Performance analysis and multi-objective optimization of solar assisted ground-source heat pump heating system

Heating through a solar assisted ground-source heat pump system is an efficient method, and optimizing its parameters can significantly improve its energy efficiency. However, the research on the impact of the start-stop temperature difference of the heat collection cycle on the system is currently limited. To address this issue, this study aims to develop a SAGSHP system model located in Tianjin using TRNSYS software, investigate the effect of parameters such as the start-stop temperature difference on the system performance, and guide the optimization process. The optimization parameters include the collector's area, the water tank's volume, and temperature difference during the heat collection cycle. The NSGA-II algorithm is applied to perform multi-objective optimization to enhance the system's energy efficiency and economy. Furthermore, a sensitivity analysis is conducted to study each variable's impact on the optimization results. The variation in temperature between the start and stop of the collection cycle has a significant impact on the system's performance, as revealed by the study. The optimal operation scheme lies within the boundary range of parameter settings. The optimization leads to a decrease of 8.63% in the system's annual operating cost and a reduction of 1555 kWh in the annual power consumption. The relationship between the area of the collector and the temperature difference during heat collection startup is expressed as 0.034 & DEG;C/m(2), and the ratio to the tank volume is 1.33 & DEG;C/m(3). The degree of influence of optimization parameters on optimization results is ranked as V (i)>A (i)>T (s)>T (e).

Automated summary

This study developed a SAGSHP system model using TRNSYS software and investigated the impact of parameters such as the start-stop temperature difference on the system performance. The study revealed that the variation in temperature between the start and stop of the collection cycle has a significant impact on the system's performance. The optimization parameters have different degrees of influence on the optimization results, ranked as V(i)>A(i)>T(s)>T(e).