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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
JournalIEEE Journal of Emerging and Selected Topics in Power Electronics
DOIs
Publication statusAccepted/In press - 2022
Externally publishedYes

Keywords

  • Distributed Secondary Control
  • Frequency control
  • Generators
  • Hybrid AC/DC Microgrids
  • Hybrid power systems
  • Microgrids
  • Power electronics
  • Predictive Control
  • Predictive models
  • Voltage control

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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