摘要
燃气–超临界CO_(2)联合循环清洁高效、结构紧凑,因系统部件中间容积较小,变负荷调节速率较高,具有良好的灵活性,通过其快速变负荷运行有利于大规模消纳可再生能源电量。该联合循环采用具有超–跨串级结构的超临界CO_(2)底循环实现燃机余热梯级利用。为解决其快速变负荷中循环特性快速预测及优化问题,该文提出基于面心立方设计和反向传播神经网络的变工况特性求解方法。采用粒子群优化算法确定该联合循环变负荷过程的最优滑压运行策略。结果表明:基于神经网络的循环变工况特性预测方法具有良好的精度,并有效缩短仿真计算时间。与流量比例调节法对比,最优滑压运行策略使循环变工况过程具有更优的循环效率及可行运行区间,可以快速准确地为燃气–超临界CO_(2)联合循环提供变工况运行参考。
The gas-supercritical CO_(2) combined cycle is clean and high-efficient,with a compact structure.As its components are of smaller volumes,the combined cycle has a stronger capability of rapid load change.As an accommodation source,it is a viable solution for large-scale accommodation of renewable energy by rapid load change.This combined cycle uses a supercritical CO_(2) cycle and a transcritical CO_(2) cycle to recover the exhaust heat from a gas turbine.To solve the issues of rapid prediction and optimization of off-design performance under rapid load change condition,this study proposes a solution procedure based on face-centered cubic design and back-propagation neural network.In terms of the particle swarm optimization algorithm,the optimal sliding pressure operation strategy is proposed.The results show the off-design performance prediction method is of a satisfied accuracy,and it can shorten the simulation time.Compared with the strategy of proportional mass flow rate operation,the optimal sliding pressure operation strategy has a higher efficiency and a wider operation range.It indicates that with this operation strategy the gas-supercritical CO_(2) combined cycle can have a better off-design performance.
作者
曹越
陈然璟
展君
陈祎璠
司风琪
CAO Yue;CHEN Ranjing;ZHAN Jun;CHEN Yifan;SI Fengqi(Key Laboratory of Energy Thermal Conversion and Control,Ministry of Education(Southeast University),Nanjing 210096,Jiangsu Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2023年第11期4178-4189,共12页
Proceedings of the CSEE
基金
国家自然科学基金项目(52206006)
江苏省基础研究计划(自然科学基金)青年基金项目(BK20210240)。
关键词
超临界CO_(2)循环
变工况特性
反向传播神经网络
快速预测
运行优化
supercritical CO_(2)cycle
off-design performance
back-propagation neural network
rapid prediction
operation optimization
