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Coordination of Large-Scale Converter-Based Resources
1. Distributed Data-Driven Optimal Control
To enable coordinated control of large-scale converter-based energy resources in real-time, we develop distributed data-driven optimal control algorithms based on feedback optimization. By leveraging the problem structures and using real-time measurement as system feedback, our control algorithms do not need the system model information (model-free) and are implemented in a fully distributed fashion, which only needs local measurement, local computation, and local communication with neighbors. Moreover, our algorithms achieve theoretical performance guarantees of optimality, safety, and stability.
Featured Publications:
[1] Xiaoyang Wang, Xin Chen, “Distributed Coordination of Grid-Forming and Grid-Following Inverters for Optimal Frequency Control in Power Systems“, arXiv:2411.12682, 2025.
[2] Xin Chen, Jorge I. Poveda, Na Li, “Continuous-Time Zeroth-Order Dynamics with Projection Maps: Model-Free Feedback Optimization with Safety Guarantees”, IEEE Transactions on Automatic Control, vol. 70, no. 8, pp. 5005-5020, Aug. 2025.
[3] Xin Chen, Changhong Zhao, Na Li, “Distributed Automatic Load-frequency Control with Optimality in Power Systems,” IEEE Transactions on Control of Network Systems, vol. 8, no. 1, pp. 307-318, March 2021.
[4] Xin Chen, Jorge I. Poveda, Na Li, “Safe Model-Free Optimal Voltage Control via Continuous-Time Zeroth-Order Methods”, 60th IEEE Conference on Decision and Control (CDC), Austin, Texas, USA, 2021. (Outstanding Student Paper Award) [Full Version]
[5] Xin Chen, Changhong Zhao, Na Li, “Distributed Automatic Load-frequency Control with Optimality in Power Systems,” 2018 IEEE Conference on Control Technology and Applications (CCTA), Copenhagen, pp. 24-31, 2018. (Best Student Paper Award Finalist)
2. Power Flexibility Aggregation
Multi-level Hierarchical Aggregation for Scalable Coordination of DERs
To enable scalable coordination, power flexibility aggregation is to model and qualify the aggregate flexibility of large-scale networked distributed energy resources (DERs) at the interconnection point, such as the substation interface. By reporting this concise and compact feasible region, the aggregated system can actively participate in the operation and control of higher-level systems, acting as a virtual power plant (VPP).
Featured Publications:
[1] Xin Chen, Na Li, “Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation”, IEEE Transactions on Smart Grid, vol. 12, no. 5, pp. 3954-3965, Sept. 2021.
[2] Xin Chen, Emiliano Dall’Anese, Changhong Zhao, Na Li, “Aggregate Power Flexibility in Unbalanced Distribution Systems,” IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 258-269, Jan. 2020.
[3] Shengyi Wang, Liang Du, Xin Chen, “Bayesian Active Learning-Based Soft Data Space Calibration for System-Wise Aggregate Flexibility Characterization“, IEEE Transactions on Smart Grid, vol. 16, no. 4, pp. 3017-3029, July 2025.
[4] Shengyi Wang, Liang Du, Xin Chen, “Cost-Aware Inner-Ball Represented Economic Flexibility Characterization for Distribution Systems Under Uncertainties”, IEEE PES General Meeting, Austin, TX, USA, 2025. (Best Paper Award)
3. Human-in-the-Loop Decision-Making for Demand Response
Online User Learning and Distributed Load Control
For privately user-owned distributed energy resources (DERs), such as residential heating & cooling facilities and electric vehicles, the aggregator can indirectly adjust the power consumption or generation of these DERs via demand response (DR) programs. It is essential for the aggregator to understand end-users’ preferences and behaviors to make optimal control decisions that optimize DR performance. To address the challenge of unknown and uncertain end-users' behaviors, we propose learning their behaviors through online interactions and observations. In particular, advanced Multi-Armed Bandits (MABs) approaches, such as contextual restless MABs, are developed to achieve this goal and enable scalable user learning and load control decisions.
Featured Publications:
[1] Xin Chen, I-hong Hou, “Contextual Restless Multi-Armed Bandits with Application to Demand Response Decision-Making“, IEEE Conference on Decision and Control (CDC), Milan, Italy, 2024.
[2] Xin Chen, Yingying Li, Jun Shimada, Na Li, “Online Learning and Distributed Control for Residential Demand Response”, IEEE Transactions on Smart Grid, vol. 12, no. 6, pp. 4843-4853, Nov. 2021.
[3] Xin Chen, Yutong Nie, Na Li, “Online Residential Demand Response via Contextual Multi-Armed Bandits,” IEEE Control Systems Letters, vol. 5, no. 2, pp. 433-438, April 2021.
Invited Talks and Slides
UNIFI_Distributed Data-Driven Coordinated IBR Control_Xin Chen.pdfContextual_Restless_Bandits_DR_Xin Chen.pdfPage updated
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