Performance investigation of a hybrid PV/T collector with a novel trapezoidal fluid channel

被引:6
|
作者
Dong, Shiqian [1 ]
Long, He [4 ]
Guan, Jingxuan [1 ]
Jiang, Lina [1 ]
Zhuang, Chaoqun [3 ]
Gao, Yafeng [1 ]
Di, Yanqiang [2 ]
机构
[1] Chongqing Univ, Joint Int Res Lab Green Bldg & Built Environm, Minist Educ, Chongqing 400044, Peoples R China
[2] China Acad Bldg Res, Beijing 100013, Peoples R China
[3] Alan Turing Inst, Data Centr Engn, British Lib, 96 Euston Rd, London NW1 2DB, England
[4] BYD AUTO CO LTD, Auto Engineer Res Inst, Xian 710000, Peoples R China
关键词
PV/T collector; Trapezoidal fluid channel; Graphite; Machine learning algorithm; APSO; THERMAL-SYSTEMS; OPTIMIZATION; HEAT; EFFICIENCY; MODULES; PVT;
D O I
10.1016/j.energy.2023.129594
中图分类号
O414.1 [热力学];
学科分类号
摘要
The photovoltaic-thermal collector (PV/T) is an innovative technology that combines PV cells and solar thermal collectors into a co-generation unit, enabling full-spectrum solar utilization. This research proposes and examines a hybrid PV/T system with novel trapezoidal fluid channels embedded in graphite, arranged in a parallel S-shaped structure under outdoor conditions of Beijing. Parametric discussions and daily performance analysis are conducted to assess its effectiveness and stability. A daily average of 12.7 degrees C reduction in cell temperature compared to PV modules results in a relative 5.20 % improvement in electrical output under the intensity of solar radiation is 597 W/m2. Similarly, the peak temperature of the water tank reaches 56.9 degrees C, and thermal efficiency is enhanced by 7.86 % over conventional sheet-tube PV/T collectors. For further optimization, a constrained adaptive particle swarm optimization algorithm (EPF-APSO) is constructed based on the experimental data, implementing a variable water flow rate (VWV) strategy. Compared to a constant water flow rate (CWV), the overall energy collected varies from 2.755 to 2.828 kWh per day, potentially reducing pump energy consumption by 17.93 %. The contribution of this paper provides references on structural and operational parameters optimization of PV/T collectors applying machine learning algorithms for better comprehensive efficiency.
引用
收藏
页数:19
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