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Experimental validation of a hybrid 1-D multi-node model of a hot water thermal energy storage tank

Author

Listed:
  • De la Cruz-Loredo, Iván
  • Zinsmeister, Daniel
  • Licklederer, Thomas
  • Ugalde-Loo, Carlos E.
  • Morales, Daniel A.
  • Bastida, Héctor
  • Perić, Vedran S.
  • Saleem, Arslan

Abstract

Hot water-based thermal energy storage (TES) tanks are extensively used in heating applications to provide operational flexibility. Simple yet effective one-dimensional (1-D) tank models are desirable to simulate and design efficient energy management systems. However, the standard multi-node modelling approach struggles to reproduce the dynamics of highly thermally stratified tanks due to their artificial numerical diffusion. In this paper, a novel 1-D multi-node modelling approach is introduced for accurately simulating water tanks with a high extent of thermal stratification. A non-linear, hybrid continuous–discrete time model able to capture the sudden temperature change within the tank is presented. The modelling approach was adopted to simulate a commercial TES tank, with the model being implemented in MATLAB/Simulink. Results from experimental tests were compared with simulation results, demonstrating that a hybrid continuous–discrete 12-node model accurately estimates the temperatures of the tank. It is also shown that the hybrid model avoids the numerical diffusion exhibited by standard multi-node models. This has been evidenced by the reduced root mean square and mean absolute errors exhibited by the hybrid model when compared with the experimental data.

Suggested Citation

  • De la Cruz-Loredo, Iván & Zinsmeister, Daniel & Licklederer, Thomas & Ugalde-Loo, Carlos E. & Morales, Daniel A. & Bastida, Héctor & Perić, Vedran S. & Saleem, Arslan, 2023. "Experimental validation of a hybrid 1-D multi-node model of a hot water thermal energy storage tank," Applied Energy, Elsevier, vol. 332(C).
    • Handle: RePEc:eee:appene:v:332:y:2023:i:c:s030626192201813x
    DOI: 10.1016/j.apenergy.2022.120556
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    1. Bastida, Hector & De la Cruz-Loredo, Ivan & Saikia, Pranaynil & Ugalde-Loo, Carlos E., 2024. "Discrete-time state-of-charge estimator for latent heat thermal energy storage units based on a recurrent neural network," Applied Energy, Elsevier, vol. 371(C).
    2. Yelizaveta Karlina & Yelnar Yerdesh & Amankeldy Toleukhanov & Yerzhan Belyayev & Hua Sheng Wang & Olivier Botella, 2024. "Numerical Simulation Study of Thermal Performance in Hot Water Storage Tanks with External and Internal Heat Exchangers," Energies, MDPI, vol. 17(22), pages 1-18, November.
    3. Zinsmeister, Daniel & Tzscheutschler, Peter & Perić, Vedran S. & Goebel, Christoph, 2023. "Stratified thermal energy storage model with constant layer volume for predictive control — Formulation, comparison, and empirical validation," Renewable Energy, Elsevier, vol. 219(P2).

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