Modulated Model Predictive Control for Dynamic Stabilization of DC Microgrid

Minrui Leng, Changming Zheng, Tomislav Dragicevic, Guohua Zhou, Frede Blaabjerg, Jose Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The majority of end-users in DC microgrid (MG) are electronic loads which behave as constant power load (CPL) when tightly controlled and may cause system instability. This paper proposes a stabilization method for DC MG by using the modulated model predictive control. First, a new cost function is defined, which includes the stabilization term of DC link. Then, the optimal voltage vector is derived by minimizing the cost function. After that, the optimal voltage vector is synthesized by the space vector modulation (SVM), resulting in low harmonics. Finally, simulation results are presented to verify the proposed strategy. It indicates that the proposed method can stabilize the DC MG, without implementing any additional active or passive components.

Original languageEnglish
Title of host publication2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages527-530
Number of pages4
ISBN (Electronic)9781728169903
DOIs
Publication statusPublished - 28 Sep 2020
Event11th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020 - Dubrovnik, Croatia
Duration: 28 Sep 20201 Oct 2020

Publication series

Name2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020

Conference

Conference11th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2020
Country/TerritoryCroatia
CityDubrovnik
Period28/09/201/10/20

Keywords

  • constant power load
  • DC microgrid
  • model predictive control
  • stabilization

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

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