摘要
为解决可再生能源存在的间歇性和波动性等问题,考虑到现有大规模储能技术的不足,提出了一种兼具抽水蓄能技术和压缩空气储能技术特点的恒压型抽水压缩空气储能系统.首先建立其热力学模型和经济学模型,然后以能量效率和单位能量成本作为目标函数,以水气比、预置压力、增压机压比和增压机效率作为决策变量,分别针对容量为1 MW、2MW、5 MW的系统进行多目标优化。多目标优化结果表明,当水气比约为7、预置压力约为4 MPa、增压机压比约为2、增压机效率较高时,系统具有较高的能量效率和较低的单位能量成本。同时,随着系统容量的增加,单位能量成本明显降低。研究结果可为该系统的工程应用提供理论支撑。
Considering the shortages of existing bulk energy storage technologies,to overcome the drawbacks of renewable energies(i.e.,intermittent and fluctuation),a constant-pressure pumped hydro combined with compressed air energy storage(PHCA) system is proposed based on pumped hydro energy storage(PHES) and compressed air energy storage(CAES) technologies.Firstly,the thermodynamic model and economic model of the system were built.Then,by selecting energy efficiency and total investment cost per total output energy(ICPE) as objective functions and setting the hydrosphere ratio,the preset pressure,the pressure ratio of compressor and the efficiency of compressor as decision variables,multi-objective optimizations for systems of 1 MW,2 MW and 5 MW were carried out,respectively.The results revealed that the system can reach a relatively high system efficiency and a relatively low ICPE when the compressor efficiency is relatively high and values of the hydrosphere ratio,the preset pressure and the pressure ratio of compressor are around7,4 MPa and 2,respectively.Furthermore,with the increasing of the capacity of the system,ICPE decreases rapidly.The results could provide theoretical basis for the further engineering application of this system.
作者
严凯
侯付彬
刘明明
贲岳
王焕然
YAN Kai;HOU Fu-Bin;LIU Ming-Ming;BEN Yue;WANG Huan-Ran(School of energy and power engineering,Xi’an Jiaotong University,Xi'an 710049,China;State nuclear electric power planning design&research institute co.ltd,Beijing 100095,China)
出处
《工程热物理学报》
EI
CAS
CSCD
北大核心
2020年第1期135-140,共6页
Journal of Engineering Thermophysics
基金
国家自然科学基金资助项目(No.51676151)
关键词
抽水蓄能
压缩空气储能
热力学分析
经济学分析
多目标优化
pumped hydro energy storage
compressed air energy storage
thermodynamic analysis
economic analysis
multi-objective optimization