Search Results for author: Dharik Mallapragada

Found 7 papers, 1 papers with code

Repurposing Coal Power Plants into Thermal Energy Storage for Supporting Zero-carbon Data Centers

1 code implementation15 Feb 2024 Yifu Ding, Serena Patel, Dharik Mallapragada, Robert James Stoner

Coal power plants will need to be phased out and face stranded asset risks under the net-zero energy system transition.

An Integer Clustering Approach for Modeling Large-Scale EV Fleets with Guaranteed Performance

no code implementations3 Oct 2023 Sijia Geng, Thomas Lee, Dharik Mallapragada, Audun Botterud

Large-scale integration of electric vehicles (EVs) leads to a tighter integration between transportation and electric energy systems.

Clustering Computational Efficiency +1

Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification

no code implementations31 Aug 2023 Rahman Khorramfar, Morgan Santoni-Colvin, Saurabh Amin, Leslie K. Norford, Audun Botterud, Dharik Mallapragada

Applying the framework to study the U. S. New England region in 2050 across 20 weather scenarios, we find high electrification of the residential sector can increase sectoral peak and total electricity demands by up to 56-158% and 41-59% respectively relative to business-as-usual projections.

Electric-Gas Infrastructure Planning for Deep Decarbonization of Energy Systems

no code implementations28 Dec 2022 Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin

The transition to a deeply decarbonized energy system requires coordinated planning of infrastructure investments and operations serving multiple end-uses while considering technology and policy-enabled interactions across sectors.

Scheduling

Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints

no code implementations24 Sep 2022 Aron Brenner, Rahman Khorramfar, Dharik Mallapragada, Saurabh Amin

Specifically, we focus on efficiently extracting a set of representative days from power and NG data in respective networks and using this set to reduce the computational burden required to solve the GTEP.

Graph Representation Learning

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