Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints

Xiaogang Dong, Jinqiang Gan, Hao Wu, Changchang Deng, Sisheng Liu and Chaolong Song
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Xiaogang Dong: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Jinqiang Gan: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Hao Wu: School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Changchang Deng: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Sisheng Liu: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Chaolong Song: School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China

Energies, 2022, vol. 15, issue 3, 1-16

Abstract: In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of observer and reducing the control complexity. In addition, the Zeno behavior is excluded through detailed analysis. Furthermore, the stability of the algorithm is verified by theoretical derivation of Lyapunov. Finally, the effectiveness of the algorithm is proved by simulation.

Keywords: AC microgrids; model predictive control; self-triggered; physical and communication state constraints (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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