From new farming practices to paleontology, meet four Georgia Tech researchers who improve the climate and predict its future.

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On Jan. 13, the Georgia Tech Strategic Energy Institute (SEI), Southern Company, and Georgia Power hosted a workshop aimed at imparting new information and sparking innovative ideas around the Tech Square Microgrid (TSMG) and the use of its data in education and research. 

Launched in 2021 with a 1.4-megawatt capacity, the microgrid is located on Williams Street, just south of Fifth Street in Tech Square. It is a joint project between Georgia Power and Georgia Tech.

The workshop brought together experts and enthusiasts to discuss the current operations of the TSMG, an essential asset for the energy resilience of Georgia Tech’s High-Performance Computing Center located next to Coda, and to share Georgia Tech studies that use data from the microgrid.

The workshop started with an overview of the microgrid’s current status and capabilities as a resilience resource, provided by Collins Pratt, the TSMG managing engineer from Georgia Power. This was followed by a keynote on TSMG modeling and simulation presented by GTRI research engineer and SEI’s lead on cybersecurity of critical infrastructure initiative, Sam Litchfield, and Adam King, a GTRI research engineer and Ph.D. student. Their insights into the state of the art of microgrid modeling and its role in assessing dependencies between energy grid resources set the stage for the rest of the workshop discussions.

During the panel on “Microgrids and Living Labs,” panelists from academia, Georgia Tech’s Office of Sustainability, and Georgia Power shared their perspectives on microgrid infrastructure and its role in research and education. The discussion emphasized the importance of microgrids as living labs, their potential for wider deployment in distributed energy systems, and the need for skilled graduates in the electrical utility workforce.

To conclude the event, attendees toured the microgrid site across the street. The tour, led by Pratt, provided a firsthand look at the microgrid infrastructure and its operating capabilities.

Reflecting on the significance of the microgrid, SEI’s Interim Executive Director Christine Conwell said, "The data from the microgrid is a vital resource for researchers at Georgia Tech and our metro Atlanta partners. It not only showcases our work in energy resilience but also serves as a living laboratory for developing innovative energy solutions."

Georgia Tech Senior Research Engineer Scott Duncan, who leads the Microgrids initiative at SEI organized the workshop along with Southern Company Principal Research Engineer Andrew Ingram. "The details shared during the workshop have sparked numerous ideas, from an array of perspectives across Georgia Tech, on how we can further utilize the microgrid and the unique data coming from its operations,” said Duncan. “The insights gained today will undoubtedly seed future research and collaborations, pushing the boundaries of what we can achieve in energy resilience."

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Andrei Fedorov, Associate Chair for Graduate Studies, Rae S. and Frank H. Neely Chair, and professor in the George W. Woodruff School of Mechanical Engineering, will represent Georgia Tech in a new international research initiative. The program, Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE) for Top Scientists, is funded by the Japan Science and Technology Agency. It will receive approximately $3.2 million in funding over five years.

The award will support a broad spectrum of multidisciplinary research activities by the multinational teams and intermediate to long-term (three months to one year) collaborative visits to global research sites in Japan, Europe, and the U.S. A total of 46 proposals were submitted to ASPIRE for Top Scientists, out of which 14 were selected by expert evaluation. Each project is an international collaboration and the initiative's key focus is advancing science and technology on an international level.

Fedorov will lead a project titled "Construction of International Data and Analysis Platform for Inorganic Power-storage Materials Informatics with Nano/Micro-Structure" that will explore the intersection of Artificial Intelligence (AI) and Informatics, and Energy. He will represent Georgia Tech as a principal investigator. The planned research will also involve faculty members and graduate students from College of Engineering schools involved in the Strategic Energy Institute.

Read the full story on the George W. Woodruff School of Mechanical Engineering website.

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The "solar rebound effect" is a phenomenon where households with residential solar photovoltaic (PV) systems end up consuming more electricity in response to greater solar energy generation. This outcome arises because the cost savings from generating their own electricity lead to increased usage. A recent study by Matthew E. Oliver from the Georgia Institute of Technology and his co-authors, Juan Moreno-Cruz from the University of Waterloo and Kenneth Gillingham from Yale University, delves into this effect, providing crucial insights for policymakers and researchers.

The study, titled "Microeconomics of the Solar Rebound under Net Metering," explores how different net metering policies influence the solar rebound effect. Net metering allows households to sell excess electricity generated by their solar panels back to the grid, often at the retail rate. This policy makes solar PV systems more financially attractive but also impacts household behavior.

The authors developed a theoretical framework to understand the solar rebound. They found that under classic net metering, the rebound is primarily an income effect. Households feel wealthier due to the savings on their electricity bills and thus consume more electricity. However, under net billing, where excess electricity is compensated at a lower rate, a substitution effect also comes into play. This means households might change their consumption patterns based on the relative costs of electricity from the grid versus their solar panels.

The study also incorporates behavioral economics concepts like moral licensing and warm glow effects. Moral licensing occurs when people justify increased consumption because they feel they are already doing something good, like generating green energy. Warm glow refers to the positive feelings from contributing to environmental sustainability, which can either increase or decrease consumption depending on the household's values.

One of the key takeaways from the study is the importance of the regulatory environment. Policymakers need to carefully design net metering policies to balance promoting solar adoption while accounting for the possibility that rebound effects may offset the desired outcomes of grid resilience and reduced greenhouse gas emissions. For instance, switching from net metering to net billing might reduce the rebound effect, leading to better environmental outcomes.

The welfare analysis conducted by the authors shows that the solar rebound's impact on social welfare depends on various factors, including the cleanliness of the electricity grid and the external costs of electricity production. In cleaner grids, the rebound might be less detrimental, while in grids reliant on fossil fuels, it could negate some of the environmental benefits of solar adoption.

This research underscores the complexity of energy policy and the need for nuanced approaches that consider both economic and behavioral factors. By understanding the solar rebound effect, stakeholders can make more informed decisions to promote sustainable energy use.

For more detailed insights, you can explore the full study by Matthew E. Oliver and his co-authors. Their work provides a robust foundation for future empirical research and policy development in the field of renewable energy.

This article was written with the assistance of Microsoft Copilot (Jan. 27, 2025) and edited by Georgia Tech EPIcenter's Gilbert X. Gonzalez and Matthew E. Oliver.

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Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate.

 “What is happening within the field is revolutionary,” says School of Earth and Atmospheric Sciences Associate Chair and Professor Annalisa Bracco, adding that because many climate-related processes — from ocean currents to melting glaciers and weather patterns — can be described with physical equations, these advancements have the potential to help us understand and predict climate in critically important ways. 

Bracco is the lead author of a new review paper providing a comprehensive look at the intersection of AI and climate physics.

The result of an international collaboration between Georgia Tech’s Bracco, Julien Brajard (Nansen Environmental and Remote Sensing Center), Henk A. Dijkstra (Utrecht University), Pedram Hassanzadeh (University of Chicago), Christian Lessig (European Centre for Medium-Range Weather Forecasts), and Claire Monteleoni (University of Colorado Boulder), the paper, ‘Machine learning for the physics of climate,’ was recently published in Nature Reviews Physics. 

“One of our team’s goals was to help people think deeply on how climate science and AI intersect,” Bracco shares. “Machine learning is allowing us to study the physics of climate in a way that was previously impossible. Coupled with increasing amounts of data and observations, we can now investigate climate at scales and resolutions we’ve never been able to before.”

Connecting hidden dots

The team showed that ML is driving change in three key areas: accounting for missing observational data, creating more robust climate models, and enhancing predictions, especially in weather forecasting. However, the research also underscores the limits of AI — and how researchers can work to fill those gaps.

“Machine learning has been fantastic in allowing us to expand the time and the spatial scales for which we have measurements,” says Bracco, explaining that ML could help fill in missing data points — creating a more robust record for researchers to reference. However, like patching a hole in a shirt, this works best when the rest of the material is intact.

“Machine learning can extrapolate from past conditions when observations are abundant, but it can’t yet predict future trends or collect the data we need,” Bracco adds. “To keep advancing, we need scientists who can determine what data we need, collect that data, and solve problems.”

Modeling climate, predicting weather

Machine learning is often used when improving climate models that can simulate changing systems like our atmosphere, oceans, land, biochemistry, and ice. “These models are limited because of our computing power, and are run on a three-dimensional grid,” Bracco explains: below the grid resolution, researchers need to approximate complex physics with simpler equations that computers can solve quickly, a process called ‘parameterization’.

Machine learning is changing that, offering new ways to improve parameterizations, she says. “We can run a model at extremely high resolutions for a short time, so that we don’t need to parameterize as many physical processes — using machine learning to derive the equations that best approximate what is happening at small scales,” she explains. “Then we can use those equations in a coarser model that we can run for hundreds of years.”

While a full climate model based solely on machine learning may remain out of reach, the team found that ML is advancing our ability to accurately predict weather systems and some climate phenomena like El Niño. 

Previously, weather prediction was based on knowing the starting conditions — like temperature, humidity, and barometric pressure — and running a model based on physics equations to predict what might happen next. Now, machine learning is giving researchers the opportunity to learn from the past. “We can use information on what has happened when there were similar starting conditions in previous situations to predict the future without solving the underlying governing equations,” Bracco says. “And all while using orders-of-magnitude less computing resources.”

The human connection

Bracco emphasizes that while AI and ML play a critical role in accelerating research, humans are at the core of progress. “I think the in-person collaboration that led to this paper is, in itself, a testament to the importance of human interaction,” she says, recalling that the research was the result of a workshop organized at the Kavli Institute for Theoretical Physics — one of the team’s first in-person discussions after the Covid-19 pandemic.

“Machine learning is a fantastic tool — but it's not the solution to everything,” she adds. “There is also a real need for human researchers collecting high-quality data, and for interdisciplinary collaboration across fields. I see this as a big challenge, but a great opportunity for computer scientists and physicists, mathematicians, biologists, and chemists to work together.”

Funding: National Science Foundation, European Research Council, Office of Naval Research, US Department of Energy, European Space Agency, Choose France Chair in AI.

DOI: https://doi.org/10.1038/s42254-024-00776-3

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From the physics of knitting to highlighting how batteries work, Georgia Tech photographers captured the impact and breadth of the Institute’s research enterprise. See our best shots and discover unseen gems in this collection.

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Associate Professor Marta Hatzell has been appointed interim deputy director of the Georgia Tech Strategic Energy Institute (SEI).

Hatzell currently leads the industrial decarbonization and clean catalysis initiative at SEI and holds joint appointments in the George W. Woodruff School of Mechanical Engineering and the School of Chemical and Biomolecular Engineering. Most recently, she led the Materials for Carbon-Neutral Fuel Production track at this year’s Energy Materials Day, which attracted over 400 participants from academia and industry. 

“Marta has been an outstanding addition to SEI’s initiative lead program, where her contributions have already made a significant impact. I am excited to see her further engage with the Georgia Tech energy community in this role,” said Christine Conwell, SEI interim executive director.

Hatzell’s research group focuses on exploring sustainable catalysis and separations to enable clean energy, water, and food production. She serves as the site principal investigator and research thrust leader for CASFER, the National Science Foundation Engineering Research Center (NSF-ERC) with funding of $26 million for advancing sustainable fertilizer production. She is also a principal investigator in the Department of Energy’s $100 million National Alliance for Water Innovation project. 

Hatzell is the recipient of the NSF Early CAREER Award for her work on distributed solar fertilizers and received the Gordon and Betty Moore Inventor fellowship to work on a low-cost, photocatalytic air-breathing system that converts air into liquid, ammonia-based fertilizer. She also received the 2020 Sloan Research Fellowship in Chemistry and the American Chemical Society’s (ACS) Sustainable Chemistry Lectureship Award in 2024. Hatzell serves as a senior editor of the ACS Energy Letters journal.

Before her role at Georgia Tech, she was a postdoctoral researcher in the Department of Materials Science and Engineering at the University of Illinois Urbana-Champaign. She holds a Ph.D. in mechanical engineering from Penn State University. 

“I’m looking forward to working with the SEI staff and faculty to continue to grow Georgia Tech’s impact on the rapidly changing energy sector,” says Hatzell.  

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