Parametric analysis and optimization for exergoeconomic performance of a combined system based on solid oxide fuel cell-gas turbine and supercritical carbon dioxide Brayton cycle. (15th April 2019)

• Record Type: Journal Article
• Authors: Chen, Yunru; Wang, Meng; Liso, Vincenzo; Samsatli, Sheila; Samsatli, Nouri J; Jing, Rui; Chen, Jincan; Li, Ning; Zhao, Yingru
• Is Part Of: Energy conversion and management. Volume 186(2019)
• Page Start: 66
• Page End: 81
• Publication Date: 2019-04-15
• Subjects: Solid oxide fuel cell-gas turbine -- Supercritical carbon dioxide Brayton cycle -- Parametric sensitivity analysis -- Exergoeconomic bi-objective -- Pareto optimization; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2019.02.036
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Integrate fuel cell-gas turbine with supercritical carbon dioxide Brayton cycle. Investigate six key parameters' impacts on systemic exergoeconomic performance. Optimize the proposed system by multi-objective method. Analyze relationship of the two conflict objectives via polynomial model. Obtain 68% of exergy efficiency and 0.0575 $ kWh −1 levelized cost as optimum. Abstract: Fuel cell-gas turbine hybrid system is a potential field of investigation. This study establishes a modeling and optimization framework for a novel hybrid system consisting of a solid oxide fuel cell, a gas turbine and a supercritical carbon dioxide Brayton cycle. Based on the proposed thermodynamical model, a parametric analysis is investigated to determine the impacts of several key parameters on the system exergoeconomic performance. Meanwhile, bi-objective optimization is conducted for maximizing the exergy efficiency and minimizing the levelized cost of electricity via the Epsilon-constraint approach. The Linear Programming Techniques for Multidimensional Analysis of Preference decision-making approach is further employed to select the Pareto optimum solution from Pareto frontiers. The results show that several extreme values for the exergy efficiency and the levelized cost of electricity exist in a series of sensitivity curves, respectively. The Pareto frontiers indicates that with the increase of the exergy efficiency, the levelized cost of electricity shows a moderately increasing trend at first and increases rapidly afterward. Overall, at the Pareto optimum solution, the combined system can achieve an optimal exergy efficiency and levelized cost of electricity by 68% and 0.0575 $ kWh −1, respectively.