典型调峰/调频工况下储能电池组荷电状态估计

doi:10.11930/j.issn.1004-9649.202401003

中国电力 ›› 2024, Vol. 57 ›› Issue (6): 18-26.DOI: 10.11930/j.issn.1004-9649.202401003

• 储能用锂离子电池本体安全关键技术 • 上一篇下一篇

典型调峰/调频工况下储能电池组荷电状态估计

朱沐雨¹(), 马宏忠¹(), 郭鹏宇²(), 宣文婧¹

1. 河海大学电气与动力工程学院，江苏南京　211100
2. 国网江苏省电力有限公司，江苏南京　210024

收稿日期:2024-01-02 出版日期:2024-06-28 发布日期:2024-06-25
作者简介:朱沐雨（2000—），男，硕士研究生，从事电化学储能安全技术研究，E-mail：962307019@qq.com
马宏忠（1962—），男，通信作者，博士，教授，从事电力设备状态监测和故障诊断技术研究，E-mail：hhumhz@163.com
郭鹏宇（1972—），男，硕士，高级工程师，从事电化学储能安全技术和电力设备消防技术研究，E-mail：13611511237@163.com
基金资助:
国家自然科学基金资助项目（双馈异步发电机内部故障的振动（声学）机理分析与机电（声）融合诊断研究，51577050）；国网江苏省电力有限公司科技项目（不同网储互动模式下储能电池安全性能研究，J2022158）。

State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions

Muyu ZHU¹(), Hongzhong MA¹(), Pengyu GUO²(), Wenjing XUAN¹

1. College of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
2. State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China

Received:2024-01-02 Online:2024-06-28 Published:2024-06-25
Supported by:
This work is supported by National Natural Science Foundation of China (Vibration (Acoustic) Mechanism Analysis and Electromechanical (Acoustic) Fusion Diagnosis of Internal Faults in Doubly-Fed Induction Generator, No.51577050) and Science & Technology Project of State Grid Jiangsu Electric Power Co., Ltd. (Research on Safety Performance of Energy Storage Battery Under Different Network Storage Interaction Modes, No.J2022158).

摘要/Abstract

摘要：

针对储能电池组在电网典型储能工况下荷电状态（state of charge，SOC）估算精度较低的问题，提出一种基于核主成分分析（kernel principal component analysis，KPCA）-鹈鹕优化（pelican optimization algorithm，POA）-双向门控循环单元（bidirectional gated recurrent unit，BiGRU）的SOC估计模型。通过设计调峰/调频工况下电池组充放电实验，从数据中提取表征SOC变化的融合特征作为模型输入；分别构建不同工况下BiGRU网络，并利用POA对其超参数进行优化，提高模型性能；进一步在混合工况下验证模型的有效性。结果表明，所建模型有着更好的SOC估计效果和更强的鲁棒性，能够提高复杂储能工况下储能电池组SOC估计精度。

关键词: 储能电池组, 荷电状态估计, 调峰调频, 鹈鹕优化, 双向门控循环单元

Abstract:

To address the issue of low estimation accuracy of the state of charge (SOC) for an energy storage battery pack under typical energy storage conditions of a power grid, this paper proposes a new SOC estimation model based on kernel principal component analysis (KPCA), pelican optimization algorithm (POA), and bidirectional gated recurrent unit (BiGRU). By designing the charge and discharge experiment of a battery pack under the condition of peak/frequency modulation, the paper extracts the fusion features of SOC change from the data as the model input. BiGRU networks are constructed under different working conditions, and POA is utilized to optimize its hyperparameters to improve the model's performance. The effectiveness of the model is further verified under mixed conditions. The results show that the proposed model has better SOC estimation performance and stronger robustness, which can improve the SOC estimation accuracy of energy storage battery packs under complex energy storage conditions.

Key words: energy storage battery pack, state of charge estimation, peak and frequency modulation, pelican optimization, bidirectional gated recurrent unit

朱沐雨, 马宏忠, 郭鹏宇, 宣文婧. 典型调峰/调频工况下储能电池组荷电状态估计[J]. 中国电力, 2024, 57(6): 18-26.

Muyu ZHU, Hongzhong MA, Pengyu GUO, Wenjing XUAN. State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions[J]. Electric Power, 2024, 57(6): 18-26.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202401003

https://www.electricpower.com.cn/CN/Y2024/V57/I6/18

图/表 14

图 1 BiGRU结构示意

Fig.1 BiGRU structure

图 2 SOC估计流程

Fig.2 Flow of SOC estimation

图 3 磷酸铁锂电池组

Fig.3 Lithium iron phosphate battery pack

表 1 电池组参数

Table 1 Battery pack parameters

型号	标称电压/V	标称容量/(A·h)
MCRSA08-LC	25.6	220

电池组尺寸/mm	工作温度/℃	质量/kg
555×430×154	–20~55	60

图 4 实验平台

Fig.4 Experimental platform

图 5 不同工况下电流指令

Fig.5 Current instructions under different working conditions

图 6 电池组数据监测

Fig.6 Battery pack data monitoring

图 7 不同工况下贡献率和特征值分布

Fig.7 Contribution rates and eigenvalue distribution under different working conditions

图 8 不同输入特征下SOC估计对比

Fig.8 Comparison of SOC estimation with different input features

表 2 不同输入特征下误差评估指标

Table 2 Error evaluation indexes of different input characteristics

工况	是否降维	E_RMS	E_MA	R²
调峰	是	0.008645	0.008527	0.9987
调峰	否	0.033260	0.022370	0.9406
调频	是	0.009983	0.010410	0.9930
调频	否	0.061020	0.047480	0.7753

图 9 不同学习模型下SOC估计对比

Fig.9 Comparison of SOC estimation with different learning models

表 3 不同学习模型下误差评估指标

Table 3 Error evaluation indexes with different learning models

工况	模型	E_RMS	E_MA	R²
调峰	POA-BiGRU	0.008645	0.008527	0.9987
	KELM	0.024090	0.019680	0.9654
	BiLSTM	0.011050	0.012310	0.9892
	BiGRU	0.011360	0.012440	0.9875
调频	POA-BiGRU	0.009983	0.010410	0.9930
	KELM	0.037650	0.022560	0.9532
	BiLSTM	0.015780	0.011020	0.9785
	BiGRU	0.016620	0.012330	0.9771

图 10 混合工况下SOC估计效果

Fig.10 SOC estimation effect under mixed conditions

表 4 混合工况下误差评估指标

Table 4 Error evaluation indexes under mixed conditions

模型	E_RMS	E_MA	R²
单模型	0.02872	0.025460	0.9755
双模型	0.01097	0.009862	0.9936

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[1]	杨胡萍, 余阳, 汪超, 李向军, 胡奕涛, 饶楚楚. 基于VMD-CNN-BIGRU的电力系统短期负荷预测[J]. 中国电力, 2022, 55(10): 71-76.
[2]	曾囿钧, 肖先勇, 徐方维, 郑林. 基于CNN-BiGRU-NN模型的短期负荷预测方法[J]. 中国电力, 2021, 54(9): 17-23.

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State of Charge Estimation of Energy Storage Battery Pack under Typical Peak/Frequency Modulation Conditions

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