摘要
为了进一步节能减排,并根据用户负荷需求,设计出合适的高温蒸汽热泵系统,提出了一种基于多目标优化的高温蒸汽热泵设计优化方法。首先根据选取的高温蒸汽热泵结构,说明系统各子系统的选型并研究高温蒸汽热泵的系统机理,建立系统的静态模型,提出了高温整齐热泵的㶲效率计算方法;然后确定了系统结构参数优化问题的优化参数,以㶲效率和总成本作为优化目标,基于多目标人工蜂群算法(multi-objective artificial bee colony algorithm, MOABC),建立高温蒸汽热泵的多目标优化模型;最后对不同工况下的系统㶲损失以及循环性能系数(coefficient of performance, COP)分别进行了MOABC的优化。结果表明,使用㶲效率作为能效指标可以提升高温蒸汽热泵系统的能源品质;而使用COP作为能效指标则降低了系统的能耗。
A suitable high-temperature steam heat pump system based on multi-objective optimization is designed to save energy and reduce emission.Firstly,the calculation method of exergic efficiency of high temperature steam heat pump was proposed after the explanation of each subsystem,the study of system mechanism and establishment of the system’s static model.Then,the optimization parameters and optimization indexes of the system structural parameter optimization were determined.Exergic efficiency and all cost were taken as the optimization objective,and the multi-objective artificial bee colony algorithm(MOABC)was used to establish the multi-objective optimization model of high-temperature steam heat pump.Finally,MOABC was optimized for exergic loss and COP(coefficient of performance)of the system under different working conditions.Experiments show that exergic efficiency as energy efficiency index can improve the energy quality of high temperature steam heat pump system.Using COP as the energy efficiency index can reduce the energy consumption of the system.
作者
袁兴宇
梁俊宇
牛琨皓
许琦
王达达
田星宇
殷捷
顾江其
YUAN Xing-yu;LIANG Jun-yu;NIU Kun-hao;XU Qi;WANG Da-da;TIAN Xing-yu;YIN Jie;GU Jiang-qi(Yunnan Electric Power Science Research Institute,Kunming 650217,China;Yunnan Power Grid Co.,Ltd.,Qujing Power Supply Bureau,Qujing 655000,China;School of Energy and Environment,Southeast University,Nanjing 210096,China;Nanjing Reason Information Technology Co.,Ltd.,Nanjing 210038,China)
出处
《科学技术与工程》
北大核心
2023年第14期6027-6036,共10页
Science Technology and Engineering
基金
国家自然科学基金(51876035)
中国南方电网公司科技项目(YNKJXM20190087)。
关键词
高温蒸汽热泵
多变量优化
人工蜂群算法
㶲效率
high temperature steam heat pump
multivariable optimization
artificial bee colony algorithm
exergy efficiency
