Simulation optimization of a hybrid system combining thermosyphon and vapor compression used in 5G base stations based on a grey-box model and genetic algorithm

Advances in communication technology have led to a significant increase in the energy consumption of 5G base stations. We previously developed a hybrid cooling system combining thermosyphon and vapor compression to reduce energy consumption, but it still needs further optimization. Establishing a model for operating parameters optimization can increase the utilization time of natural cold sources and improve energy efficiency. In this paper, a novel grey-box model is built and experimentally validated to quickly predict the cooling capacity and energy consumption of the system under different working conditions. The model uses seven characteristic parameters to describe the influence of the physical structure and fluid properties on heat transfer. Next, two basic operation strategies are proposed, and the annual energy consumption of the system under these two strategies and different climatic conditions and loads is calculated. Finally, the operating parameters are optimized to minimize energy consumption using a genetic algorithm (GA) and the results are compared with the two aforementioned basic operating strategies. The results show that the energy efficiency improvement achieved through GA optimization during the transitional seasons is significantly greater than that in winter and summer. After GA optimization, the annual energy efficiency ratio of the system implemented in Kunming increased from 8.24 to 20.61, while Guangzhou's system increased from 4.41 to 6.42, respectively. Further analysis shows that the greater the proportion of transitional seasons annually, the greater the energy savings achieved through GA optimization. The results contribute to the efficient operation of hybrid cooling systems in base stations.