Optimizing the solar-air hybrid source heat pump heating system based on the particle swarm algorithm

In order to deal with the increasingly severe energy situation and climate change, reducing global carbon emissions and developing new energy have become a universal consensus among countries in the world. The design of clean energy heating systems such as solar collectors (SC) and air source heat pumps (ASHP) has also received widespread attention. However, optimizing multiple parameters that interact with each other in the hybrid heating systems such as solar-air hybrid source heat pumps (HSHP) is still challenging, and the optimization of the parameters remains to be studied. By using the TRNSYS simulation platform, modify the performance parameters to decrease the system's annual cost with particle swarm optimization (PSO) and coordinate search method (CSM), respectively. The results show that two algorithms can significantly enhance the system performance, where it is the easier for PSO to find global optimum, and the average performance index COPsys of the system is about 15% higher than that of the CSM, and the system's annual power consumption could be lowered by 27.75%; In addition, the matching principle of the key parameters of the hybrid heating system is proposed and the sensitivity ranking of the optimized parameters is derived. These results offer theoretical foundations for optimal design of the solar-air HSHP heating system. (C) 2022 Xi' an Shiyou University. Published by Elsevier Ltd.

Automated summary

Dealing with the severe energy and climate change situation, reducing carbon emissions and developing new energy sources have become a global consensus. The design of clean energy heating systems, such as solar collectors and air source heat pumps, has received widespread attention. However, optimizing parameters in hybrid heating systems, like solar-air hybrid source heat pumps, that interact with each other remains challenging and requires further study. Using the TRNSYS simulation platform, performance parameters were modified to reduce annual costs with the particle swarm optimization and coordinate search method. The results show significant enhancements in system performance using both algorithms.