This study aims to develop a zero-emission multi-generation system based on concentrated solar power to reduce solar thermal power cost and improve the efficiency of power generation. The multi-generation system mainly includes concentrated solar power system, supercritical carbon dioxide recompression Brayton cycle, organic flash cycle and absorption refrigeration cycle. A comprehensive energy, exergy, environment and economic analysis is carried out. Five key parameters are considered to assess the effect of decision parameters on system performance. An optimization method combining genetic algorithm and machine learning is used to accelerate the optimization process. Results display that compared with the stand-alone supercritical carbon dioxide recompression Brayton cycle, the energy utilization factor and exergy efficiency of the multi-generation system can improve 18.7 % and 6.4 %, respectively, and the decrement of levelized cost of electricity is reduced by 4 %. A higher turbine-Ⅰ inlet temperature can improve the exergy efficiency and reduce the levelized cost of electricity. Using the proposed optimization method, the long optimization time of complex multi-generation system is overcome. Comparing the exergy efficiency optimization case with the basic case and the cost optimization case, the exergy efficiency optimization case has the highest net power output and exergy efficiency. This work exhibits great potential in utilizing solar energy for power and cooling.

中文翻译：

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用于聚光太阳能的新型零排放联合供电和冷却系统：热力学和经济评估与优化

本研究旨在开发一种基于聚光太阳能发电的零排放多联产系统，以降低光热发电成本并提高发电效率。多联产系统主要包括聚光太阳能发电系统、超临界二氧化碳再压缩布雷顿循环、有机闪蒸循环和吸收式制冷循环。进行了全面的能源、火用、环境和经济分析。考虑五个关键参数来评估决策参数对系统性能的影响。采用遗传算法和机器学习相结合的优化方法来加速优化过程。结果表明，与独立超临界二氧化碳再压缩布雷顿循环相比，多联产系统的能量利用率和火用效率分别提高18.7%和6.4%，平准化电费降低幅度为4%。较高的Ⅰ号汽轮机进口温度可以提高火用效率，降低平准化电费。利用所提出的优化方法，克服了复杂多联系统优化时间长的问题。将火用效率优化工况与基本工况和成本优化工况进行比较，火用效率优化工况具有最高的净功率输出和火用效率。这项工作在利用太阳能发电和冷却方面展现出巨大的潜力。