Tube-based model predictive control approach for real-time operation of energy storage system

Cheng Lyu, Youwei Jia, Zhao Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Energy storage systems are widely used to complement high renewables and assist in supply-demand balance in smart grids. In practice, lithium-ion battery becomes the most popular due to its relatively long life cycles. However, there are two main challenges for batteries to participate in the real-time operation: 1) the change of battery energy level is across-time coupled; 2) uncertainties are unavoidably arisen in the forecasting process for renewable generation. In this paper, a segmental degradation cost model is proposed for real-time management of lithium-ion batteries. In particular, a tube-based model predictive control (MPC) approach is newly proposed in accommodating the real-time operation of energy storage system. Numerical simulation results demonstrate the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages493-497
Number of pages5
ISBN (Electronic)9781728185507
DOIs
Publication statusPublished - Nov 2020
Event2020 International Conference on Smart Grids and Energy Systems, SGES 2020 - Virtual, Perth, Australia
Duration: 23 Nov 202026 Nov 2020

Publication series

NameProceedings - 2020 International Conference on Smart Grids and Energy Systems, SGES 2020

Conference

Conference2020 International Conference on Smart Grids and Energy Systems, SGES 2020
Country/TerritoryAustralia
CityVirtual, Perth
Period23/11/2026/11/20

Keywords

  • Battery energy storage system
  • Real time operation
  • Tube-based model predictive control

ASJC Scopus subject areas

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Transportation

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