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Volume 15
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Article

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

by
Xiaogang Dong
1,
Jinqiang Gan
1,*,
Hao Wu
2,
Changchang Deng
1,
Sisheng Liu
1 and
Chaolong Song
1
1
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
2
School of Chemistry and Chemical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Energies 2022, 15(3), 1170; https://doi.org/10.3390/en15031170
Submission received: 24 December 2021 / Revised: 23 January 2022 / Accepted: 30 January 2022 / Published: 5 February 2022
(This article belongs to the Special Issue Challenges and Research Trends of Distributed Control and Optimization)
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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 AC microgrids; model predictive control; self-triggered; physical and communication state constraints

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MDPI and ACS Style

Dong, X.; Gan, J.; Wu, H.; Deng, C.; Liu, S.; Song, C. Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints. Energies 2022, 15, 1170. https://doi.org/10.3390/en15031170

AMA Style

Dong X, Gan J, Wu H, Deng C, Liu S, Song C. Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints. Energies. 2022; 15(3):1170. https://doi.org/10.3390/en15031170

Chicago/Turabian Style

Dong, Xiaogang, Jinqiang Gan, Hao Wu, Changchang Deng, Sisheng Liu, and Chaolong Song. 2022. "Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints" Energies 15, no. 3: 1170. https://doi.org/10.3390/en15031170

APA Style

Dong, X., Gan, J., Wu, H., Deng, C., Liu, S., & Song, C. (2022). Self-Triggered Model Predictive Control of AC Microgrids with Physical and Communication State Constraints. Energies, 15(3), 1170. https://doi.org/10.3390/en15031170

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