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Optimal Design Method of a Hybrid CSP-PV Plant Based on Genetic Algorithm Considering the Operation Strategy

DOI:10.1155/2018/8380276 期刊:International Journal of Photoenergy 出版年份:2018 更新时间:2025-09-11 14:15:04
摘要: Solar energy is the most abundant renewable energy and it has a great potential for development. There are two ways to transfer solar energy to electricity: photovoltaic power generation (PV) and concentrated solar power (CSP). CSP-PV hybrid system can be fully integrated with the advantages of the two systems to achieve low cost, stable output, and manageable to generate electricity. In this paper, the operation strategy of the CSP-PV system is proposed for parabolic trough CSP system and PV system which are now commercially operated. Genetic algorithm is used to optimize the design of the system and calculate PV-installed capacity, battery capacity, and storage capacity of CSP system, making the system to achieve the lowest cost of electricity generation. The results show that the introduction of the CSP system makes it possible to ensure the stability of the output power of hybrid system when the battery capacity is small, which greatly improves the annual utilization time of the PV and reduces solar abandonment. When the system is optimized by operation characteristics of Spring Equinox, the lowest LCOE is 0.0627 $/kWh, the rated capacity of PV and CSP system are 222.462 MW and 30 MW, storage and battery are 356.562 MWh and 14.687 MWh. When the system is optimized by the operation characteristics of the whole year, the lowest LCOE is 0.0555 $/kWh, the rated capacity of PV and CSP system are 242.954 MW and 30 MW, respectively, and the capacity of heat storage and battery are 136.059 MWh and 8.977 MWh. The comparison shows that the power generation curves of the hybrid system are similar in the two optimization-based methods—Spring Equinox based and annual based, but LCOE is lower when optimized by the annual operation characteristic, and the annual utilization rate of the system is higher when optimized by Spring Equinox based.
作者: Rongrong Zhai,Ying Chen,Hongtao Liu,Hao Wu,Yongping Yang
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Investigating the optimal design of a hybrid CSP-PV plant using genetic algorithm to achieve the lowest cost of electricity generation while ensuring stable output and manageability.

The integration of CSP and PV systems can significantly reduce the cost of electricity generation while ensuring stable output. The genetic algorithm-based optimization method effectively identifies the optimal configuration of the hybrid system. The study demonstrates that the CSP-PV hybrid system can achieve lower LCOE and higher annual utilization rates compared to standalone systems.

The study assumes constant efficiencies for some components (e.g., battery efficiency) and ignores some losses in the actual power generation process, which may affect the accuracy of the results. The optimization is based on specific meteorological data from Lhasa, which may not be representative of other locations.

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