Dynamic Clusters Supported Bi-Level Control System With Lower Communication Requirements for a Distribution Network With Distributed Battery and Photovoltaic Systems

• Published in: IEEE transactions on smart grid Vol. 15; no. 4; pp. 3824 - 3838
• Main Authors: Zhang, Runfan, Liu, Zixuan, Bie, Zhaohong, Hredzak, Branislav
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Batteries; Clusters; Control systems; Data transmission; distributed eventtriggered control; Distributed generation; distributed renewable generation; Distribution networks; Dynamic microgrid cluster; Electric potential; Electric power loss; Energy storage; energy storage systems; Event triggered control; Frequency control; Interactive control; Microgrids; optimal power flow; Power generation; Predictive control; Real time; receding horizon model predictive control; secondary control; State of charge; Storage systems; tertiary control; time delays; Time lag; Voltage; Voltage control
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2024.3379455

This paper proposes a bi-level control framework for dynamic microgrid clusters in a distribution network with distributed photovoltaic and battery storage systems. The proposed bi-level control framework comprises interactive secondary and tertiary level control systems. A distributed event-triggered mechanism is proposed for the secondary level control of each dynamic microgrid cluster to achieve frequency and voltage regulation and balancing of the state of charge of battery storage systems within the cluster. At the tertiary level, a receding horizon model predictive control is implemented to minimize transmission power losses and battery storage systems losses by providing optimal battery storage systems output powers and the voltage source converters output voltages, with a one-minute interval. The secondary level control is modified to implement the optimal solutions provided by the tertiary level model predictive control. Furthermore, the secondary level distributed event-triggered control minimizes unnecessary data transmission by introducing a time delay, resulting in a reduced communication burden. The proposed bi-level control framework is validated in real-time on a modified IEEE 13-node test feeder using RTDS with the tertiary level model predictive control solved on a computer via the hardware-in-loop method.