MIT Concrete Sustainability Hub (CSHub) researchers shows that the annual greenhouse gas (GHG) emissions of all construction materials used in the U.S. pavement network are 11.9 to 13.3 megatons. This is equivalent to the emissions of a gasoline-powered passenger vehicle driving about 30 billion miles in a year.

      As roads are built, repaved, and expanded, new approaches and thoughtful material choices are necessary to dampen their carbon footprint. 

      The CSHub researchers found that, by 2050, mixtures for pavements can be made carbon-neutral if industry and governmental actors help to apply a range of solutions — like carbon capture — to reduce, avoid, and neutralize embodied impacts. (A neutralization solution is any compensation mechanism in the value chain of a product that permanently removes the global warming impact of the processes after avoiding and reducing the emissions.) Furthermore, nearly half of pavement-related greenhouse gas (GHG) savings can be achieved in the short term with a negative or nearly net-zero cost.

      The research team, led by Hessam AzariJafari, MIT CSHub’s deputy director, closed gaps in our understanding of the impacts of pavements decisions by developing a dynamic model quantifying the embodied impact of future pavements materials demand for the U.S. road network. 

      The team first split the U.S. road network into 10-mile (about 16 kilometer) segments, forecasting the condition and performance of each. They then developed a pavement management system model to create benchmarks helping to understand the current level of emissions and the efficacy of different decarbonization strategies. 

      This model considered factors such as annual traffic volume and surface conditions, budget constraints, regional variation in pavement treatment choices, and pavement deterioration. The researchers also used a life-cycle assessment to calculate annual state-level emissions from acquiring pavement construction materials, considering future energy supply and materials procurement.

      The team considered three scenarios for the U.S. pavement network: A business-as-usual scenario in which technology remains static, a projected improvement scenario aligned with stated industry and national goals, and an ambitious improvement scenario that intensifies or accelerates projected strategies to achieve carbon neutrality. 

      If no steps are taken to decarbonize pavement mixtures, the team projected that GHG emissions of construction materials used in the U.S. pavement network would increase by 19.5 percent by 2050. Under the projected scenario, there was an estimated 38 percent embodied impact reduction for concrete and 14 percent embodied impact reduction for asphalt by 2050.

      The keys to making the pavement network carbon neutral by 2050 lie in multiple places. Fully renewable energy sources should be used for pavement materials production, transportation, and other processes. The federal government must contribute to the development of these low-carbon energy sources and carbon capture technologies, as it would be nearly impossible to achieve carbon neutrality for pavements without them. 

      Additionally, increasing pavements’ recycled content and improving their design and production efficiency can lower GHG emissions to an extent. Still, neutralization is needed to achieve carbon neutrality.

      Making the right pavement construction and repair choices would also contribute to the carbon neutrality of the network. For instance, concrete pavements can offer GHG savings across the whole life cycle as they are stiffer and stay smoother for longer, meaning they require less maintenance and have a lesser impact on the fuel efficiency of vehicles. 

      Concrete pavements have other use-phase benefits including a cooling effect through an intrinsically high albedo, meaning they reflect more sunlight than regular pavements. Therefore, they can help combat extreme heat and positively affect the earth’s energy balance through positive radiative forcing, making albedo a potential neutralization mechanism.

      At the same time, a mix of fixes, including using concrete and asphalt in different contexts and proportions, could produce significant GHG savings for the pavement network; decision-makers must consider scenarios on a case-by-case basis to identify optimal solutions. 

      In addition, it may appear as though the GHG emissions of materials used in local roads are dwarfed by the emissions of interstate highway materials. However, the study found that the two road types have a similar impact. In fact, all road types contribute heavily to the total GHG emissions of pavement materials in general. Therefore, stakeholders at the federal, state, and local levels must be involved if our roads are to become carbon neutral. 

      The path to pavement network carbon-neutrality is, therefore, somewhat of a winding road. It demands regionally specific policies and widespread investment to help implement decarbonization solutions, just as renewable energy initiatives have been supported. Providing subsidies and covering the costs of premiums, too, are vital to avoid shifts in the market that would derail environmental savings.

      When planning for these shifts, we must recall that pavements have impacts not just in their production, but across their entire life cycle. As pavements are used, maintained, and eventually decommissioned, they have significant impacts on the surrounding environment.

      If we are to meet climate goals such as the Paris Agreement, which demands that we reach carbon-neutrality by 2050 to avoid the worst impacts of climate change, we — as well as industry and governmental stakeholders — must come together to take a hard look at the roads we use every day and work to reduce their life cycle emissions. 

      The study was published in the International Journal of Life Cycle Assessment. In addition to AzariJafari, the authors include Fengdi Guo of the MIT Department of Civil and Environmental Engineering; Jeremy Gregory, executive director of the MIT Climate and Sustainability Consortium; and Randolph Kirchain, director of the MIT CSHub. More