Research on optimization strategies for smart grid energy storage systems based on big data analysis

doi:10.19799/j.cnki.2095-4239.2025.0823

Abstract

Abstract:

With the rapid development of smart grids, the role of energy storage systems has become increasingly prominent. This paper discusses how to optimize smart grid energy storage systems using big data technologies, analyzes the current application status of big data in energy storage systems—including data collection, storage, and processing—and introduces optimization strategies for energy storage systems based on big data analysis, such as capacity configuration optimization, charging/discharging strategy optimization, operation status monitoring, and fault diagnosis. It also elaborates on the challenges encountered in these optimization strategies and prospects the future development trends.

Key words: smart grid, energy storage system, big data analysis, optimization strategy

CLC Number:

TM 727

Chen LIANG, Yixiang ZHANG. Research on optimization strategies for smart grid energy storage systems based on big data analysis[J]. Energy Storage Science and Technology, 2025, 14(10): 3811-3813.

References 4

[1]	刘瑞元, 刘家豪, 陈月, 等. 基于智能电网的电力大数据关键技术分析[J]. 通讯世界, 2024, 31(7): 118-120. DOI:10.3969/j.issn.1006-4222.2024.07.039.
	LIU R Y, LIU J H, CHEN Y, et al. Analysis on key technologies of power big data based on smart grid[J]. Telecom World, 2024, 31(7): 118-120. DOI:10.3969/j.issn.1006-4222.2024.07.039.
[2]	李一帆, 梁继元. 基于大数据技术的风光互补储能系统优化控制分析[J]. 电子技术, 2024, 53(1): 240-241.
	LI Y F, LIANG J Y. Analysis of optimization control of wind solar complementary energy storage system based on big data technology[J]. Electronic Technology, 2024, 53(1): 240-241.
[3]	刘甜甜, 彭放, 李文娟. 大数据背景下电网负荷预测方法研究[C]//2022电力行业信息化年会论文集. 北京:中国电机工程学会电力信息化专业委员会, 国家电网公司信息通信分公司, 2023: 54-58. DOI:10.26914/c.cnkihy.2023.053467.
[4]	曾雨桐, 储婧文. 基于大数据分析的智能用电服务系统优化策略研究[J]. 家电维修, 2024(10): 95-97.
	ZENG Y T, CHU J W. Research on optimization strategy of intelligent electricity service system based on big data analysis[J]. Appliance Repairing, 2024(10): 95-97.

[1]	Kailiang WANG, Yujun SUN, Jinxing ZHONG, Xiangyang SU, Junhui LI, Zongyang LIU, Yu CAI, Yidan CHEN. Hybrid energy storage configuration for regional distribution network considering energy storage lifespan and empirical mode decomposition [J]. Energy Storage Science and Technology, 2025, 14(9): 3417-3430.
[2]	Youjun YANG, Jian MA, Jie SUN. Research on intelligent operation and maintenance of photovoltaic energy storage systems based on big data and artificial intelligence [J]. Energy Storage Science and Technology, 2025, 14(9): 3414-3416.
[3]	Lei ZHANG. Operating status monitoring and evaluation of lithium-ion battery energy storage power stations [J]. Energy Storage Science and Technology, 2025, 14(9): 3538-3540.
[4]	Zheng ZHOU. Real time monitoring of electricity consumption information and collaborative control of distributed energy storage systems in smart grids [J]. Energy Storage Science and Technology, 2025, 14(9): 3619-3621.
[5]	Bowen LI, Xiankui WEN, Qiang FAN, Tingyun GU, Zhengjun SHI, Xiaoyin ZHANG. Experimental study on heat dissipation through circulation in the hollow shaft of MW-class flywheel motor rotor [J]. Energy Storage Science and Technology, 2025, 14(8): 2925-2931.
[6]	Chengshan XU, Ye SUN, Zhikai YANG, Mingqiang ZHAO, Yalun LI, Xuning FENG, Hewu WANG, Languang LU, Minggao OUYANG. Research progress on arc induced by thermal runaway in lithium-ion battery systems for energy storage [J]. Energy Storage Science and Technology, 2025, 14(8): 3037-3050.
[7]	Xinkai SU, Lulu ZHAO, Yanqiao CHEN, Chu WANG, Huanjun CHEN, Yi JIN. Review of the research on industrialization and applications of supercapacitors [J]. Energy Storage Science and Technology, 2025, 14(8): 2994-3003.
[8]	Liyue HU, Wei HUANG, Yun ZHOU, Yingqiang ZHOU, Changzheng SHAO, Ke WANG. Fuzzy reasoning-based evaluation of the thermal diffusion probability of lithium-ion battery modules for energy storage systems [J]. Energy Storage Science and Technology, 2025, 14(7): 2662-2674.
[9]	Yuan LI, Mingzhi ZHAO, Yujie XU, Jie CAI. Variable-operating-condition operational characteristics of liquid carbon dioxide energy storage systems [J]. Energy Storage Science and Technology, 2025, 14(7): 2761-2771.
[10]	Xiaohui ZHANG, Ruigeng YANG, Songkun JIAO. Research on capacity planning and demand forecasting for energy storage systems based on machine learning [J]. Energy Storage Science and Technology, 2025, 14(7): 2881-2883.
[11]	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.
[12]	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.
[13]	Junyang XIAO, Jinge LUO, Weizhe MA, Wuping CHENG, Tong ZENG. Energy storage optimization control strategy in distribution system based on improved artificial bee colony algorithm [J]. Energy Storage Science and Technology, 2025, 14(6): 2567-2574.
[14]	Qingxiang XU, Wei TENG, Run QIN, Shunyi SONG, Yibing LIU, Shuangyin LIANG. Energy management and control strategy for grid-connected frequency regulation flywheel energy storage systems [J]. Energy Storage Science and Technology, 2025, 14(5): 2013-2022.
[15]	Bing YAN, XU Hu, Zhenling LI. Research on intelligent operation and maintenance model of energy storage systems supported by big data [J]. Energy Storage Science and Technology, 2025, 14(5): 2010-2012.