Skip to main navigation Skip to search Skip to main content

Model Predictive Control for Current Sharing and Voltage Balancing in DC Microgrids

  • SUNY Buffalo

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

1 Scopus citations

Abstract

In this paper, a Model Predictive Control (MPC) technique is presented for the control of energy sources in a dc microgrid. Proportional current sharing is achieved, where the current of each source is shared appropriately by the weight terms. Similarly, a cost function is presented to achieve average voltage of all the source nodes in the network. In addition, a Luenberger observer is designed to estimate the unknown states and disturbances. The centralized MPC optimization problem is then placed in quadratic programming form and solved using the mpcqp solver, which allows for code generation and real time implementation using an embedded system. A four node dc microgrid model is used as a case study. Real-time simulation using Opal RT is shown demonstrating the feasibility of online implementation of the controller.

Original languageEnglish
Title of host publication2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1040-1045
Number of pages6
ISBN (Electronic)9781728151359
DOIs
StatePublished - 2021
Event13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canada
Duration: Oct 10 2021Oct 14 2021

Publication series

Name2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

Conference

Conference13th IEEE Energy Conversion Congress and Exposition, ECCE 2021
Country/TerritoryCanada
CityVirtual, Online
Period10/10/2110/14/21

Keywords

  • dc microgrids
  • Luenberger observer
  • model predictive control
  • proportional current sharing
  • voltage balancing

Fingerprint

Dive into the research topics of 'Model Predictive Control for Current Sharing and Voltage Balancing in DC Microgrids'. Together they form a unique fingerprint.

Cite this