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Latent thermal energy storage (LTS) systems are versatile due to their high-energy storage density within a small temperature range. In shell-and-tube type storage systems fins can be used in order to achieve enhanced charging and discharging power. Typically, circular fins are evenly distributed over the length of the heat exchanger pipe. However, it is yet to be proven that this allocation is the most suitable for every kind of system and application. Consequently, within this paper, a simulation model was developed in order to examine the effect of different fin distributions on the performance of shell-and-tube type latent thermal storage units at discharge. The model was set up in MATLAB Simulink R2015b (The MathWorks, Inc., Natick, MA, USA) based on the enthalpy method and validated by a reference model designed in ANSYS Fluent 15.0 (ANSYS, Inc., Canonsburg, PA, USA). The fin density of the heat exchanger pipe was increased towards the pipe outlet. This concentration of fins was implemented linearly, exponentially or suddenly with the total number of fins remaining constant during the variation of fin allocations. Results show that there is an influence of fin allocation on storage performance. However, the average storage performance at total discharge only increased by three percent with the best allocation compared to an equidistant arrangement.

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This article presents a 125-day experiment to investigate model predictive heat pump control. The experiment was performed in two parallel operated systems with identical components during the heating season. One of the systems was operated by a standard controller and thus represented a reference to evaluate the model predictive control. Both test rigs were heated by an air-source heat pump which is influenced by real weather conditions. A single-family house model depending on weather measurement data ensured a realistic heat consumption in the test rigs. The adapted model predictive control algorithm aimed to minimize the operational costs of the heat pump. The evaluation of the measurement results showed that the electrical energy demand of the heat pump can be reduced and the coefficient of performance can be increased by applying the model predictive controller. Furthermore, the self-consumption of photovoltaic electricity, which is calculated by means of a photovoltaic model and global radiation measurement data, was more than doubled. Consequently, the energy costs of heat pump operation were reduced by 9.0% in comparison to the reference and assuming German energy prices. The results were further compared to the scientific literature and short-term measurements were performed with the same experimental setup. The dependence of the measurement results on the weather conditions and the weather forecasting quality are shown. It was found that the duration of experiments should be as long as possible for a comprehensive evaluation of the model predictive control potential.

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