基于改进动态模态分解的先进绝热压缩空气储能一次调频控制策略

李天宇; 陈来军; 崔森; 刘瀚琛; 梅生伟

doi:10.13334/j.0258-8013.pcsee.250633

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基于改进动态模态分解的先进绝热压缩空气储能一次调频控制策略

可再生能源与储能 | 更新时间：2026-09-28

- 基于改进动态模态分解的先进绝热压缩空气储能一次调频控制策略
- Primary Frequency Modulation Control of Advanced Adiabatic Compressed Air Energy Storage Based on Modified Dynamic Mode Decomposition
- 中国电机工程学报 2026年46卷第14期页码：5869-5878
- 作者机构：
  
  清华大学电机工程与应用电子技术系，北京市海淀区,100084
- 作者简介：
- 基金信息：
- DOI：10.13334/j.0258-8013.pcsee.250633
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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李天宇, 陈来军, 崔森, 等. 基于改进动态模态分解的先进绝热压缩空气储能一次调频控制策略[J]. 中国电机工程学报, 2026,46(14):5869-5878.

LI Tianyu, CHEN Laijun, CUI Sen, et al. Primary Frequency Modulation Control of Advanced Adiabatic Compressed Air Energy Storage Based on Modified Dynamic Mode Decomposition[J]. 2026, 46(14): 5869-5878.
李天宇, 陈来军, 崔森, 等. 基于改进动态模态分解的先进绝热压缩空气储能一次调频控制策略[J]. 中国电机工程学报, 2026,46(14):5869-5878. DOI： 10.13334/j.0258-8013.pcsee.250633.

LI Tianyu, CHEN Laijun, CUI Sen, et al. Primary Frequency Modulation Control of Advanced Adiabatic Compressed Air Energy Storage Based on Modified Dynamic Mode Decomposition[J]. 2026, 46(14): 5869-5878. DOI： 10.13334/j.0258-8013.pcsee.250633.

摘要

先进绝热压缩空气储能(advanced adiabatic compressed air energy storage，AA-CAES)作为一种大容量惯性系统，能够有效提升电网的频率调节能力。然而，AA-CAES的非线性运行特征限制了系统反馈控制律的整定。该文针对AA- CAES的频率调节问题，提出一种数据驱动的线性二次型调节器(data-driven linear regulation controller，DLQR)。首先，基于Koopman算符(Koopman operator，KO)理论提出改进动态模态分解算法(modified dynamic mode decomposition algorithm，MDMD)，全局线性化AA-CAES动态模型；其次，结合AA-CAES动态物理信息甄别MDMD观测函数以提升算法学习性能；随后，基于MDMD提出面向AA-CAES一次调频控制的DLQR，优化系统反馈控制律；最后，通过MATLAB/SIMULINK搭建AA-CAES动态模型进行算例分析。结果表明，基于MDMD的DLQR能够准确描述AA- CAES的动态行为并给出最优控制律，且在频率调节过程中所提方法能够有效跟踪功率指令并显著改善AA-CAES的频率响应特性。

Abstract

Advanced adiabatic compressed air energy storage (AA-CAES)

as a large-capacity system

can effectively boost the frequency regulation capacity of power grids. Nevertheless

the nonlinear features of AA-CAES constrain the rectification of the feedback control law. This paper presents a data-driven linear quadratic regulator (DLQR) for AA-CAES to provide temporal frequency support. Firstly

based on the Koopman operator (KO) theory

a modified dynamic mode decomposition (MDMD) algorithm is put forward

which can globally linearize the AA-CAES model. Secondly

by incorporating the physical knowledge of AA-CAES into the selection of the observables

the performance of dynamic learning is enhanced. Then

a DLQR based on MDMD is designed to optimize the feedback control law of the system. Finally

the AA-CAES dynamic model is constructed in MATLAB/SIMULINK for simulation and analysis. Case studies demonstrate that the DLQR can accurately describe the dynamic behavior of AA-CAES and provide the optimal control law. During the frequency regulation process

the proposed method can effectively track the power command and significantly improve the frequency response characteristics of AA-CAES.

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