Parameter optimization for compressed air energy storage system based on particle swarm optimization

ZHOU Meng; JIA Yalei

doi:10.19666/j.rlfd.201704010

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Parameter optimization for compressed air energy storage system based on particle swarm optimization

Thermal Power Generation Vol. 47, Issue 1, Pages: 94-99(2018)

更新时间：2025-09-28

- Parameter optimization for compressed air energy storage system based on particle swarm optimization
- 作者机构：
  
  1. 河北软件职业技术学院软件技术系
  2. 华北电力大学电站设备状态监测与控制教育部重点实验室
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201704010
  CLC： TK02
- Published：2018
- 稿件说明：
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ZHOU Meng, JIA Yalei. Parameter optimization for compressed air energy storage system based on particle swarm optimization[J]. Thermal Power Generation, 2018, 47(1): 94-99.
DOI：

ZHOU Meng, JIA Yalei. Parameter optimization for compressed air energy storage system based on particle swarm optimization[J]. Thermal Power Generation, 2018, 47(1): 94-99. DOI： 10.19666/j.rlfd.201704010.

摘要

压缩空气储能技术是一种高效、环保的大规模储能技术

在可再生能源并网和电网调峰领域有广泛应用。为了提高压缩空气储能系统的储能效率

对系统建立了热力学模型

研究了系统运行参数对系统性能的影响

并采用改进粒子群算法对多个运行参数进行优化。研究结果表明:随着储气室最低压力的升高

储能效率先升高后降低

储气过程压力升高幅度和储气室对流换热系数的增大会使储能效率降低;通过改进粒子群优化算法

储能效率最高可以达到0.699 45;优化算法具有较好的全局收敛性和较高的精确度。

Abstract

The compressed air energy storage(CAES) technology is an efficient and green large-scale energy storage technology

which has been widely used in the field of renewable energy and power grid peaking. In order to improve the energy storage efficiency of the compressed air energy storage system

the thermodynamic model of the system was established and the influence of the system operating parameters on the system performance was studied. Moreover

an improved particle swarm optimization algorithm was applied to optimize several operating parameters. The results show that

with the increase of the minimum pressure in the air storage chamber

the energy storage efficiency increases firstly and then decreases. The energy storage efficiency will reduce when increasing the amplitude of gas pressure rise and the convective heat transfer coefficient. Through the improved particle swarm optimization algorithm

the energy storage efficiency can reach up to 0.699 45

and the optimization algorithm has well global convergence and high accuracy.

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