电池储能温度-功率特性建模及其在综合能源系统中的应用

doi:10.19595/j.cnki.1000-6753.tces.231028

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (855 KB)
输出: BibTeX | EndNote (RIS)

摘要

针对综合能源系统（integrated energy system, IES）中电池储能在极端高温、低温下寿命衰减快、性能差的问题,本文提出含温度-功率特性的电池储能运行模型及含电池储能温度控制的IES低碳经济调度方法。通过电池特性实验和软件仿真,构建电池电热耦合模型,并提出相应的电热耦合模型凸化方法;根据凸化电池电热耦合模型估算电池温度并构建含温度-功率特性的电池储能运行模型,分析电、气、冷、热多能流耦合特性,构建IES低碳经济运行模型;结合电池储能运行模型及其温度控制,以最小化IES总运行成本为目标,构建混合整数规划模型,制定IES经济调度策略。为验证所提方法的效果,本文根据不同季节运行场景,构建仿真模型,将所提方法与不包含电池储能温度控制的方法进行对比。对比结果表明,所提方法可以有效提升电池储能运行的使用寿命、热安全性及利用率,降低IES运行成本与碳排放量。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	熊康
	黎灿兵
	樊飞龙
	李新喜
	杨汶圣

关键词 ：综合能源系统, 电池储能, 温度特性, 温度控制, 优化调度

Abstract：

To mitigate the fluctuations of new energy output, battery energy storage is widely used in integrated energy system (IES). However, its performance and lifespan are significantly affected by temperature. Neglecting the influence of temperature on battery energy storage operations leads to several critical issues: a) The charging and discharging power of the battery is constrained at extreme high temperature and low temperature, thereby preventing the full absorption of new energy output. b) Extreme temperatures accelerate battery degradation, resulting in increased costs and reduced longevity. To address these challenges, it is necessary to study temperature-dependent charge/discharge characteristics and lifespan deterioration of battery energy storage under extreme temperature conditions and establish corresponding temperature-power and temperature-lifespan deterioration models , and then consider the above models in the optimal scheduling of the IES, so as to improve the thermal safety of battery energy storage and the ability to absorb new energy output, and reduce the lifespan loss of battery energy storage and system operating costs.
This paper proposed a battery energy storage operation model with temperature-power characteristics and an IES low carbon economic dispatch method with battery energy storage temperature control. The key steps can be summarized as follows:
1)Convex Electric-Thermal Coupling Model: The study begins by constructing an electric-thermal coupling model through a combination of battery experiments and simulations. This model is further refined using convexity techniques. It accurately estimates battery temperature based on electric-thermal coupling, allowing for precise quantification of temperature-dependent power output and lifespan deterioration rates.
2)Battery Energy Storage Operation Model: The developed model encapsulates temperature-power and temperature-lifespan deterioration characteristics. Battery temperature estimation, derived from the electric-thermal coupling model, plays a pivotal role. This comprehensive model is employed to analyze the interdependencies between electricity, gas, cold, and heat multi-energy flows, consequently leading to the formulation of an IES low-carbon economic operation model.
3)Integrated Planning Model: Through the amalgamation of the battery energy storage model and its temperature control aspects, a mixed-integer planning model is devised. The primary goal is to minimize the total operating cost of the IES. This gives rise to an IES low-carbon economic dispatch strategy. To verify the effectiveness of the proposed method, a simulation model is constructed according to different seasonal operation scenarios, and the proposed method was compared with the method that does not include temperature control of battery energy storage.
The theoretical analysis and case simulations yield the following conclusions: (1)Convex the battery electric-thermal coupling model, estimate the battery temperature based on the convex model, quantify the battery energy storage temperature-power output capability, temperature- lifespan deterioration rate, and control the battery energy storage temperature and power. In this way, it can avoid damage to the battery due to overcharging and discharging at extreme high temperature and low temperature. After calculation, the method in this paper can reduce the lifespan loss cost of battery energy storage by 30.97% and 69.64% in typical summer and winter days compared with that without considering the temperature control of battery energy storage. (2) According to the characteristics of battery energy storage temperature-power output capability, an IES low-carbon economic scheduling strategy including battery energy storage temperature control can be proposed, and the power output capability of the battery energy storage temperature can be improved by controlling the battery energy storage temperature. In this way, the ability to absorb new energy output can be improved, and the operating cost of IES can be reduced. After calculation, the method in this paper can reduce the operating cost of IES by 6.72% and 13.78% in typical days in summer and winter compared with that without considering the temperature control of battery energy storage.

Key words： Integrated energy system battery energy storage temperature characteristics temperature control optimize scheduling

收稿日期: 2023-07-03

PACS:

TM74

基金资助:

国家自然科学基金(51977062)和上海市浦江人才计划（22PJ1405000）资助项目

通讯作者: 黎灿兵男,1979年生,教授,博士生导师,研究方向为电力系统分析与控制。E-mail：licanbing@sjtu.edu.cn

作者简介: 熊康男,1998年生,硕士研究生,研究方向为综合能源系统优化、电池储能技术。E-mail：575901039@sjtu.edu.cn

引用本文:

熊康, 黎灿兵, 樊飞龙, 李新喜, 杨汶圣. 电池储能温度-功率特性建模及其在综合能源系统中的应用[J]. 电工技术学报, 0, (): 231028-. Xiong Kang, Li Canbing, Fan Feilong, Li Xinxi, Yang Wensheng. Modeling of Temperature-Power Characteristics of Battery Energy Storage and Its Application in Integrated Energy System. Transactions of China Electrotechnical Society, 0, (): 231028-.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.231028 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/231028

[1] 本报评论员. 把握好“十四五”碳达峰关键期窗口期[N]. 延安日报, 2021-03-23.
[2] 谢小荣, 马宁嘉, 刘威, 等. 新型电力系统中储能应用功能的综述与展望[J]. 中国电机工程学报, 2023, 43(1): 158-168, 中插13.
Xie Xiaorong, Ma Ningjia, Liu Wei, et al.Functions of energy storage in renewable energy dominated power systems: review and prospect[J]. Proceedings of the CSEE, 2023, 43(1): 158-168, 13.
[3] Olabi A G, Onumaegbu C, Wilberforce T, et al.Critical review of energy storage systems[J]. Energy, 2021, 214: 118987.
[4] 张谦, 邓小松, 岳焕展, 等. 计及电池寿命损耗的电动汽车参与能量-调频市场协同优化策略[J]. 电工技术学报, 2022, 37(1): 72-81.
Zhang Qian, Deng Xiaosong, Yue Huanzhan, et al.Coordinated optimization strategy of electric vehicle cluster participating in energy and frequency regulation markets considering battery lifetime degradation[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 72-81.
[5] Chen Rusong, Nolan A M, Lu Jiaze, et al.The thermal stability of lithium solid electrolytes with metallic lithium[J]. Joule, 2020, 4(4): 812-821.
[6] 黄德扬, 陈自强, 周诗尧, 等. 极寒环境下动力锂离子电池特性[J]. 上海交通大学学报, 2019, 53(9): 1051-1057.
Huang Deyang, Chen Ziqiang, Zhou Shiyao, et al.Characteristics of power lithium-ion batteries at extreme cold environment[J]. Journal of Shanghai Jiao Tong University, 2019, 53(9): 1051-1057.
[7] Li Yang, Bai Minli, Zhou Zhifu, et al.Experimental investigations of liquid immersion cooling for 18650 lithium-ion battery pack under fast charging conditions[J]. Applied Thermal Engineering, 2023, 227: 120287.
[8] 李懿洋. 锂离子电池低温充放电循环与高温浮充下的失效机理研究[D]. 北京: 清华大学, 2017.
[9] Liu Jiexun, Gao Dawei, Cao Jianhua.Study on the effects of temperature on LiFePO₄ battery life[C]//2012 IEEE Vehicle Power and Propulsion Conference, Seoul, Korea (South), 2013: 1436-1440.
[10] 孙金磊, 朱春波, 李磊, 等. 电动汽车动力电池温度在线估计方法[J]. 电工技术学报, 2017, 32(7): 197-203.
Sun Jinlei, Zhu Chunbo, Li Lei, et al.Online temperature estimation method for electric vehicle power battery[J]. Transactions of China Electrotechnical Society, 2017, 32(7): 197-203.
[11] 刘素贞, 陈晶晶, 张闯, 等. 基于区域电压的锂离子电池不均匀发热模型[J]. 电工技术学报, 2022, 37(21): 5627-5636.
Liu Suzhen, Chen Jingjing, Zhang Chuang, et al.Regional voltage-based uneven heating model of lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5627-5636.
[12] 陈英杰, 杨耕, 祖海鹏, 等. 基于恒流实验的锂离子电池开路电压与内阻估计方法[J]. 电工技术学报, 2018, 33(17): 3976-3988.
Chen Yingjie, Yang Geng, Zu Haipeng, et al.An open circuit voltage and internal resistance estimation method of lithium-ion batteries with constant current tests[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3976-3988.
[13] 姜余, 陈自强. 可变环境温度下锂离子电池平均温度估计[J]. 上海交通大学学报, 2021, 55(7): 781-790.
Jiang Yu, Chen Ziqiang.Average temperature estimation for lithium-ion batteries at variable environment temperature[J]. Journal of Shanghai Jiao Tong University, 2021, 55(7): 781-790.
[14] 孙丙香,宋东林,阮海军,等.基于自产热和外传热的锂离子电池热学模型参数辨识方法[J/OL].电工技术学报. https://doi.org/10.19595/j.cnki.1000-6753.tces.222104
[15] Ng B, Coman P T, Mustain W E, et al.Non-destructive parameter extraction for a reduced order lumped electrochemical-thermal model for simulating Li-ion full-cells[J]. Journal of Power Sources, 2020, 445: 227296.
[16] Li Shi, Pischinger S, He Chaoyi, et al.A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test[J]. Applied Energy, 2018, 212: 1522-1536.
[17] 李建林, 李雅欣, 刘海涛, 等. 计及储能电站安全性的功率分配策略研究[J]. 电工技术学报, 2022, 37(23): 5976-5986.
Li Jianlin, Li Yaxin, Liu Haitao, et al.Research on power distribution strategy considering the safety of energy storage power station[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5976-5986.
[18] 李中浩, 余娟, 杨知方, 等. 精准计及大规模储能电池寿命的电力系统经济调度[J/OL]. 中国电机工程学报, 2022: 1-13. (2022-09-05). https://kns.cnki.net/kcms/detail/11.2107.TM.20220902.1535.003.html.
Li Zhonghao, Yu Juan, Yang Zhifang, et al. Economic dispatch of power system accurately considering the life of large-scale energy storage battery[J/OL]. Proceedings of the CSEE, 2022: 1-13. (2022-09-05). https://kns.cnki.net/kcms/detail/11.2107.TM.20220902.1535.003.html.
[19] Liu Chunyang, Ma Houzhen, Zhang Hengxu, et al.A MILP-based battery degradation model for economic scheduling of power system[J]. IEEE Transactions on Sustainable Energy, 2023, 14(2): 1000-1009.
[20] 柴炜, 李征, 蔡旭, 等. 基于使用寿命模型的大容量电池储能系统变步长优化控制方法[J]. 电工技术学报, 2016, 31(14): 58-66.
Chai Wei, Li Zheng, Cai Xu, et al.Variable step-size control method of large capacity battery energy storage system based on the life model[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 58-66.
[21] 杨艳红, 裴玮, 邓卫, 等. 计及蓄电池储能寿命影响的微电网日前调度优化[J]. 电工技术学报, 2015, 30(22): 172-180.
Yang Yanhong, Pei Wei, Deng Wei, et al.Day-ahead scheduling optimization for microgrid with battery life model[J]. Transactions of China Electrotechnical Society, 2015, 30(22): 172-180.
[22] 傅晓梅, 温步瀛, 唐雨晨. 考虑电池储能运行特性的微网优化运行[J]. 电气技术, 2021, 22(4): 12-19.
Fu Xiaomei, Wen Buying, Tang Yuchen.Optimal operation of microgrid considering operation characteristics of battery energy storage[J]. Electrical Engineering, 2021, 22(4): 12-19.
[23] Liu Xiang, Ren Dongsheng, Hsu H, et al.Thermal runaway of lithium-ion batteries without internal short circuit[J]. Joule, 2018, 2(10): 2047-2064.
[24] 冯旭宁. 车用锂离子动力电池热失控诱发与扩展机理、建模与防控[D]. 北京: 清华大学, 2016.
[25] 蔡敏怡, 张娥, 林靖, 等. 串联锂离子电池组均衡拓扑综述[J]. 中国电机工程学报, 2021, 41(15): 5294-5311.
Cai Minyi, Zhang E, Lin Jing, et al.Review on balancing topology of lithium-ion battery pack[J]. Proceedings of the CSEE, 2021, 41(15): 5294-5311.
[26] Xu Bolun, Oudalov A, Ulbig A, et al.Modeling of lithium-ion battery degradation for cell life assessment[J]. IEEE Transactions on Smart Grid, 2018, 9(2): 1131-1140.
[27] Shi Yuanyuan, Xu Bolun, Wang Di, et al.Using battery storage for peak shaving and frequency regulation: joint optimization for superlinear gains[C]//2018 IEEE Power & Energy Society General Meeting (PESGM), Portland, OR, USA, 2018: 1.
[28] 熊焰, 吴杰康, 王强, 等. 风光气储互补发电的冷热电联供优化协调模型及求解方法[J]. 中国电机工程学报, 2015, 35(14): 3616-3625.
Xiong Yan, Wu Jiekang, Wang Qiang, et al.An optimization coordination model and solution for combined cooling, heating and electric power systems with complimentary generation of wind, PV, gas and energy storage[J]. Proceedings of the CSEE, 2015, 35(14): 3616-3625.