Time-Varying Model Predictive Control of a Reversible-SOC Energy-Storage Plant Based on the Linear Parameter-Varying Method

Xuetao Xing, Jin Lin*, Nigel Brandon, Aayan Banerjee, Yonghua Song

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Hydrogen conversion plants based on reversible solid oxide cells (rSOCs) provide a potential solution for large-scale energy storage to facilitate renewable integration. When operating an rSOC plant, the security and performance should be monitored at all times, including the steady-state periods and the transient processes connecting them. Model predictive control (MPC) is an appropriate method for this problem. This paper presents a comprehensive rSOC plant model to describe the multiscale dynamics (such as temperature and mass flows) under both fuel cell and electrolysis modes. Then, a time-varying MPC strategy based on a linear parameter-varying prediction model is proposed to provide fast control to the nonlinear rSOC plant. A numerical case is simulated to validate the effects of the proposed MPC strategy. The results suggest that the automatic coordination of actuators ensures consistent stack security, improves both short-term tracking and long-term production performance, and ultimately benefits the plant economically in practical operation.

Original languageEnglish
Article number8782575
Pages (from-to)1589-1600
Number of pages12
JournalIEEE Transactions on Sustainable Energy
Volume11
Issue number3
Early online date31 Jul 2019
DOIs
Publication statusPublished - Jul 2020
Externally publishedYes

Keywords

  • linear parameter-varying
  • model predictive control
  • Reversible solid oxide cell

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