Model Predictive Control Based Dual-Mode Controller for Multi-Source DC Microgrid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a DC microgrid based on solar PV array and battery storage is simulated and a dual-mode control system is developed for grid-connected and islanded modes. The developed dual-mode control approach is based on model predictive control (MPC). The control system is flexible to allow operating the microgrid under different modes that include islanded and grid-connected modes. The considered microgrid is modeled and simulated using MATLAB/Simulink software and different scenarios have been tested to verify the proposed principle.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages908-913
Number of pages6
ISBN (Electronic)9781728136660
DOIs
Publication statusPublished - 1 Jun 2019
Event28th IEEE International Symposium on Industrial Electronics, ISIE 2019 - Vancouver, Canada
Duration: 12 Jun 201914 Jun 2019

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2019-June

Conference

Conference28th IEEE International Symposium on Industrial Electronics, ISIE 2019
CountryCanada
CityVancouver
Period12/6/1914/6/19

    Fingerprint

Keywords

  • distributed generation
  • energy storage
  • Microgrid
  • model predictive control
  • power management
  • renewable energy

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Cite this

Bayhan, S., & Abu-Rub, H. (2019). Model Predictive Control Based Dual-Mode Controller for Multi-Source DC Microgrid. In Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019 (pp. 908-913). [8781391] (IEEE International Symposium on Industrial Electronics; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2019.8781391