Dynamic modeling of a sensible thermal energy storage tank with an immersed coil heat exchanger under three operation modes. (1st June 2017)

• Record Type: Journal Article
• Authors: Nash, Austin L.; Badithela, Apurva; Jain, Neera
• Is Part Of: Applied energy. Volume 195(2017)
• Page Start: 877
• Page End: 889
• Publication Date: 2017-06-01
• Subjects: Dynamic modeling -- Control-oriented modeling -- Thermal energy storage -- Immersed heat exchanger -- Hot water storage tank -- Waste heat recovery -- Demand response; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2017.03.092
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A control-oriented dynamic model of a thermal energy storage tank is proposed. The dynamic tank model is spatially discretized into n nodes. Simplifying assumptions enable an accurate yet zero-order immersed coil HX model. The model is experimentally tuned and validated. The model is well-suited for advanced control design and real-time simulation. Abstract: In this paper we consider control-oriented modeling of a sensible thermal energy storage (TES) tank with a helical immersed heat exchanger (IHX) coil. A key focus of the modeling approach is to minimize the number of dynamic states required to adequately describe the system dynamics. The resulting model is well-suited for model-based control design, real-time simulation, and hardware-in-the-loop testing aimed at intelligent operation of TES systems. We use a discretized approach to model the temperature dynamics of the water within the storage tank. We use a quasi-steady approach to model the IHX coil dynamics, thereby limiting computational complexity. In simulation, the model runs up to 1200× faster than real-time. A simulated case study of model-based feedback control demonstrates the utility of the modeling approach. The model contains four tuning parameters that are empirically determined using experimental data collected from a commercially available domestic hot water storage tank. The model is then validated, both temporally and spatially, against data collected during the simultaneous charge/discharge mode. Finally, we quantify the trade-off between model fidelity and increased control volume discretization, showing that a 60 node model yields a RMSE value under 4.5%.