再压缩S-CO_(2)布雷顿循环性能分析及多目标优化

Performance analysis and multi-objective optimization of recompression S-CO2 Brayton cycle

结合储热的太阳能热发电技术输出稳定、调峰能力强,引入超临界二氧化碳(S-CO_(2))布雷顿循环可进一步提升热电转换效率。既有研究大多采用单一指标对S-CO_(2)循环进行性能评估,结果相对片面,因而有必要开展多指标综合性能评价以客观反映循环性能状况。建立了35 MW再压缩式S-CO_(2)循环的热力学和经济性模型,考察了关键参数对循环性能的影响。构建了反向传播神经网络结合遗传算法的优化方法(BP-GA),对循环性能进行多目标优化。结果表明,回热器总热导率的增加可提升循环效率,但存在上限;透平入口温度、循环最低和最高压力、分流比与循环性能分别存在显著的非单调作用关系,优化后的设计值依次为639.14℃、8.10 MPa、29.74 MPa和0.70。与初始设计值下的循环性能相比,优化后的循环系统度电成本降低了11.1%,循环热效率和比功分别提高了5.1%和27.6%。

The solar thermal power generation technology combined with heat storage has stable output and strong peak shaving capabilities.The introduction of supercritical carbon dioxide(S-CO2)Brayton cycle can further improve thermoelectric conversion efficiency.Most of the existing studies evaluated the performance of S-CO2 cycle based on a single index,leading to inconsistent evaluation results.Hence,it is necessary to carry out multi-index comprehensive evaluation to objectively reflect the cycle performance.In the present paper,mathematical models were established to investigate the thermodynamic performance and economy of a 35 MW recompression S-CO2 cycle,and the effects of critical parameters on cycle performance was analyzed.A BP-GA optimization method of back propagation neural network combined with elitist nondominated sorting genetic algorithm was constructed for multi-objective optimization of cycle performance.The results indicate that the cycle efficiency increases with an increasing total thermal conductivity of the recuperators,but there is a ceiling on growth.There are significant non-monotonic relations between turbine inlet temperature,minimum cycle pressure,maximum cycle pressure,split ratio and cycle performance,and the corresponding optimal values are 639.14℃,8.10 MPa,29.74 MPa and 0.70,respectively.Compared with the cycle performance based on the design conditions,the optimized cycle shows a reduction of 11.1%in LCOE,and an increase of 5.1%and 27.6%in efficiency and specific work,respectively.