Welcome to the Smart Power, Energy and Decision-making (SPEED) Lab led by Dr. Xin Chen at TAMU ECE. The research of SPEED lies in the intersection of control, machine/reinforcement learning, and optimization for human-cyber-physical systems, with particular applications to sustainable power and energy systems. The SPEED lab aims to develop fundamental theories, scalable decision-making algorithms, and practically applicable tools to advance the intelligence, resilience, and sustainability of modern power and energy systems.
[Highlight] Our lab has multiple openings for fully funded Ph.D. positions starting in Spring 2024 or Fall 2024. We are looking for self-motivated students with strong mathematical backgrounds and interests in smart power and energy systems.
- Please contact Dr. Xin Chen at “email@example.com” with the subject line “Prospective Ph.D. Student”, enclosing CV, transcript, and a short description of research interests, if you are interested in joining our lab. See here for more details.
- See here for the instructions of Graduate Program Applications.
Dr. Xin Chen will chair a session “Advanced Learning and Optimization for Carbon-Neutral Electricity” in the 2023 INFORMS Annual Meeting, Oct. 15-18, Phoenix, Arizona, USA. See here for the info of session time and speakers.[08/2023] Check our new preprint “Towards Carbon-Free Electricity: A Comprehensive Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization”. This paper establishes a pioneering comprehensive flow-based framework for carbon research and development in the electric power sector, including carbon accounting, decarbonization decision-making, and carbon-electricity market. [08/2023] Check our new preprint “Carbon-Aware Optimal Power FLow”, which integrates carbon flow into optimal power flow (OPF) to enable the optimal management of both energy and carbon emissions in power systems planning and operation. [05/2023] Dr. Xin Chen’s Ph.D. dissertation was selected as one of the four IEEE PES Outstanding Doctoral Dissertations (2020-2022) by IEEE PES PEEC. He also won the Best Research Award (one of two, out of over 100 participants) in IEEE PES Grid Edge Technologies Conference and Exposition 2023.