Stratification efficiency of thermal energy storage systems – A new KPI based on dynamic hardware in the loop testing - Part II: Test results

Haller, Michel; Haberl, Robert; Reber, Andreas

doi:10.1016/j.enbuild.2019.109366

Cited by 5 publications

(3 citation statements)

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“…Simulations are conducted to provide confidence in the TES tank model described by Equations ( 6)- (8). The model is implemented in MATLAB/Simulink, with simulation results compared with those obtained using an existing water tank component available in Apros.…”

Section: Stratified 1-d Model Of a Water -Based Tes Tankmentioning

confidence: 99%

“…Three‐dimensional (3‐D) dynamic models using computational fluid dynamics are used to accurately describe the spatial thermal behaviour within a TES tank [6]. A common modelling approach is thermal stratification, where horizontal layers are defined throughout the height of the tank to represent temperature differences in the storage medium [7–9]. Stratified 3‐D models are capable of precisely matching results obtained experimentally, at the expense of increased modelling complexity and computational times [10].…”

Section: Introductionmentioning

confidence: 99%

“…Three-dimensional (3-D) dynamic models using computational fluid dynamics are used to accurately describe the spatial thermal behaviour within a TES tank [6]. A common modelling approach is thermal stratification, where horizontal layers are defined throughout the height of the tank to represent temperature differences in the storage medium [7][8][9]. Stratified 3-D models are capable of precisely matching results obtained experimentally, at the expense of increased modelling complexity and computational times [10].…”

Section: Introductionmentioning

confidence: 99%

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Design and verification of an effective state‐of‐charge estimator for thermal energy storage

et al. 2021

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Thermal energy storage (TES) is widely used in district heating and cooling systems (DHCS) to act as a buffer between the supply and demand schedules. The adequate control of charging and discharging modes of TES may improve the overall performance of a DHCS and, to this end, an effective regulation of its state-of-charge (SoC) is required. However, the calculation of SoC depends on the availability and accuracy of temperature measurements. A model-based observer for the calculation of the SoC of water-based TES tanks is presented. A dynamic model of a one-dimensional stratified water tank is adopted to develop the observer. Its effectiveness is assessed through 'model-in-the-loop' cosimulations, with the observer and the feedback control system being implemented in MATLAB/Simulink and a high-fidelity water tank component available in Apros being used as the plant model. Simulation results considering three different system configurations demonstrate that the model-based observer accurately estimates the temperature distribution within the tank, leading to an effective SoC computation and control-even in the case of sensor failure or upon limited sensor availability. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Stratified 1-d Model Of a Water -Based Tes Tankmentioning

confidence: 99%