基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制

王天宇; 张江丰; 尹昊蕊; 张旭娟; 赵洪宇; 王祺; 李泉

doi:10.19666/j.rlfd.202511048

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基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制

热力发电 2026年55卷第2期页码：147-157

多类型储能辅助调峰调频技术 | 更新时间：2026-08-07

- 基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制
- Stochastic model predictive control for hybrid energy storage assisted thermal power unit in AGC based on adaptive Markov power prediction
- 作者机构：
  
  1. 国网浙江省电力有限公司电力科学研究院
  2. 华北电力大学控制与计算机工程学院
- 作者简介：
- 基金信息：
  
  国网浙江省电力有限公司科技项目（B311DS240011）~~
- DOI：10.19666/j.rlfd.202511048
  中图分类号： TM621;TP273
- 纸质出版：2026
- 稿件说明：
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[1]王天宇,张江丰,尹昊蕊,等.基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制[J].热力发电,2026,55(02):147-157.
[1]王天宇,张江丰,尹昊蕊,等.基于自适应马尔科夫功率预测的混合储能辅助火电机组AGC随机模型预测控制[J].热力发电,2026,55(02):147-157. DOI： 10.19666/j.rlfd.202511048.

DOI：10.19666/j.rlfd.202511048.

摘要

【目的】为提升混合储能系统(hybrid energy storage system

HESS)辅助火电机组响应自动发电控制(automatic generation control

AGC)指令时的调节性能，提出一种基于随机模型预测控制(stochastic model predictive control

SMPC)的火储联合功率分配策略。【方法】首先，针对包括功率型储能钛酸锂电池与能量型储能磷酸铁锂电池构成的HESS系统，提出基于马尔科夫概率矩阵构建未来时段火电机组响应AGC指令的HESS功率需求模型，并引入自适应机制实时动态修正状态转移概率，以提升AGC指令波动下的预测精度；其次，提出一种基于概率阈值与分层抽样相结合的场景树生成方法用于将自适应马尔科夫模型输出的概率分布转化为可用于优化的有限场景集合，描述多场景下功率需求预测的不确定性；最后，在上述框架基础上构建随机预测控制器，实现火电机组和HESS的功率最优分配。【结果】仿真实验表明，所提策略在调节性能上优于不考虑功率预测的传统联合调频策略以及未引入动态修正的由静态转移概率矩阵构建的SMPC策略，其性能指标K

分别提升14.1%和7.5%。【结论】该策略有效提升了火电机组与HESS的协同调节性能，具有较强的应用潜力。未来可以进一步优化模型，提升其在实际应用中的鲁棒性和适应性，推动该技术的实际落地。

Abstract

[Objective

]

With the growing integration of renewable energy sources into the power grid

frequency fluctuations have become a significant challenge. To address this

hybrid energy storage systems(HESS) are used to assist thermal power units in responding to automatic generation control(automatic generation control

AGC) commands. This

paper proposes a novel hybrid power distribution strategy based on stochastic model predictive control(SMPC) to enhance the regulation performance of thermal power units in AGC applications

particularly under fluctuating power demands. The aim is to optimize the power allocation between the thermal power unit and the HESS to improve the accuracy

stability

and efficiency of the regulation process

ensuring a more reliable response to AGC signals. [Methods

]

The proposed strategy first constructs a power demand model for the HESS system

consisting of lithium titanate batteries for high-power storage and lithium iron phosphate batteries for energy storage

based on a Markov probability matrix

which simulates the response of the thermal power unit to AGC commands. An adaptive mechanism is introduced to dynamically adjust the state transition probabilities in realtime

enhancing the accuracy of power demand predictions during AGC fluctuations. Additionally

a scene tree generation method is proposed

which combines probability thresholds with stratified sampling to transform the probability distribution output by the adaptive Markov model into a finite set of scenarios for optimization. This method is designed to better handle the uncertainty of power demand predictions under multiple future scenarios

addressing the inherent variability of AGC command responses. Finally

the strategy integrates the above components into an SMPC controller

which optimizes power distribution between the thermal power unit and HESS in real-time

considering the stochastic nature of power demands and control parameters. [Results

]

Simulation experiments demonstrate that the proposed strategy significantly outperforms traditional frequency regulation strategies

which do not incorporate power prediction

and static SMPC strategies that lack dynamic correction of state transition probabilities. The performance index K

is improved by 14.1% and 7.5%

respectively

showing that the SMPC strategy with adaptive power demand for

ecasting can achieve more precise and stable regulation performance. Additionally

the model's ability to handle uncertainty in power demand prediction allows for more accurate and timely responses to AGC fluctuations

resulting in better coordination between the thermal power unit and the HESS. [Conclusion

]

The proposed strategy effectively enhances the collaborative regulation performance between the thermal power unit and HESS

offering strong application potential. Further optimization of the model can improve its robustness and adaptability in practical applications

advancing the implementation of this technology.

关键词

Keywords

references

余晋宇,周荔丹,于天佑,等.基于分布式模型预测控制的电-氢混合储能系统控制策略[J].电力系统自动化, 2025, 49(3):114-124.YU Jinyu, ZHOU Lidan, YU Tianyou, et al. Control strategy for electricity-hydrogen hybrid energy storage system based on distributed model predictive control[J].Automation of Electric Power Systems, 2025, 49(3):114-124.

居文平,王一帆,赵勇,等.新型电力系统长时储能技术综述[J].热力发电, 2024, 53(9):1-9.JU Wenping, WANG Yifan, ZHAO Yong, et al. Overview of long-term energy storage technologies in new power systems[J]. Thermal Power Generation, 2024, 53(9):1-9.

李菁华,兀鹏越,黄富强,等.超级电容混合储能辅助火电机组AGC调频容量配置方案研究与应用[J].热力发电, 2025, 54(7):101-110.LI Jinghua, WU Pengyue, HUANG Fuqiang, et al.Research and application of AGC frequency regulation capacity allocation scheme for supercapacitor hybrid energy storage assisted thermal power unit[J]. Thermal Power Generation, 2025, 54(7):101-110.

杨鼎全,魏平.基于小波变换的混合储能二次调频容量优化配置方法研究[J].电网与清洁能源, 2025,41(10):24-35.YANG Dingquan, WEI Ping. A study on the optimal capacity configuration method for secondary frequency regulation of hybrid energy storage based on wavelet transform[J]. Power System and Clean Energy, 2025,41(10):24-35.

唐卫华,陈长青,史清芳,等.基于数据分解的“飞轮+锂电”混合储能系统辅助风电调频容量配置研究[J].电网与清洁能源, 2025, 41(9):132-140.TANG Weihua, CHEN Changqing, SHI Qingfang, et al.Research on frequency modulation capacity allocation of wind power assisted by“flywheel+lithium” hybrid energy storage system based on data decomposition[J].Power System and Clean Energy, 2025, 41(9):132-140.

LV Y, SUN H, WANG T, et al. A frequency modulation capability enhancement strategy of thermal power units by the integration of a hybrid energy storage system[J].Applied Thermal Engineering, 2025, 278:127441.

刘佳钰,梁晓斌,黄锡芳,等.多类型储能参与电力系统频率调节的协同控制策略[J].中国电力, 2026,59(1):124-132.LIU Jiayu, LIANG Xiaobin, HUANG Xifang, et al.Coordinated control strategy for multi-type energy storage participating in power system frequency regulation[J].Electric Power, 2026, 59(1):124-132.

崔杨,修志坚,薄利明,等.考虑火–储深度调峰容量二次分配的含风电电力系统分层优化调度[J].电网技术, 2022, 46(7):2520-2531.CUI Yang, XIU Zhijian, BO Liming, et al. Hierarchical optimal scheduling of power systems with wind generation considering secondary allocation of thermalstorage deep peaking capacity[J]. Power System Technology, 2022, 46(7):2520-2531.

陈鹏,王玮,杨建青,等.基于多尺度分解的风火储协同调频控制策略[J].太阳能学报, 2024, 45(3):428-435.CHEN Peng, WANG Wei, YANG Jianqing, et al.Cooperative frequency regulation control strategy of wind-thermal-storage system based on multi-scale decomposition[J]. Acta Energiae Solaris Sinica, 2024,45(3):428-435.

邢峰,褚文超,苏智东,等.基于AGC预测指令的火电机组控制策略[J].现代电力, 2023, 40(5):742-750.XING Feng, CHU Wenchao, SU Zhidong, et al. A control strategy of thermal power unit based on AGC prediction command[J]. Modern Electric Power, 2023,40(5):742-750.

AJAMI O M T, TAN R H G, NADARAJAH M, et al.Multi-objective optimization of HESS control for optimal frequency regulation in a power system with re penetration[J]. E-Prime-Advances in Electrical Engineering, Electronics and Energy, 2024, 10:100805.

朱振山,翁凯亮,欧阳海涛.基于动态参与因子和自适应模型预测控制的电池储能二次调频控制策略[J].电工技术学报, 2026, 41(1):343-358.ZHU Zhenshan, WENG Kailiang, OUYANG Haitao.Secondary frequency modulation control strategy for battery energy storage based on dynamic participation factor and adaptive model predictive control[J].Transactions of China Electrotechnical Society, 2026,41(1):343-358.

ZHANG F, HU Z, MENG K, et al. Hess sizing methodology for an existing thermal generator for the promotion of AGC response ability[J]. IEEE Transactions on Sustainable Energy, 2020, 11(2):608-617.

魏乐,苏少忻,房方,等.基于负荷预测的飞轮-火电系统自动发电控制响应性能优化[J].热力发电, 2023,52(5):92-99.WEI Le, SU Shaoxin, FANG Fang, et al. Optimization of automatic generation control response performance of flywheel-thermal power system based on load forecasting[J]. Thermal Power Generation, 2023, 52(5):92-99.

姚钦才,向文国,陈时熠,等.基于ICEEMDANKPCA-ICPA-LSTM的光伏发电功率预测[J].动力工程学报, 2025, 45(3):374-382.YAO Qincai, XIANG Wenguo, CHEN Shiyi, et al.Photovoltaic power forecasting based on ICEEMDANKPCA-ICPA-LSTM[J]. Journal of Chinese Society of Power Engineering, 2025, 45(3):374-382.

付炳喆,王玮,任国瑞,等.考虑增强特征选择的深度卷积时序网络短期风功率预测[J].动力工程学报,2024, 44(10):1565-1573.FU Bingzhe, WANG Wei, REN Guorui, et al. Consider enhancing feature selection in deep convolutional temporal networks for short-term wind power forecasting[J]. Journal of Chinese Society of Power Engineering, 2024, 44(10):1565-1573.

朱晨曦,张焰,严正,等.采用改进马尔科夫链蒙特卡洛法的风电功率序列建模[J].电工技术学报, 2020,35(3):577-589.ZHU Chenxi, ZHANG Yan, YAN Zheng, et al. A wind power time series modeling method based on the improved Markov chain Monte Carlo method[J].Transactions of China Electrotechnical Society, 2020,35(3):577-589.

张帅,杨晶显,刘继春,等.基于多尺度时序建模与估计的电力负荷数据恢复[J].电工技术学报, 2020,35(13):2736-2746.ZHANG Shuai, YANG Jingxian, LIU Jichun, et al. Power load recovery based on multi-scale time-series modeling and estimation[J]. Transactions of China Electrotechnical Society, 2020, 35(13):2736-2746.

HONG Y, CHANG W, CHANG Y, et al. Optimal sizing of renewable energy generations in a community microgrid using Markov model[J]. Energy, 2017, 135:68-74.

XIE S, HE H, PENG J. An energy management strategy based on stochastic model predictive control for plug-in hybrid electric buses[J]. Applied Energy, 2017, 196:279-288.

GOLCHOUBIAN P, AZAD N L, PONNAMBALAM K.Stochastic nonlinear model predictive control of batterysupercapacitor hybrid energy storage systems in electric vehicles[C]. 2017 American Control Conference(ACC).Seattle, WA, USA, 2017.

何俊强,师长立,韦统振.基于马尔科夫链的自适应储能需求功率预测模型[J].电工技术学报, 2021, 36(增刊2):563-571.HE Junqiang, SHI Changli, WEI Tongzhen. Adaptive power demand prediction model of energy storage based on Markov chain[J]. Transactions of China Electrotechnical Society, 2021, 36(Suppl.2):563-571.

殷展翔,陆锋,潘庭龙,等.基于FCS-MPC的分布式HESS功率协调控制[J].太阳能学报, 2024, 45(1):442-449.YIN Zhanxiang, LU Feng, PAN Tinglong, et al.Distributed hybrid energy storage system power coordination control based on FCS-MPC[J]. Acta Energiae Solaris Sinica, 2024, 45(1):442-449.

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