Dharik Mallapragada
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Assistant Professor
Energy system decarbonization requires widespread adoption of variable renewable energy-based electricity supply and electrification of many end-uses. Both these strategies will increase the spatial and temporal variability in energy supply and demand that complicates system operations and necessitates technological and market innovations to ensure reliable and cost-effective supply of low-carbon electricity. At the same time, for end-uses where electrification is currently impractical, there is a need to identify flexible and scalable processes for enabling electricity use or alternative emissions reduction strategies that can complement grid decarbonization efforts.
The Sustainable energy Transitions (SET) group is interested in developing mathematical modeling approaches to analyze low-carbon technologies and their energy system integration under different policy and geographical contexts. The group’s research aims to create the knowledge and analytical tools necessary to support accelerated energy transitions in developed economies like the U.S. as well as emerging market and developing economy countries in the global south that are central to global climate mitigation efforts. We develop mathematical models for design and operations of processes and integrated energy systems. We use a wide range of computational techniques in our work including first-principle and data-driven modeling, formulating and solving mixed integer linear/nonlinear optimization models and data analytics for variability and uncertainty characterization.
Education
Indian Institute of Technology Madras, 2008
B.Tech, Chemical Engineering
Purdue University, 2013
M.S, Chemical Engineering
PhD, Chemical Engineering
Recent Publications
- Khorramfar, R., Mallapragada, D., and Amin, S. (2024). Electric-gas infrastructure planning for deep decarbonization of energy systems. Applied Energy 354, 122176. 10.1016/j.apenergy.2023.122176.
- Schittekatte, T., Mallapragada, D., Joskow, P.L., and Schmalensee, R. (2024). Electricity Retail Rate Design in a Decarbonizing Economy: An Analysis of Time-of-use and Critical Peak Pricing. The Energy Journal 45, 25–56. 10.5547/01956574.45.3.tsch.
- Schittekatte, T., Mallapragada, D., Joskow, P.L., and Schmalensee, R. (2023). Reforming retail electricity rates to facilitate economy-wide decarbonization. Joule 7, 831–836. 10.1016/j.joule.2023.03.012.
- Riedmayer, R., Paren, B.A., Schofield, L., Shao-Horn, Y., and Mallapragada, D. (2023). Proton Exchange Membrane Electrolysis Performance Targets for Achieving 2050 Expansion Goals Constrained by Iridium Supply. Energy Fuels 37, 8614–8623. 10.1021/acs.energyfuels.3c01473.
- Zang, G., Graham, E.J., and Mallapragada, D. (2023). H2 production through natural gas reforming and carbon capture: A techno-economic and life cycle analysis comparison. International Journal of Hydrogen Energy. 10.1016/j.ijhydene.2023.09.230.
- Barbar, M., Mallapragada, D.S., and Stoner, R.J. (2023). Impact of demand growth on decarbonizing India’s electricity sector and the role for energy storage. Energy and Climate Change 4, 100098. 10.1016/j.egycc.2023.100098.
- Sheha, M., Graham, E.J., Gençer, E., Mallapragada, D., Herzog, H., Cross, P., Custer, J., Goff, A., and Cormier, I. (2024). Techno-economic analysis of a combined power plant CO2 capture and direct air capture concept for flexible power plant operation. Computers & Chemical Engineering 180, 108472. 10.1016/j.compchemeng.2023.108472.
- Mallapragada, D.S., Dvorkin, Y., Modestino, M.A., Esposito, D.V., Smith, W.A., Hodge, B.-M., Harold, M.P., Donnelly, V.M., Nuz, A., Bloomquist, C., et al. (2023). Decarbonization of the chemical industry through electrification: Barriers and opportunities. Joule 7, 23–41. 10.1016/j.joule.2022.12.008.
In the News
- Unlocking hydrogen’s potential for renewable energy storage, transport (Lehigh)
- This key chemical is super dirty to make. Can an electric furnace help? (Canary Media)
- To decarbonize the chemical industry, electrify it (MIT News)
- How the hydrogen revolution can help save the planet — and how it can’t (Nature)
- As energy costs soar, homeowners are seeking ways to stay warm without breaking the bank (Boston Globe)
- In the transition to renewables, energy storage is a hot topic (MarketPlace)
- Meet the research scientists behind MITEI’s Electric Power Systems Center (MIT News)
Research News
New Study Highlights the Weather-Driven Costs of 24/7 Clean Power Matching
As demand for electricity surges from artificial intelligence, data centers, and other new technologies, companies are increasingly pledging to procure carbon-free electricity to mitigate emissions associated with their electricity use. While quantifying system-wide emissions is relatively straightforward, the complex nature of power grid operations makes it more challenging to ascertain the emissions impacts of a single consumer that also procures its own clean energy. This challenge is further compounded by clean energy sources like solar and wind, whose hour to hour generation can vary drastically, depending on weather patterns.
A new study by researchers at NYU Tandon and the Massachusetts Institute of Technology evaluated the system impacts of so-called 24/7 or hourly matching, involving matching grid electricity consumption with generation from procured clean electricity, a strategy of growing corporate and regulatory interest. By incorporating multiple years of weather variability into sophisticated power-system models, the researchers found that procuring clean electricity to match consumption every hour of the day can deliver emissions benefits, but at higher cost than previous studies suggested. The work also indicates that in regions already pursuing aggressive renewable energy policies, less strict approaches may achieve nearly the same climate benefits for considerably less money.
The study, published in Environmental Science & Technology, focuses on electricity-based production of hydrogen, a pathway of growing interest to decarbonize difficult-to-electrify segments of the economy like heavy industry. Recently, governments in both the United States and Europe have proposed or implemented rules requiring producers to match their electricity use with generation from newly built renewable energy resources, initially on an annual basis and eventually on an hourly basis. Beyond hydrogen, the findings apply broadly to other large electricity consumers, including data centers, and speak directly to ongoing revisions of the Greenhouse Gas Protocol's Scope 2 rules, the standard many companies use to account for and report the emissions tied to their electricity use, where hourly matching is a central proposal under debate.
Until now, however, most analyses on hourly matching have relied on a single year of weather data to characterize wind and solar resource variability. In reality, wind and solar resources fluctuate significantly from year to year.
"We wanted to understand how real-world, inter-annual weather variability changes the economics and emissions of clean electricity procurement," said senior author Dharik S. Mallapragada, associate professor of chemical and biomolecular engineering at NYU Tandon. "When projects are expected to operate for decades, designing them around a single weather year can paint an overly optimistic picture of both cost and performance."
To explore that question, the researchers modeled a grid-connected hydrogen facility operating in the Texas power grid, one of the nation's largest renewable energy markets. They optimized investments across nine representative years of historical wind and solar conditions, both individually and collectively, and then tested those designs for their robustness against additional weather scenarios.
The team's model consistently showed that hourly matching costs more than annual matching and that the cost premium is sensitive to the weather year, varying between ranging from $0.68 to $1.18/kg of H2 produced. For context, the average cost of fossil H2 production is around $1/kg. The team also showed that such single weather-year based investment plans can lead to shortfalls in clean electricity supply across multiple hours of the year when tested against additional weather scenarios not included in the planning analysis.
The cost of clean electricity procurement to be robust to inter-annual weather variations was found to lead to a still higher cost premium, of $1.29 per kilogram compared with annual matching. This cost premium stems from oversizing the capacity of wind and solar farms to manage shortfalls in supply as well as installing larger electrolyzers capable of ramping production up and down, along with extensive hydrogen storage to buffer periods when renewable generation falls short.
The researchers also examined ways to reduce costs of 24/7 matching without sacrificing most of the environmental benefits. Allowing hydrogen producers to match80 to 90 percent of their hourly electricity needs with clean power lowered costs while retaining much of the emissions benefits achieved under full compliance. Likewise, if hydrogen production operates within electricity grids already governed by strong renewable portfolio standards, annual matching performed nearly as well as hourly matching from an emissions standpoint, but at much lower costs. A further option, with similar emissions outcomes at a comparable or lower cost, is to fold new electricity demand directly into those existing renewable portfolio standards rather than layering separate, granular, matching requirements on top. These findings speak directly to ongoing debates among governments and corporations over the role of "24/7 matching" in voluntary and regulatory emissions reporting.
Michael A. Giovanniello, Dharik S. Mallapragada; Emissions and Cost Trade-Offs of Time-Matched Clean Electricity Procurement under Interannual Weather Variability: A Case Study of Hydrogen Production. Environ. Sci. Technol. 21 July 2026; 60 (28): 19854–19865. https://doi.org/10.1021/acs.est.6c00988
New Method of Data Center Cooling Could Dramatically Decrease Electricity Use
Data centers — the warehouse-sized buildings that store our photos, stream our movies and train artificial intelligence — are voracious consumers of electricity. A surprisingly large share of that power never reaches a microchip. Instead, it is spent on cooling, hauling away the heat generated by millions of tightly packed servers.
As data centers proliferate thanks to the AI boom, their electricity needs are colliding with a grid already under strain. One solution is to rethink the basics of cooling. In a new study, researchers at NYU Tandon explore an alternative solution: use waste heat from nearby factories to cool data centers, by storing that heat in a material that can later deliver cooling on demand.
“While the electricity needs for data centers are still a small slice of the total U.S. electricity market, it is rapidly growing,” says Dharik Mallapragada, Assistant Professor of Chemical and Biomolecular Engineering and lead author of the paper. “This is an opportunity to ‘bend the curve’ and aim for a much more sustainable future, in a way that is beneficial to everyone involved.”
Thermal batteries
At the heart of the concept are minerals called zeolite. Zeolites are crystalline materials riddled with microscopic pores, giving them a remarkable ability to soak up water vapor. When a dry zeolite encounters water vapor, it adsorbs the vapor and releases heat. When the zeolite is heated to sufficiently high temperatures, it releases the water again, resetting the cycle.
Importantly, zeolites are inexpensive materials that are already in use for a wide range of applications, including water treatment and oil refining. “Zeolite and its interaction with water can be used for storing thermal energy”, says Assistant Professor Pavel Kots, a co-author on the study and an expert in zeolite synthesis and characterization. At an industrial facility — such as a chemical plant or refinery — low- to medium-temperature waste heat (below about 200 degrees Celsius) is used to “charge” the thermal battery by drying the zeolite. The water driven off is condensed and recovered. The charged zeolite is then transported, by truck or rail, to a data center.
Once on site, the process runs in reverse. Warm air or other coolants (e.g., water) from the server room help evaporate water, producing a cooling effect. The water vapor is adsorbed by the dried zeolite, which effectively acts as a heat sink. Crucially, this adsorption process can replace the electricity-hungry compression chillers that dominate today’s data center cooling systems.
Unlike typical heat storage methods, zeolite-based storage does not slowly lose its energy over time. The thermal energy remains locked in the material until the water is reintroduced. That makes it suitable not only for long-duration storage but also for transport over tens of kilometers.
Big energy savings, modest trade-offs
Using detailed thermodynamic modeling, the NYU team, which included Kots, Mallapragada, and postdoctoral researcher Gilvan Farias Neto, compared their proposed system with a conventional setup: a data center cooled by a compression chiller and an industrial facility rejecting waste heat through cooling towers.
The results are striking. Across a range of operating conditions, the team estimated that the proposed approach can reduce total electricity used by the data center for cooling and the industrial facility by more than 75 percent. For the data center alone, electricity consumption for cooling can be reduced by as much as 86 percent. In energy efficiency terms, this translates into a 12% improvement in power usage effectiveness, a key metric in the data center industry.
Water use tells a more nuanced story. The combined system consumes somewhat more water overall — roughly 15 to 25 percent more — because evaporation is central to the cooling process. But this increase masks an important detail: the industrial facility itself sees a dramatic reduction in water use, since much of its waste heat is diverted into charging the thermal batteries rather than being dumped through cooling towers. Water released during zeolite charging can also be reused on site, partially closing the loop. The analysis did not consider the changes in indirect water use, i.e. associated with electricity generation, for the facility, which could partially or fully offset increases in direct water use, depending on the make-up of the electricity supply.
In order for this set up to work, data centers need to be fairly close to industrial facilities. To assess the scalability of their approach, the researchers conducted a geospatial analysis of U.S. facilities. The median distance between data centers and the 10 nearest industrial sites turned out to be just 57 kilometers.
Even after accounting for the energy needed to haul tons of zeolite back and forth — assuming modern electric trucks — the system still delivers net electricity savings in many scenarios, sometimes exceeding 40 percent. Rail transport could reduce the energy penalty further.
The proposed system is still at the modeling stage, and many engineering challenges remain. Zeolite beds must be designed for durability, rapid heat transfer and repeated cycling. Coordinating operations between data centers and industrial partners will require new business models. The research team has begun speaking with several industry leaders about the possibility of scaling this solution up.
Still, the idea highlights an under-appreciated truth: in an energy-hungry digital economy, waste heat can be monetized as a valuable resource. By reimagining cooling as a problem of thermal logistics rather than electrical demand, zeolite-based thermal batteries could help data centers grow without overheating the grid.
Farias, Kots, Mallapragada(2026) Zeolite Based Thermal Energy Storage to Leverage Industrial Waste Heat for Data Center Cooling, Chem Rxiv https://doi.org/10.26434/chemrxiv-2026-28wv2.
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