Prof. Xin Chen's Research Group

 

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[Our group is seeking highly self-motivated students at all levels with research passion and a strong mathematical background to join us. In particular, we have multiple fully funded Ph.D. positions available, starting in Spring 2025 and Fall 2025. Students with backgrounds in power systems, control theory, machine learning, optimization, mathematics, and related fields are encouraged to apply. For more details, click here]

Dr. Xin Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Texas A&M University. Dr. Chen directs the Smart Power, Energy and Decision-making (SPEED) Lab 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.

Dr. Chen received the Ph.D. degree in electrical engineering from Harvard University (working with Prof. Na Li), the master’s degree in electrical engineering and two bachelor’s degrees in engineering and economics from Tsinghua University. Prior to joining TAMU, Dr. Chen was a Postdoctoral Associate affiliated with MIT Energy Initiative at Massachusetts Institute of Technology, working with Prof. Andy Sun. Dr. Chen is a recipient of the IEEE PES Outstanding Doctoral Dissertation Award, IEEE Transactions on Smart Grid Top-5 Papers, the Best Research Award at the 2023 IEEE PES Grid Edge Conference, the Outstanding Student Paper Award at the 2021 IEEE Conference on Decision and Control,  the Best Student Paper Award Finalist at the 2018 IEEE Conference on Control Technology and Applications, and the Best Conference Paper Award at the 2016 IEEE PES General Meeting.

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News

[11/2024] Our review and vision paper on carbon-electricity nexus titled “Towards Carbon-Free Electricity: A Comprehensive Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization” has been accepted to the IET Energy Conversion and Economics.

[10/2024] Dr. Xin Chen was invited to present a talk “Distributed Data-Driven Coordination of IBRs for Grid-Level Optimal Voltage Control” at the NSF-supported workshop “Enabling Cyber-Resilient Distribution Systems with Edge Inverter-Based Resources (IBR)”, hosted by MIT and West Virginia University at Cambridge.

[09/2024] Dr. Xin Chen was invited to present a talk “Distributed Data-Driven Coordinated Control for IBR-Rich Power Systems” at the Universal Interoperability for Grid-Forming Inverters (UNIFI) Consortium Webinar.

[09/2024] Dr. Xin Chen was invited to present a talk “Online Learning for Residential Demand Response via Advanced Multi-Armed Bandits” at the 7th Workshop on Autonomous Energy Systems, hosted the National Renewable Energy Laboratory (NREL).

[07/2024] Our paper “Continuous-Time Zeroth-Order Dynamics with Projection Maps: Model-Free Feedback Optimization with Safety Guarantees” has been accepted to the IEEE Transactions on Automatic Control (TAC).

[07/2024] Our paper “Enhance Low-Carbon Power System Operation via Carbon-Aware Demand Response” has been accepted to the journal IET Energy Internet.

[07/2024] Our paper “Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making” has been accepted to the 2024 63rd IEEE Conference on Decision and Control (CDC).

[06/2024] Excited to share that our collaborative team at Texas A&M and Prairie View A&M has been awarded the Grid Resilience and Climate Change Impacts Analysis (GRACI) grant by the U.S. Department of Energy’s (DOE) Grid Deployment Office, for providing grid vulnerability assessments and risk planning guidance to state energy offices.

[04/2024] Dr. Xin Chen was honored to be interviewed by IEEE Control Systems Magazine about his Ph.D. research in control. Here is the interview article “Xin Chen [Ph.D.s in Control]“.

[04/2024] Check out our two new preprints: 1) “Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making” introduces a novel multi-armed bandits framework, Contextual Restless Bandits (CRB), that models both the global contextual influence and internal arm dynamics; 2) “Enhance Low-Carbon Power System Operation via Carbon-Aware Demand Response” integrates the carbon-aware demand response mechanism into power dispatch to facilitate grid decarbonization by leveraging user-side power flexibility.

[12/2023] Our paper “Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges” has been selected as one of the IEEE Transactions on Smart Grid Top-5 Outstanding Papers in 2020-2022. Dr. Chen has also been invited to present this work at an upcoming IEEE webinar. Find details here.

[10/2023] Excited to share that our team “TIM-GO” ranked the second place in the Leaderboard of The Grid Optimization (GO) Competition Challenge 3 hosted by ARPA-E DOE, with the total prize of $595k.

[10/2023] Dr. Xin Chen chaired the session “Advanced Learning and Optimization for Carbon-Neutral Electricity” in the 2023 INFORMS Annual Meeting, Phoenix, Arizona, USA. See here for the topics and info of the session.

[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.