Abstract One of the most vital concerns in evaluating power generation systems is the comprehensive review of all useful indexes. In the vast range of researches regarding renewable energy systems, just the thermodynamic performance of a typical power plant is considered without solar system operating time management. Thus, in this paper, a novel approach, simultaneous dynamic simulation, and conventional thermodynamic analysis are used for the evaluation of a solar-assisted post-combustion CO 2 capture, integrated into a coal-fired steam power plant as a case study. For the dynamic simulation of linear parabolic solar collector, the non-linear sliding mode control (SMC) approach, and for thermodynamic modeling, the prevalent technique (exergy analysis) is employed. The effects of thermodynamic parameters, on control system performance, are appeared during the investigation of the power generation system. Therefore, the control system characteristic (settling time), as well as exergetic parameters, are affected power generation system. Finally, to achieve an optimal configuration, multi-objective optimization (using PSO Algorithm Tool) is performed to make a power generation system with maximum agility (minimum settling time) and maximum efficiency. The promising two-objective optimization outcomes indicated 14.42 and 0.8% enhancement in settling time and exergetic efficiency, and the three-objective optimization revealed 14.4and 0.74% of improvement in settling time and thermal efficiency, respectively.

中文翻译：

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燃烧后二氧化碳捕集燃煤电厂的太阳系动态模拟、热力学分析和多目标优化的新程序

摘要 评估发电系统最重要的问题之一是对所有有用指标的综合审查。在有关可再生能源系统的大量研究中，只考虑了典型发电厂的热力学性能，而没有太阳能系统运行时间管理。因此，在本文中，采用一种新方法、同步动态模拟和传统热力学分析来评估太阳能辅助燃烧后 CO 2 捕获，并将其集成到燃煤蒸汽发电厂中作为案例研究。对于线性抛物面太阳能集热器的动态模拟，采用非线性滑模控制 (SMC) 方法，对于热力学建模，采用流行的技术（火用分析）。热力学参数对控制系统性能的影响，在发电系统的调查中出现。因此，控制系统特性（稳定时间），以及火用参数，都受到发电系统的影响。最后，为了实现最佳配置，执行多目标优化（使用 PSO 算法工具），使发电系统具有最大敏捷性（最小稳定时间）和最大效率。有希望的双目标优化结果表明稳定时间和热效率提高了 14.42% 和 0.8%，三目标优化显示稳定时间和热效率分别提高了 14.4% 和 0.74%。发电系统受到影响。最后，为了实现最佳配置，执行多目标优化（使用 PSO 算法工具），使发电系统具有最大敏捷性（最小稳定时间）和最大效率。有希望的双目标优化结果表明稳定时间和火用效率提高了 14.42% 和 0.8%，三目标优化显示稳定时间和热效率分别提高了 14.4% 和 0.74%。发电系统受到影响。最后，为了实现最佳配置，执行多目标优化（使用 PSO 算法工具），使发电系统具有最大敏捷性（最小稳定时间）和最大效率。有希望的双目标优化结果表明稳定时间和热效率提高了 14.42% 和 0.8%，三目标优化显示稳定时间和热效率分别提高了 14.4% 和 0.74%。