Abstract

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.

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