CSP plants cooling technology: Techno-economic analysis, parametric study, and stacking ensemble learning forecasting

被引:0
|
作者
Elfeky, Karem Elsayed [1 ,2 ]
Hosny, Mohamed [3 ]
Abu Khatwa, Shaaban [4 ]
Mohammed, Abubakar Gambo [5 ]
Wang, Qiuwang [1 ]
机构
[1] Xi An Jiao Tong Univ, Sch Energy & Power Engn, Key Lab Thermo Fluid Sci & Engn, Minist Educ, Xian 710049, Shaanxi, Peoples R China
[2] Benha Univ, Benha Fac Engn, Mech Engn Dept, Banha 13511, Qalyubia, Egypt
[3] Benha Univ, Benha Fac Engn, Dept Elect Engn, Banha, Egypt
[4] Al Azhar Univ, Fac Engn, Dept Urban Planning Engn, Cairo, Egypt
[5] Univ Tenaga Nas, Inst Sustainable Energy, Putrajaya Campus, Kajang 43000, Selangor, Malaysia
基金
中国国家自然科学基金;
关键词
Convolutional neural network; Concentrating solar power; Dry wet and hybrid cooling; Levelized cost of electricity; THERMAL POWER-PLANT; TROUGH SOLAR COLLECTOR; ENERGY-PRODUCTION; REDUCE WATER; TIME-SERIES; TOWER; PERFORMANCE; PREDICTION; CONSUMPTION;
D O I
10.1016/j.tsep.2024.102777
中图分类号
O414.1 [热力学];
学科分类号
摘要
The growing solar industry and technological developments that increase the efficiency and affordability of solar plants are driven by the growing need for sustainable energy sources. The selection of the type of cooling tower technology significantly impacts the overall performance of concentrating solar power (CSP) plants because the cooling towers are essential elements for heat expulsion. The primary objective is to assess the influence of cooling tower technology on CSP plants from the perspective of techno-economic performance by implementing wet, dry, and hybrid cooling systems and optimizing the variables affecting solar tower power plants by conducting a parametric analysis. Moreover, a unique stacking ensemble model comprising a dual-layer structure is developed for solar tower power plant performance prediction. Following the findings, dry and wet cooling technologies came in second and third, respectively, with the hybrid cooling technique achieving the best performance outcomes. By incorporating wet-dry as well as hybrid cooling towers at the Benban location, the levelized cost of electricity for the solar tower was determined to be 13.99, 13.62, and 13.37 cent/kWh. The results show that based on the parametric assessment; the capacity factor rose from 11.73 to 73.13% when the mirror reflectance changed from 0.6 to 0.95% and the reflective area to profile ratio from 0.5 to 0.9%. The proposed stacking ensemble demonstrated superior performance compared to standalone base models and existing techniques.
引用
收藏
页数:18
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