Research on the energy storage configuration method based on entropy theory

doi:10.19799/j.cnki.2095-4239.2024.1229

Abstract

Abstract:

Inspired by the thermodynamic concept of Clausius entropy, power entropy can be used to assess the adjustment ability of energy systems. Power entropy exhibits a consistent positive correlation with key energy storage indicators, such as power, duration, and capacity. Building on previous research, this study proposes a calibration method for evaluating system adjustment ability within a power entropy framework and discusses its application in optimizing energy storage configurations under specific conditions. The results demonstrate that under different energy storage configurations, the system power entropy decreased by 33%. The system optimization progresses as the power entropy increase introduced by the energy storage decreases, which corresponds to reduced redundancy in the energy storage regulation performance. The power entropy concept provides a new idea for the optimization of energy storage configurations in future power systems and the optimization of multiple energy forms in integrated energy systems.

Key words: energy storage, optimize configuration, power entropy, normalization, functional analysis

CLC Number:

TM 911

Zhenxin SUN, Zhiming ZHANG, Yi ZHANG, Haizhao LI, Haiyan LIAO, Liangjie WEI. Research on the energy storage configuration method based on entropy theory[J]. Energy Storage Science and Technology, 2025, 14(6): 2431-2438.

Figures/Tables 9

Fig. 1

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig.5

Fig. 6

Fig. 7

Fig. 8

References 21

1	李相俊, 马会萌, 姜倩. 新能源侧储能配置技术研究综述[J]. 中国电力, 2022, 55(1): 13-25. DOI: 10.11930/j.issn.1004-9649.2021 09032.
	LI X J, MA H M, JIANG Q. Review of energy storage configuration technology on renewable energy side[J]. Electric Power, 2022, 55(1): 13-25. DOI: 10.11930/j.issn.1004-9649.202109032.
2	田蓓, 王朝晖, 张爽, 等. 面向风光综合消纳的电力系统广域储能容量优化配置研究[J]. 智慧电力, 2020, 48(6): 67-72.
	TIAN B, WANG Z H, ZHANG S, et al. Wide-area optimized allocation of energy storage capacity considering wind/photovoltaic power accommodation in power systems[J]. Smart Power, 2020, 48(6): 67-72.
3	夏新茂, 关洪浩, 丁鹏飞, 等. 基于改进型量子遗传算法的储能系统容量配置与优化策略[J]. 储能科学与技术, 2019, 8(3): 551-558.
	XIA X M, GUAN H H, DING P F, et al. Capacity allocation and optimization strategy of an energy storage system based on an improved quantum genetic algorithm[J]. Energy Storage Science and Technology, 2019, 8(3): 551-558.
4	马速良, 马会萌, 蒋小平, 等. 基于Bloch球面的量子遗传算法的混合储能系统容量配置[J]. 中国电机工程学报, 2015, 35(3): 592-599. DOI: 10.13334/j.0258-8013.pcsee.2015.03.011.
	MA S L, MA H M, JIANG X P, et al. Capacity configuration of the hybrid energy storage system based on the Bloch spherical quantum genetic algorithm[J]. Proceedings of the CSEE, 2015, 35(3): 592-599. DOI: 10.13334/j.0258-8013.pcsee.2015.03.011.
5	张晴, 李欣然, 杨明, 等. 净效益最大的平抑风电功率波动的混合储能容量配置方法[J]. 电工技术学报, 2016, 31(14): 40-48. DOI: 10.19595/j.cnki.1000-6753.tces.2016.14.005.
	ZHANG Q, LI X R, YANG M, et al. Capacity determination of a hybrid energy storage system for smoothing wind power fluctuations with maximum net benefit[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 40-48. DOI: 10.19595/j.cnki.1000-6753.tces. 2016.14.005.
6	董昱, 范高锋, 董存, 等. 电力系统配置储能分析计算方法[J]. 中国电力, 2022, 55(1): 26-36. DOI: 10.11930/j.issn.1004-9649.202101138.
	DONG Y, FAN G F, DONG C, et al. Analysis and calculation of the energy storage configuration in power system[J]. Electric Power, 2022, 55(1): 26-36. DOI: 10.11930/j.issn.1004-9649.2021 01138.
7	曾紫光. 基于经济效益的风/储系统容量优化配置及研究[D]. 南昌: 南昌大学, 2014.
8	曹一家, 王光增, 曹丽华, 等. 基于潮流熵的复杂电网自组织临界态判断模型[J]. 电力系统自动化, 2011, 35(7): 1-6.
	CAO Y J, WANG G Z, CAO L H, et al. An identification model for the self-organized criticality of power grids based on power flow entropy[J]. Automation of Electric Power Systems, 2011, 35(7): 1-6.
9	赵源上, 林伟芳. 基于皮尔逊相关系数融合密度峰值和熵权法典型场景研究[J]. 中国电力, 2023, 56(5): 193-202.
	ZHAO Y S, LIN W F. Research on typical scenarios based on the fusion density peak value and entropy weight method of Pearson's correlation coefficient[J]. Electric Power, 2023, 56(5): 193-202.
10	蒋群. 电力系统可靠性裕度评估[D]. 北京: 华北电力大学, 2011.
11	翟峰, 冯云, 程凯, 等. 基于信息熵的多源电力物联终端设备信任度评价方法[J]. 中国电力, 2022, 55(5): 158-165.
	ZHAI F, FENG Y, CHENG K, et al. Information entrop-basedd multisource power IoT terminal equipment trust degree evaluation method[J]. Electric Power, 2022, 55(5): 158-165.
12	徐艳春, 赵彩彩, 孙思涵, 等. 基于改进LMD和能量相对熵的主动配电网故障定位方法[J]. 中国电力, 2021, 54(11): 133-143.
	XU Y C, ZHAO C C, SUN S H, et al. Fault location for the active distribution network based on improved LMD and energy relative entropy[J]. Electric Power, 2021, 54(11): 133-143.
13	钟静, 吕飞鹏, 孔德洪, 等. 基于加权潮流转移熵的电网脆弱线路辨识[J]. 电测与仪表, 2016, 53(10): 22-26. DOI: 10.3969/j.issn.1001-1390.2016.10.004.
	ZHONG J, LV F P, KONG D H, et al. Vulnerable line identification of the power grid based on the weighted transfer entropy of the power flow[J]. Electrical Measurement & Instrumentation, 2016, 53(10): 22-26. DOI: 10.3969/j.issn.1001-1390.2016.10.004.
14	李志民, 李卫星, 王永建. 基于熵理论的最优潮流代理约束算法[J]. 电力系统自动化, 2001, 25(11): 28-31. DOI: 10.3321/j.issn: 1000-1026.2001.11.007.
	LI Z M, LI W X, WANG Y J. Surrogate constraint algorithm for the optimal power flow based on the entropy theory[J]. Automation of Electric Power Systems, 2001, 25(11): 28-31. DOI: 10.3321/j.issn: 1000-1026.2001.11.007.
15	朱成骐, 孙宏斌, 张伯明. 基于最大信息熵原理的短期负荷预测综合模型[J]. 中国电机工程学报, 2005, 25(19): 1-6. DOI: 10.13334/j. 0258-8013.pcsee.2005.19.001.
	ZHU C Q, SUN H B, ZHANG B M. A combined model for short-term load forecasting based on maximum entropy principle[J]. Proceedings of the CSEE, 2005, 25(19): 1-6. DOI: 10.13334/j.0258-8013.pcsee.2005.19.001.
16	谢敬东, 陆池鑫, 鲁思薇, 等. 基于序关系-熵权法的电力市场风险评估[J]. 中国电力, 2021, 54(6): 71-78. DOI: 10.11930/j.issn.1004-9649.202006099.
	XIE J D, LU C X, LU S W, et al. Electricity market risk evaluation based on the order relation-entropy weight method[J]. Electric Power, 2021, 54(6): 71-78. DOI: 10.11930/j.issn.1004-9649.2020 06099.
17	管霖, 卓映君, 周保荣, 等. 复杂波动时间序列的多尺度分解算法及其在可再生能源发电建模应用中的性能评估[J]. 南方电网技术, 2020, 14(6): 11-16, 32. DOI: 10.13648/j.cnki.issn1674-0629.2020. 06.002.
	GUAN L, ZHUO Y J, ZHOU B R, et al. Multi-scale decomposition algorithms for complicated fluctuant time series and their performance evaluation in renewable energy generation modeling[J]. Southern Power System Technology, 2020, 14(6): 11-16, 32. DOI: 10.13648/j.cnki.issn1674-0629.2020.06.002.
18	陈中, 胡吕龙, 丁楠. 基于改进熵的风光储互补并网系统优化运行[J]. 电力系统保护与控制, 2013, 41(21): 86-91.
	CHEN Z, HU L, DING N. Optimized operation of the wind-solar-battery hybrid power system based on improved entropy[J]. Power System Protection and Control, 2013, 41(21): 86-91.
19	ZHOU G Y, CHAN C C, ZHANG D, et al. Smart energy evolution roadmap based on the correlation between energy and information[J]. Energy Procedia, 2019, 158: 3082-3087. DOI: 10.1016/j.egypro.2019.01.997.
20	孙振新, 张秩鸣, 马俯波, 等. 基于熵理论的能量调节性能研究[J]. 储能科学与技术, 2024, 13(5): 1584-1591. DOI: 10.19799/j.cnki. 2095-4239.2023.0940.
	SUN Z X, ZHANG Z M, MA F B, et al. Research on energy regulation performance based on entropy theory[J]. Energy Storage Science and Technology, 2024, 13(5): 1584-1591. DOI: 10.19799/j.cnki.2095-4239.2023.0940.
21	孙振新, 李海昭, 张秩鸣, 等. 克劳修斯熵在多时间尺度储能配置问题上的应用[J]. 热力发电, 2024, 53(9): 92-99. DOI: 10.19666/j.rlfd.202404081.
	SUN Z X, LI H Z, ZHANG Z M, et al. Application of Clausius entropy to energy storage configuration problems at multitime scale[J]. Thermal Power Generation, 2024, 53(9): 92-99. DOI: 10.19666/j.rlfd.202404081.

组别	参量	ES1	ES2	ES3	总和
第一组	P_max/kW	8	5	3	16
	T_cap/h	6.895	4.648	2.309	13.853
	E/kWh	55.163	23.242	6.928	85.333
	S/kW	16.530	8.657	3.590	28.777

第二组	P_max/kW	6	5	5	16
	T_cap/h	7.195	5.451	2.981	15.628
	E/kWh	43.170	27.257	14.907	85.333
	S/kW	12.621	9.321	6.908	28.851

最优解	P_max/kW	9.076	4.999	1.925	16
	T_cap/h	6.726	4.147	1.850	12.722
	E/kWh	61.040	20.733	3.560	85.333
	S/kW	18.556	8.191	2.016	28.763

[1]	Huimin FAN, Haohong PENG, Hui MENG, Menghong TANG, Haohao YI, Jing DING, Jincheng LIU, Chengshan XU, Xuning FENG. Research and simulation analysis of swelling force characteristics in energy storage battery modules [J]. Energy Storage Science and Technology, 2025, 14(6): 2488-2497.
[2]	Shuai YUAN, Yujie CUI, Donghao CHENG, Feng TAI, Jinzhong WU. Statistical analysis of fire and explosion accidents in electrochemical energy-storage stations from 2017 to 2024 throughout the world [J]. Energy Storage Science and Technology, 2025, 14(6): 2362-2376.
[3]	Zhaoting YIN, Wei GUO, Jinxin WANG, Yang MENG. Modification strategies and development prospects for positive electrodes for aqueous zinc-ion batteries [J]. Energy Storage Science and Technology, 2025, 14(6): 2320-2335.
[4]	Bin WANG, Jun TAN, Fenghe LI, Xinxing LIN, Sumin GUAN, Ruochen DING, Wen SU. Performance research of compression energy storage system with CO₂-based mixture [J]. Energy Storage Science and Technology, 2025, 14(6): 2391-2404.
[5]	Junbo HAO, Guangyin YAN, Honglan PEI, Yingxin ZHAO, Jin HAO. Revenue allocation strategy for cooperation alliance of distributed new energies and shared energy storage in the distribution network based on contribution degree [J]. Energy Storage Science and Technology, 2025, 14(6): 2462-2472.
[6]	Li HE, Zhaoxing LENG, Zhuangxi TAN, Xueyuan LI, Xiaowen WU, Chaoyang CHEN. Estimation of the state of charge of energy-storage batteries based on adaptive capacity considering the discharge rate [J]. Energy Storage Science and Technology, 2025, 14(6): 2405-2415.
[7]	Kangbin LIU, Haichuan SHEN, Guanjia ZHAO, Wentao XIE, Weiyao XUN. Experimental and numerical study of self-pressurized ultrahigh-pressure hydrothermal energy storage [J]. Energy Storage Science and Technology, 2025, 14(6): 2352-2361.
[8]	Yinchi SHAO, Yu GONG, Meng NIU, Ruohuan YANG, Yating LIU, Ran DING. Grid-forming energy storage system taking phase angle and amplitude jumps into account short-circuit current characteristics and its calculation model [J]. Energy Storage Science and Technology, 2025, 14(6): 2451-2461.
[9]	Jiabao TAN, Yufei WANG, Hua XUE. Modeling and performance analysis of piston gravity energy storage system [J]. Energy Storage Science and Technology, 2025, 14(6): 2383-2390.
[10]	Haisheng CHEN, Hong LI, Yujie XU, Dehou XU, Liang WANG, Xuezhi ZHOU, Man CHEN, Dongxu HU, Haibo LIN, Xianfeng LI, Yongsheng HU, Zhongxun AN, Yu LIU, Liye XIAO, Kai JIANG, Guobin ZHONG, Qingsong WNAG, Zhen LI, Feiyu KANG, Xuanpeng WANG, Zhao YIN, Xingjian DAI, Xipeng LIN, Yilin ZHU, Chi ZHANG, Yuxin ZHANG, Wei LIU, Fen YUE, Changkun ZHANG, Zhenhua YU, Rongbin DANG, Qingquan QIU, Shiqing CHEN, Zhuoqun SHI, Hualiang ZHANG, Haomiao LI, Cheng XU, Dong ZHOU, Zhichun SI, Zhen SONG, Xinyu ZHAO, Xuan LIU, Wenxin MEI. Research progress on China’s energy storage technology in 2024 [J]. Energy Storage Science and Technology, 2025, 14(6): 2149-2192.
[11]	Xiaoyu QU, Liuxin DOU. Innovative application and prospect analysis of energy storage technology in cold chain logistics of fresh agricultural products e-commerce [J]. Energy Storage Science and Technology, 2025, 14(6): 2336-2338.
[12]	Zheng LI, Xukai REN. Analysis on fire safety management measures for energy storage power stations [J]. Energy Storage Science and Technology, 2025, 14(6): 2377-2379.
[13]	Luxia ZHANG. Design and mechanism research of energy storage safety system based on reconfigurable battery [J]. Energy Storage Science and Technology, 2025, 14(6): 2380-2382.
[14]	Xun CHEN. Energy management and optimal scheduling strategies for energy storage systems based on deep reinforcement learning [J]. Energy Storage Science and Technology, 2025, 14(6): 2439-2441.
[15]	Zhenyu LIU, Jian CHEN, Zhaolei YIN, Manman YANG. Design of power grid energy storage joint dispatch strategy based on deep learning [J]. Energy Storage Science and Technology, 2025, 14(6): 2555-2557.