Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/70919
Title: A novel CHP-hP coupling system and its optimization analysis by genetic algorithm
Authors: Kang, SS 
Lu, L 
Li, HQ
Zhang, GQ
Issue Date: 2017
Source: Energy procedia, 2017, v. 105, no. , p. 2089-2094
Abstract: Combined heating and power system has been applied widely due to its energetic and environmental characteristics. Heat pump system is also regarded as a promising energy-saving and environment-friendly technology. This paper proposed a novel coupling system by integrating the combined heating and power system with the heat pump system. The performance characteristics of the novel coupling system are investigated by the Aspen plus software based on a case system. The results show that the energetic efficiency and exergetic efficiency are 142.2% and 22.6% respectively, increasing by 3.9% and 3.7% respectively, compared with the ones of reference system. In order to make the novel coupling system more practical, the multi-objective optimal model is developed based on the primary energy saving ratio, carbon dioxide emission reduction ratio and annual total cost saving ratio simultaneously. The key parameters, such as the capacity of power generation unit and the outlet temperature from the heat pump, are optimized by genetic algorithm so as to maximize the comprehensive performance.
Keywords: Combined heating and power system
Heat pump
Integration system
Performance analysis
Multi-objective optimization
Publisher: Elsevier
Journal: Energy procedia 
ISSN: 1876-6102
DOI: 10.1016/j.egypro.2017.03.588
Description: 8th International Conference on Applied Energy (ICAE), Oct 08-11, 2016, Beijing, Peoples R. China
Rights: © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The following publication Kang, S. S., Lu, L., Li, H. Q., & Zhang, G. Q. (2017). A novel CHP-hP coupling system and its optimization analysis by genetic algorithm. Energy Procedia, 105, 2089-2094 is available athttps://dx.doi.org/10.1016/j.egypro.2017.03.588
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