Jubair Yusuf

Senior Member of the Technical Staff

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Senior Member of the Technical Staff

jyusuf@sandia.gov

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(505) 525-7186

Biography

Jubair Yusuf is a Senior Member of the Technical Staff in the Electric Power Systems Research Department at Sandia National Laboratories. He received the Ph.D. degree in Electrical Engineering with a specialization in Electric Power and Energy Systems from the University of California, Riverside, USA, where he worked in the Sustainable Integrated Grid Initiative (SIGI) group. Before joining Sandia, he was an intern at the National Renewable Energy Laboratory, where he worked on distribution feeder modeling for Electric Vehicles (EV) grid integration. His research interests and expertise include EV load modeling and grid integration, distribution system modeling, and applying both optimization and data driven approaches to grid modeling, study the integration of high EV penetrations and other distributed energy resources.

Education

  • Ph.D., Electrical Engineering, Emphasis in Electric Power and Energy Systems, University of California, Riverside
  • M.S., Electrical Engineering, Emphasis in Electric Power and Energy Systems, University of California, Riverside
  • B.S., Electrical and Electronic Engineering, Emphasis in Electric Power and Energy Systems, Bangladesh University of Engineering and Technology

Publications

Matthew Reno, Logan Blakely, Rodrigo Trevizan, Bethany Pena, Matthew Lave, Joseph Azzolini, Jubair Yusuf, Christian Jones, Alvaro Furlani Bastos, Rohit Chalamala, Mert Korkali, Chih-Che Sun, Jonathan Donadee, Emma Stewart, Vaibhav Donde, Jouni Peppanen, Miguel Hernandez, Jeremiah Deboever, Celso Rocha, Matthew Rylander, Piyapath Siratarnsophon, Santiago Grijalva, Samuel Talkington, Karl Mason, Sadegh Vejdan, Ahmad Khan, Jordan Mbeleg, Kavya Ashok, Deepak Divan, Feng Li, Francis Therrien, Patrick Jacques, Vittal Rao, Cody Francis, Nicholas Zaragoza, David Nordy, Jim Glass, Derek Holman, Tim Mannon, David Pinney, (2022). IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments https://doi.org/10.2172/1888157 Publication ID: 80226

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