基于模型预测控制的混合储能平抑风电波动控制策略

宋子仪; 吕游; 尹昊蕊; 刘吉臻

doi:10.19805/j.cnki.jcspe.2026.250142

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基于模型预测控制的混合储能平抑风电波动控制策略

新能源与储能 | 更新时间：2026-07-16

- 基于模型预测控制的混合储能平抑风电波动控制策略
- Control Strategy for Mitigating Wind Power Fluctuations Using Hybrid Energy Storage Based on Model Predictive Control
- 动力工程学报 2026年46卷第7期页码：31-38
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,北京,102206
  2. 华北电力大学新能源电力系统全国重点实验室,北京,102206
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(U25B20164);中央高校基本科研业务费专项资金资助项目(2025MS024)
- DOI：10.19805/j.cnki.jcspe.2026.250142
  中图分类号： TM614
- 纸质出版：2026
- 稿件说明：
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宋子仪, 吕游, 尹昊蕊, 刘吉臻. 基于模型预测控制的混合储能平抑风电波动控制策略[J]. 动力工程学报, 2026, 46(7): 31-38.
宋子仪, 吕游, 尹昊蕊, 刘吉臻. 基于模型预测控制的混合储能平抑风电波动控制策略[J]. 动力工程学报, 2026, 46(7): 31-38. DOI： 10.19805/j.cnki.jcspe.2026.250142.

DOI： 10.19805/j.cnki.jcspe.2026.250142

摘要

针对风电功率波动性和随机性导致的并网功率平滑控制需求

在风电场侧配置锂电池-飞轮混合储能系统

提出一种考虑风电功率超短期预测、模型预测控制(MPC)滚动优化和混合储能功率分配的风电波动平抑控制策略。首先

在长短期记忆(LSTM)网络中加入注意力机制(AM)

对风电功率进行超短期预测

并将预测结果作为扰动量输入MPC模型进行修正

以提高模型预测精度;其次

采用MPC方法获取风电并网功率和储能目标平抑功率

在兼顾储能荷电状态(SOC)稳定的同时达到平滑风电输出的目的;然后

根据混合储能的不同特性

采用改进的自适应噪声完备集合经验模态分解(ICEEMDAN)法对锂电池和飞轮的实时功率进行合理分配;最后

结合风电场实际功率数据进行仿真分析。结果表明:所提控制策略能够明显改善电池SOC运行状态

在满足储能平抑风电功率需求的同时兼顾储能寿命

保证储能系统的充放电裕度充足

以应对系统未来可能出现的变化

并降低SOC可能出现突变的频率。

Abstract

To address the demand for smoothing grid-connected wind power caused by the inherent volatility and randomness of wind power

a lithium battery-flywheel hybrid energy storage system was configured on the wind-farm side

while a wind power fluctuation mitigation control strategy was proposed with considering ultra-short-term wind power prediction

model predictive control (MPC)-based rolling optimization

and hybrid energy storage power allocation. First

an attention mechanism (AM) was incorporated into long short-term memory (LSTM) network for ultra-short-term wind power prediction

and the prediction results were fed as disturbance inputs into MPC model for correction

thereby improving the model prediction accuracy. Second

the MPC method was employed to obtain the grid-connected wind power and the target smoothing power of the energy storage system

so that the wind power output has been smoothed while the stability of the state of charge (SOC) of the energy storage system has been maintained. Then

based on the different characteristics of hybrid energy storage components

the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method was applied to reasonably allocate the real-time power between the lithium battery and the flywheel. Finally

simulation analysis was carried out using actual wind farm power data. Results show that the battery SOC operating state can be significantly improved by the proposed control strategy

the wind power smoothing requirements of the energy storage system are met while the energy storage lifetime is taken into account

sufficient charge/discharge margins of the energy storage system are ensured to cope with possible future changes in the system

and the frequency of abrupt SOC variations is reduced.

关键词

Keywords

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