Multicomponent numerical model for heat pump control with low-temperature heat storage: A benchmark in the conditions of Central Europe

journal article in Web of Science

en

This paper presents a computational parametric study on increasing the Seasonal Coefficient of Performance for residential heat pumps, utilizing an enhanced computational model and comparing it to previous research. The model computes a system that consists of a heat pump, low-temperature heat storage, heat exchanger and circulation pump, and a control unit which allows the heat pump to choose either low-temperature heat storage or ambient air as the favorable heat source. A parametric study is conducted to obtain results from a numerical model describing in detail the heat storage, surrounding soil, and the behavior of a water tank with and without temperature stratification and with two different geometries. These are combined into three different systems. Each system uses an algorithm for heat pump control that combines equithermal regulation and deferred heat storage discharging based on long-term temperature trends. Python is used to transform the numerical model into a computational model, and the assessment of heat pump operation is made based on meteorological data from the years 2012–2021 recorded in the city of Brno, Czech Republic, Central Europe. Previous studies have suggested that this combination for heat pump control and a more detailed numerical model of the system should result in a SCOP increase. In this study, heat losses of an actual occupied building with floor area of 140 m2 without considering heat radiation are used. The results show that stratification cannot be omitted for numerical modelling, and that the geometry of the storage influences how the storage should be designed. For a wide and shallow storage, it is better to insulate the system and increase the SCOP from 3.8 to 4.65, but non-insulated deep and narrow storage can increase the from 3.8 to 4.95. Flow rate is also an important parameter with a local maximum between 0.01 and 0.02 kg/s for every cubic meter of the storage. When a system consisting of parts readily available on the market is used at optimal conditions, the resulting increase can be from 11.8% up to as high as 25.7% with low-temperature heat storage ranging from 9 to 25 cubic meters.

This paper presents a computational parametric study on increasing the Seasonal Coefficient of Performance for residential heat pumps, utilizing an enhanced computational model and comparing it to previous research. The model computes a system that consists of a heat pump, low-temperature heat storage, heat exchanger and circulation pump, and a control unit which allows the heat pump to choose either low-temperature heat storage or ambient air as the favorable heat source. A parametric study is conducted to obtain results from a numerical model describing in detail the heat storage, surrounding soil, and the behavior of a water tank with and without temperature stratification and with two different geometries. These are combined into three different systems. Each system uses an algorithm for heat pump control that combines equithermal regulation and deferred heat storage discharging based on long-term temperature trends. Python is used to transform the numerical model into a computational model, and the assessment of heat pump operation is made based on meteorological data from the years 2012–2021 recorded in the city of Brno, Czech Republic, Central Europe. Previous studies have suggested that this combination for heat pump control and a more detailed numerical model of the system should result in a SCOP increase. In this study, heat losses of an actual occupied building with floor area of 140 m2 without considering heat radiation are used. The results show that stratification cannot be omitted for numerical modelling, and that the geometry of the storage influences how the storage should be designed. For a wide and shallow storage, it is better to insulate the system and increase the SCOP from 3.8 to 4.65, but non-insulated deep and narrow storage can increase the from 3.8 to 4.95. Flow rate is also an important parameter with a local maximum between 0.01 and 0.02 kg/s for every cubic meter of the storage. When a system consisting of parts readily available on the market is used at optimal conditions, the resulting increase can be from 11.8% up to as high as 25.7% with low-temperature heat storage ranging from 9 to 25 cubic meters.

SCOP,Heat pump,Heat storage,Heat source,FEM

08.01.2023

Elsevier

2352-7102

66

20