Stochastic model predictive control for hybrid energy storage assisted thermal power unit in AGC based on adaptive Markov power prediction

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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Stochastic model predictive control for hybrid energy storage assisted thermal power unit in AGC based on adaptive Markov power prediction

Thermal Power Generation Vol. 55, Issue 2, Pages: 147-157(2026)

更新时间：2026-08-07

- 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：
- DOI：10.19666/j.rlfd.202511048
  CLC： TM621;TP273
- Published：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

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