Data-Driven Model Reduction of the Moving Boundary Heat Pump Dynamic Model

Ruihao Song, Guillaume Yon, Thomas Hamacher, Vedran S. Peric

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

Heat pump systems have the potential to be used as controllable load to compensate for the uncertainties in modern power systems. The moving boundary model for heat pumps is known for its accuracy and acceptable computational burden. However, this model is sometimes impractical because it requires detailed information on the mechanical structure of the internal loops and the thermal state of the refrigerant. A data-driven method based on the cascaded wiener model is proposed in this paper to simplify the moving boundary model. The proposed model is developed from an observation that the dynamic behavior of the heat pump is relatively linear over a large range of operation states, while the static behavior is very nonlinear. Through comparison of simulation results, the proposed model has close accuracy to the moving boundary model and can be a viable alternative for control design purposes.

Original languageEnglish
Title of host publication2022 IEEE Power and Energy Society General Meeting, PESGM 2022
PublisherIEEE Computer Society
Publication date2022
ISBN (Electronic)9781665408233
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE Power and Energy Society General Meeting, PESGM 2022 - Denver, United States
Duration: 17 Jul 202221 Jul 2022

Conference

Conference2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Country/TerritoryUnited States
CityDenver
Period17/07/202221/07/2022
SeriesIEEE Power and Energy Society General Meeting
Volume2022-July
ISSN1944-9925

Keywords

  • controllable load
  • data-driven modelling
  • dynamic modelling
  • heat pump
  • moving boundary model
  • wiener model

Fingerprint

Dive into the research topics of 'Data-Driven Model Reduction of the Moving Boundary Heat Pump Dynamic Model'. Together they form a unique fingerprint.

Cite this