Thermodynamic design and power prediction of a solar power tower integrated system using neural networks

Abstract

In spite of the fact that the solar power tower system is considered as one of the most valuable power generation facilities, it still faces challenges such as insufficient utilization of the solar salt temperature range and the dependence on high solar radiation intensity. To address this issue, an integrated recompression Brayton cycle and trans-critical regenerative organic Rankine cycle in parallel layout is proposed. The comparison with other literature shows that the specific work of the integrated system (147.8 kW/kg) outperforms the partial cooling Brayton cycle (130.1 kW/kg) under the same solar salt temperature range. In order to forecast future electricity generation and serve as a reference for plant operation, a power prediction strategy using neural networks is developed. Findings indicate that the integrated system can increase power production and maximize solar salt temperature utilization by adjusting the physical constraint strategy based on the changing temperature interval. Its equivalent work and thermal efficiency reach 12.7 MW and 38.36%, respectively, and it can recover the cost in 8 years. These results suggest that the proposed integrated system is a promising solution to enhance the performance of solar power tower systems with significant economic benefits.