压缩空气储能系统变工况仿真及运行优化研究

刘传亮; 张天博; 郝宁; 陈东; 刘佳

doi:10.19805/j.cnki.jcspe.2026.250053

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

压缩空气储能系统变工况仿真及运行优化研究

储能专题 | 更新时间：2026-10-10

- 压缩空气储能系统变工况仿真及运行优化研究
- Research on Variable Operating Condition Simulation and Operation Optimization of Compressed Air Energy Storage System
- 动力工程学报 2026年46卷第5期页码：13-22
- 作者机构：
- 作者简介：
  
  刘传亮(1984—),男,江苏宿迁人,正高级工程师,硕士研究生,研究方向为储能、风电、新型电力系统集成及应用等,E-mail:liuchuanliang@speri.com.cn。
- 基金信息：
  
  国家电力投资集团有限公司集团B1类课题项目(KYB12022CN01)
- DOI：10.19805/j.cnki.jcspe.2026.250053
  中图分类号： TK37
- 纸质出版：2026
- 稿件说明：
移动端阅览
刘传亮, 张天博, 郝宁, 陈东, 刘佳. 压缩空气储能系统变工况仿真及运行优化研究[J]. 动力工程学报, 2026, 46(5): 13-22.
刘传亮, 张天博, 郝宁, 陈东, 刘佳. 压缩空气储能系统变工况仿真及运行优化研究[J]. 动力工程学报, 2026, 46(5): 13-22. DOI： 10.19805/j.cnki.jcspe.2026.250053.

DOI： 10.19805/j.cnki.jcspe.2026.250053

摘要

压缩空气储能是解决可再生能源波动性问题的长时储能技术。研究其变工况特性及运行方式优化对压缩空气储能的科学调控具有重要意义。以10 MW压缩空气储能系统为研究对象

采用模块化的建模方法

基于Modelica语言建立了压缩空气储能系统储能过程和释能过程仿真模型

并采用粒子群算法对不同工况下系统的运行方式进行优化。结果表明:所建立的压缩空气储能系统模型可以准确模拟储能、释能运行过程

且将其应用于不同工况下的运行策略优化时具有良好的效果

变工况优化可显著降低储能功耗和释能气耗

提升系统循环效率

研究成果可支撑压缩空气储能系统变工况运行特性分析及运行策略优化。

Abstract

Compressed air energy storage is a long-duration energy storage technology that solves the volatility problem of renewable energy. It is of great significance to study the characteristics of variable operating conditions and the optimization of operation modes for the scientific regulation and control of compressed air energy storage. A 10 MW compressed air energy storage system was taken as the research object

a modular modeling method was used to establish simulation models of the energy storage process and energy release process of the compressed air energy storage system based on Modelica language

and the operation modes of the system under different working conditions were optimized based on the particle swarm optimization algorithm. Results show that the established compressed air energy storage system model can accurately simulate the operation processes of energy storage and energy release

and have good results when applied to the optimization of operation strategies under different working conditions of the system. The optimization of variable operating conditions can significantly reduce the power consumption of energy storage and air consumption of energy release

and improve the round-trip efficiency of the system. The study results can support the analysis of the characteristics of variable operating conditions and the optimization of operation strategies of compressed air energy storage system.

关键词

Keywords

references

梅生伟, 李瑞, 陈来军, 等. 先进绝热压缩空气储能技术研究进展及展望[J]. 中国电机工程学报, 2018, 38(10): 2893-2907, 3140.MEI Shengwei, LI Rui, CHEN Laijun, et al. An overview and outlook on advanced adiabatic compressed air energy storage technique [J]. Proceedings of the CSEE, 2018, 38(10): 2893-2907, 3140.

郭文宾. 压缩空气储能系统离心压缩机变工况特性及调节规律研究[D]. 北京: 中国科学院大学(中国科学院工程热物理研究所), 2021.

陈辉, 李文, 盛勇, 等. CAES释能过程多工况动态仿真及效率分析[J]. 动力工程学报, 2023, 43(7): 869-876, 892. CHEN Hui, LI Wen, SHENG Yong, et al. Dynamic simulation and efficiency analysis of energy release process in compressed air energy storage under multi conditions[J]. Journal of Chinese Society of Power Engineering, 2023, 43(7): 869-876, 892.

RAJU M, KUMAR KHAITAN S. Modeling and simulation of compressed air storage in caverns: a case study of the Huntorf plant[J]. Applied Energy, 2012, 89(1): 474-481.

KHAITAN S K, RAJU M. Dynamic simulation of air storage-based gas turbine plants[J]. International Journal of Energy Research, 2013, 37(6): 558-569.

王成山, 武震, 杨献莘, 等. 基于微型压缩空气储能的混合储能系统建模与实验验证[J]. 电力系统自动化, 2014, 38(23): 22-26.WANG Chengshan, WU Zhen, YANG Xianshen, et al. Modeling and verification of hybrid energy storage system based on micro compressed air energy storage[J]. Automation of Electric Power Systems, 2014, 38(23): 22-26.

文贤馗, 刘石, 李翔, 等. 先进压缩空气储能系统模拟与效率分析[J]. 动力工程学报, 2021, 41(9): 802-808.WEN Xiankui, LIU Shi, LI Xiang, et al. Simulation and efficiency analysis of advanced compressed air energy storage system[J]. Journal of Chinese Society of Power Engineering, 2021, 41(9): 802-808.

胡博. 含压缩空气储能的CCHP系统多目标优化设计与运行研究[D]. 南昌: 南昌大学, 2022.

何青, 刘辉, 张军良, 等. 基于正交设计的压缩空气储能系统效率分析[J]. 动力工程学报, 2016, 36(4): 313-319.HE Qing, LIU Hui, ZHANG Junliang, et al. Analysis on exergy efficiency of a compressed-air energy storage system based on orthogonal design[J]. Journal of Chinese Society of Power Engineering, 2016, 36(4): 313-319.

李鹏, 李国能, 苏航, 等. 不同运行方案下AA-CAES系统性能分析及优化[J]. 动力工程学报, 2022, 42(9): 843-851.LI Peng, LI Guoneng, SU Hang, et al. Performance analysis and optimization of AA-CAES system under different operation schemes[J]. Journal of Chinese Society of Power Engineering, 2022, 42(9): 843-851.

郭欢, 徐玉杰, 张新敬, 等. 蓄热式压缩空气储能系统变工况特性[J]. 中国电机工程学报, 2019, 39(5): 1366-1376.GUO Huan, XU Yujie, ZHANG Xinjing, et al. Off-design performance of compressed air energy storage system with thermal storage[J]. Proceedings of the CSEE, 2019, 39(5): 1366-1376.

张斌, 王光磊, 徐震, 等. 集成恒压压缩空气储能的燃气轮机CCHP系统特性研究[J]. 动力工程学报, 2024, 44(3): 369-375.ZHANG Bin, WANG Guanglei, XU Zhen, et al. Study on system characteristics of gas turbine CCHP integrated with constant-pressure compressed air energy storage[J]. Journal of Chinese Society of Power Engineering, 2024, 44(3): 369-375.

ADAMS II T A, NEASE J, TUCKER D, et al. Energy conversion with solid oxide fuel cell systems: a review of concepts and outlooks for the short- and long-term[J]. Industrial & Engineering Chemistry Research, 2013, 52(9): 3089-3111.

曹福毅, 张旭, 王廷举, 等. 大容量压缩空气储能系统空气透平工艺结构及运行特性研究[J]. 汽轮机技术, 2024, 66(1): 53-55.CAO Fuyi, ZHANG Xu, WANG Tingju, et al. Research on technological process and dynamic running of big-capacity air turbine of compressed air energy storage system[J]. Turbine Technology, 2024, 66(1): 53-55.

ZHOU Yujie, PAN Lei, HAN Xu, et al. Dynamic modeling and thermodynamic analysis of lithium bromide absorption refrigeration system using Modelica[J]. Applied Thermal Engineering, 2023, 225: 120106.

HINKELMAN K, WANG Jing, ZUO Wangda, et al. Modelica-based modeling and simulation of district cooling systems: a case study[J]. Applied Energy, 2022, 311: 118654.

谢心喻, 鲁业明, 王晓放, 等. 基于不同压气机特性曲线预测方法的单轴燃气轮机动态性能仿真研究[J]. 热能动力工程, 2021, 36(3): 26-34.XIE Xinyu, LU Yeming, WANG Xiaofang, et al. Simulation investigation on dynamic performance of single shaft gas turbine based on different compressor characteristic curve prediction methods[J]. Journal of Engineering for Thermal Energy and Power, 2021, 36(3): 26-34.

程堃, 万祥, 孙海军, 等. 弗留格尔公式的改进及其在汽轮机湿蒸汽级组中的应用[J]. 汽轮机技术, 2020, 62(2): 104-106, 131.CHENG Kun, WAN Xiang, SUN Haijun, et al. The improvement of Flugel formula and its application in wet steam stage groups of steam turbine[J]. Turbine Technology, 2020, 62(2): 104-106, 131.

KENNEDY J, EBERHART R. Particle swarm optimization [C] //ICNN'95-International Conference on Neural Networks. Perth, WA, Australia: IEEE, 1995. 1942-1948.

熊伟, 王琪. 基于神经网络和改进粒子群算法的电涡流传感器探头参数优化[J]. 武汉大学学报(工学版), 2025, 58(6): 999-1007.XIONG Wei, WANG Qi. Optimization of eddy current sensor probe parameters based on neural network and improved particle swarm optimization algorithm [J]. Engineering Journal of Wuhan University, 2025, 58(6): 999-1007.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于氢燃机的"电-氢-电"综合能源系统设计与运行管理

基于布雷顿循环的压缩二氧化碳储能系统研究

考虑热网动态特性的热电联产机组运行优化

绝热压缩空气储能系统充气负荷控制策略的研究与优化

压缩空气储能系统大型人工硐室结构的数值模拟优化研究