Sambuddha Chakrabarti

Professional Research Specialist

Sambuddha Chakrabarti (Sam) is a researcher focused on Mathematical Optimization, Operations Research (OR), Machine Learning, Automatic Controls Theory, and Electric Power System Network.

He joined the ZERO Lab at Andlinger Center in mid 2020, where he focuses on the development of the open-source GenX electricity system planning model and other research endeavors. He holds PhD (grad: August, 2017) & MSE (grad: August, 2010) from the Department of Electrical & Computer Engineering of The University of Texas at Austin (specializing in Energy System/Power System) & has experience in research industry (academia and national lab.), academia (teaching), electrical manufacturing industry, power system generation planning, & electricity trading since July, 2006 to present.Before joining Princeton University he was a postdoctoral researcher with the National Renewable Energy Laboratory (NREL) at Golden, CO. He also interned at Pacific Northwest National Laboratory (PNNL), Lower Colorado River Authority (LCRA), and Trailstone Energy Commodities Trading as a graduate student. He regularly serves as peer reviewer at several high impact journals, like IEEE Transactions (Power Systems, Smart Grid, Energy Market-Policy-Regulations), Elsevier Science Direct, MDPI, Nature-Energy among others. At ZERO Lab, he coordinates the development and hosting of the GenX open-source capacity expansion planning model and works on answering the research questions pertaining to next generation energy system models.

Publications

GenX.jl: a Configurable Power System Capacity Expansion Model for Studying Low-Carbon Energy Futures

Software, 2021

Aneesha Manocha, Filippo Pecci, Gabriel Mantegna, Jacob Schwartz, Jesse Jenkins, Luca Bonaldo, Maya Mutic, Neha Patankar, Qian Luo, Qingyu Xu, Sambuddha Chakrabarti, Wilson Ricks

github.com/GenXProject/GenX.jl

GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape. GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints. GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning.

Modeling the operational flexibility of natural gas combined cycle power plants coupled with flexible carbon capture and storage via solvent storage and flexible regeneration

International Journal of Greenhouse Gas Control, 2022

Fangwei Cheng, Jesse Jenkins, Neha Patankar, Sambuddha Chakrabarti

10.1016/j.ijggc.2022.103686

In electricity systems with many variable renewables, flexible operation of natural gas combined cycle (NGCC) power plants with carbon capture and sequestration (CCS) can increase their economic value. This study evaluates NGCC-CCS plants with solvent storage for such operation, using a modular modeling framework for accuracy and efficiency. The model divides NGCC-CCS plants into subcomponents, applying linear constraints for energy and mass balances, and addressing unit commitment (UC) constraints in thermal power plants. This approach employs linear relaxation of UC decision variables alongside a generator clustering method for flexible CCS modeling. Integrated into a power system model, it shows the hourly operations of NGCC-CCS and impacts on system performance. The results indicate faster computational times than traditional binary UC methods, with minimal errors. Flexible NGCC-CCS plants reduce operating costs, especially during peak demand, proving beneficial in grids with increasing renewable energy.

Evaluating the Operational Flexibility of Natural Gas Combined Cycle Power Plants Coupled with Flexible Carbon Capture and Storage via Solvent Storage and Flexible Regeneration

I.J. Greenhouse Gas Control, 2022

Fangwei Cheng, Jesse Jenkins, Neha Patankar, Sambuddha Chakrabarti

10.1016/j.ijggc.2022.103686

In electricity systems with high variable renewable energy, flexible operations of natural gas combined cycle (NGCC) power plants with carbon capture and sequestration (CCS) can enhance economic value. A new model efficiently represents these flexible NGCC-CCS plants. It dissects plants into subcomponents with linear constraints for energy and mass balances, and simplifies complex unit commitment (UC) constraints. This study explores linear relaxation of discrete UC variables with a generator clustering method, integrating this into a power system model. A case study shows the hourly operations of NGCC-CCS components and their impacts on environmental and economic performance. The findings reveal that linear relaxation with generator clustering significantly speeds up runtime (18–527 times faster) and minimizes errors. The model demonstrates that flexible NGCC-CCS plants reduce power system operating costs by adjusting output during peak demand, indicating their increased relevance in future grids dominated by renewable resources.