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Distributed Model-Based Predictive Secondary Control for Hybrid AC/DC Microgrids

Rute-Luengo, Erwin; Navas-Fonseca, Alex; Gomez, Juan S.; Espina, Enrique; Burgos-Mellado, Claudio; Saez, Doris; Sumner, Mark; Munoz-Carpintero, Diego

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Authors

Erwin Rute-Luengo

Alex Navas-Fonseca

Juan S. Gomez

Enrique Espina

Claudio Burgos-Mellado

Doris Saez

MARK SUMNER MARK.SUMNER@NOTTINGHAM.AC.UK
Professor of Electrical Energy Systems

Diego Munoz-Carpintero



Abstract

This paper presents a novel scheme based on distributed model-based predictive control for the secondary level control of hybrid AC/DC microgrids. Prediction models based on droop control and power transfer equations are proposed to characterize the generators in both the AC and DC sub-microgrids, whereas power balance constraints are used to predict the behavior of interlinking converters. The operational constraints (such as powers and control action limits) are included in all the formulations. Experimental results validate the proposed scheme for the following cases: (i) load changes, working within operating constraints, (ii) managing frequency regulation in the AC sub-microgrid, voltage regulation in the DC sub-microgrid and global power consensus in the whole hybrid microgrid, and (iii) maintaining the microgrid performance in the presence of communication malfunction while ensuring that plug-and-play capability is preserved.

Journal Article Type Article
Acceptance Date Feb 22, 2022
Online Publication Date Mar 8, 2022
Publication Date 2023-02
Deposit Date May 13, 2022
Publicly Available Date May 13, 2022
Journal IEEE Journal of Emerging and Selected Topics in Power Electronics
Print ISSN 2168-6777
Electronic ISSN 2168-6785
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 11
Issue 1
Pages 627-642
DOI https://doi.org/10.1109/JESTPE.2022.3157979
Keywords Electrical and Electronic Engineering; Energy Engineering and Power Technology
Public URL https://nottingham-repository.worktribe.com/output/8048086
Publisher URL https://ieeexplore.ieee.org/document/9730874
Additional Information © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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