Abstract
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.
Highlights
The mismatch between the low‐cost generation and the peak demand of thermal energy and the large distances between the supply points and the thermal loads are two important challenges faced bydistrict heating and cooling systems (DHCS)
It has been estimated that the potential annual savings afforded by thermal energy storage (TES) in district heating systems (DHS) can reach 5% of the total cost depending on the system configuration [31], with energy savings up to 1400 TWh in all Europe [32]
A method to calculate the SoC of sensible heat‐based TES tanks has been presented
Summary
The mismatch between the low‐cost generation and the peak demand of thermal energy and the large distances between the supply points and the thermal loads are two important challenges faced bydistrict heating and cooling systems (DHCS). Both the system state variables and system outputs are the temperatures at each layer i (yi = Ti = xi, i = 1, 2, ..., 5) and mass flow rate m_ is the input of the system. As there may be a limited number of sensors within a TES tank, this may lead to not having access to the necessary outputs of the system to adequately estimate state vector x (i.e. layer temperatures) for an effective SoC calculation. The MiL simulation is carried out as a cosimulation using two software platforms, with the control scheme and the nonlinear observer being implemented in MATLAB/Simulink and a high‐fidelity water tank representation available in Apros being used as the plant model. For the model‐based observer implemented in MATLAB, temperature values of the TES tank in Apros are read and mass flow rate is modified according to the output of the controller— implemented in MATLAB
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