Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact. (15th September 2022) - Yan, Biao; Yang, Wansheng; He, Fuquan; Huang, Kehua; Zeng, Wenhao; Zhang, Wenlong; Ye, Haiseng
Abstract: Part load ratio is often observed in real operations of airport terminal cooling system. This phenomenon is more obvious during the COVID-19 pandemic, as sudden flight restrictions impacting cooling demand are widely adopted in hub airport terminals. This research aims to propose optimal strategies of multi-chiller in airport terminals based on cooling load characteristics modeling, to tackle the aforementioned issues. Numerical experiments based on a real-world Chinese airport terminal are conducted to validate the proposed method. The results show that an average cooling load drop of 30% is observed from scenario of normal flight before COVID-19 to scenario of COVID-19 Period flight, and the average cooling load drop reaches to 44% from scenario of busy flight before COVID-19 to scenario of COVID-19 Period flight. The results also reflect that cooling load presents synchronous trend with passenger flow, but presents asynchronous trend with outdoor temperature. The influence of outdoor temperature on cooling demand delays due to building envelops. It indicates that simple superimposition according to passenger flow change for chiller operation number is reliable, efficient and effective, but is not suitable for outdoor temperature change. The findings are helpful to develop optimal strategies for further real-time control of multi-chiller. Highlights: Cooling load characteristics modeling was proposed for optimal strategies of multi-chiller in airport terminals. The impact of COVID-19 Pandemic to cooling demand was accurately described in the modeling. A real-world airport terminal was conducted to validate the proposed strategies. An average cooling load drop of 30% is observed between before and after the COVID-19 Pandemic. The results compared with real operational data validate that the model proposed is accurate and reliable.