Using the modified mayfly algorithm for optimizing the component size and operation strategy of a high temperature PEMFC-powered CCHP

• Main Authors: Xiaokai Guo, Xianguo Yan, Kittisak Jermsittiparsert
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Energy Reports
• Subjects: Modified mayfly algorithm; CCHP; High temperature PEMFC; Sensitivity analysis; Optimum size; Optimum operation
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352484721001402

A CCHP is modeled here, which is operated based on thermal energy provision because the target consumer demands more heat and cooling than electricity. The main components of the CCHP system are a PEMFC as the main generator, absorption chiller, electric chiller, and auxiliary boiler. The fuel consumption, cost, and carbon release improvements compared to a separated generation system (SGS) are maximized in this system using the proposed modified mayfly algorithm. The system design and operation are optimized in two different stages. A hotel building has been chosen as the case study to demonstrate the effectiveness of the proposed CCHP system and optimization in comparison to the traditional mayfly algorithm (MA) and genetic algorithm (GA). The proposed optimization algorithm has performed 8.15% and 10.06% better compared to MA and GA, respectively. It also reached its solution in far shorter time. The optimum size of PEMDC and share of auxiliary chiller (AC) in cooling provision is determined to be 548 kW and 43.1%, respectively. It is shown that improvement achieved by using the AC in the CCHP is increased by 23.21% compared to a CCHP without one. Furthermore, the AC make the improvement of CCHP more constant and robust to load variation. Moreover, the sensitivity of the optimum operation condition of the CCHP against the electricity and fuel price changes and share of the AC in supplying the cooling demand are studied to provide the system operators with the necessary tools needed to optimally handle price changes.