Stratified thermal energy storage model with constant layer volume for predictive control — Formulation, comparison, and empirical validation

Daniel Zinsmeister, Peter Tzscheutschler, Vedran S. Perić and Christoph Goebel

Renewable Energy, 2023, vol. 219, issue P2

Abstract: Recent developments in heating systems have witnessed a significant increase of heat pumps with a highly temperature-dependent efficiency. Optimal real-time operation of these heating systems with predictive control requires a thorough understanding and modeling of the internal temperature distribution of the associated thermal energy storage. At the same time, the thermal energy storage models need to be sufficiently simple to ensure computational tractability in real-time predictive control. Therefore, this article presents a stratified thermal energy storage model with constant layer volume and variable temperature suitable for real-time predictive control. The model employs a novel formulation with quadratic or simpler constraints which enable high accuracy at low computation burden. The proposed model is validated experimentally and compared with other models available in literature. The results show that the proposed stratified thermal energy storage model represents the real-world behavior of a thermal energy storage with great accuracy, while reducing the required computational burden as compared to other models for real-time operation and control. A case study further demonstrates that the increased accuracy of the proposed new model leads to cost and energy savings for the operator.

Keywords: Thermal energy storage; Model predictive control; Optimization; Energy management system; Experimental validation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:219:y:2023:i:p2:s096014812301426x

DOI: 10.1016/j.renene.2023.119511

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