基于竞争合作机制及IBCA的BESS低损均衡功率分配策略

doi:10.19595/j.cnki.1000-6753.tces.240593

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (2445 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

In order to reduce the lifespan loss of battery energy storage systems (BESS) when suppressing unbalanced power in microgrids, while ensuring its adjustability, this paper proposes a low loss balanced power allocation strategy for BESS based on competitive cooperation mechanism and improved bipartite consistency algorithm (IBCA). Firstly, in order to promote the balance of individual state of charge (SOC), the event driven BESS grouping model is established. Further considering the individual lifespan loss and the adjustable ability of BESS, the BESS power allocation model based on competition and cooperation mechanism is designed, which proposes the design concept of competition and cooperation mechanism, constructs four power allocation modes based on competition and cooperation mechanism, and establishes corresponding BESS power allocation objective functions; Secondly, the bipartite consistency algorithm(BCA) is selected as the BESS power allocation implementation algorithm. Taking into account communication delay, the state feedback mechanism, power allocation weighting matrix, and gauge transformation matrix are introduced into the bipartite consistency algorithm to form improved-BCA (IBCA); Subsequently, the BESS power allocation strategy based on the packet power allocation model is designed and the IBCA is utilized to complete the BESS power allocation; Finally, the performance of the proposed IBCA and power allocation strategy is verified through simulation and experiments.
Using a certain microgrid demonstration platform, a typical daily unbalanced power data was selected for simulation. In order to verify the advantages of IBCA, convergence speed, robustness, and memory usage are used as evaluation indicators and compared with the other three algorithms. The results show that due to the influence of communication delay, there are fluctuations in the initial iteration of each algorithm, while IBCA can quickly converge to steady state, with a shorter iteration time and faster convergence speed; At the same time, IBCA can quickly recover to steady state after being disturbed, and still has high convergence accuracy, indicating that IBCA has high robustness; Furthermore, by analyzing the amount of data stored in the iterative process system, it can be concluded that IBCA has a lower memory usage. In order to verify the effectiveness of the control strategy proposed in this article, the grouping power allocation effect, reducing lifespan loss effect, battery units SOC recovery and balance control effect, and power tracking effect are used as evaluation indicators, and compared with the other three schemes. The results show that the power allocation model based on competition and cooperation mechanism effectively ensures the response advantage of the power response subject group, reduces the power shock caused by units state switching, reduces BESS lifespan loss, promotes SOC recovery, and effectively improves SOC balance. In addition, the BESS grouping model based on event driven mechanism effectively achieves the reordering and grouping of battery units triggering events. The final power tracking effect diagram shows that according to the strategy proposed in this article, BESS can achieve fast and accurate tracking of power instructions, provide fast compensation for unbalanced power in microgrids, and thus improve the stability of microgrid operation.
To further verify the effectiveness of the power allocation strategy proposed in this article, a BESS hardware experimental platform was built. The experimental results show that the proposed strategy effectively reduces the lifespan loss of BESS, with an expected service life of up to 12.47 years; At the same time, proving the effectiveness of the strategy proposed in this article for battery unit grouping and SOC balance; In addition, the power tracking results indicate that BESS can achieve fast and accurate tracking of power instructions, and the experimental results are consistent with the simulation results.

Key words： Microgrid battery energy storage event driven mechanism competition- cooperation mechanism bipartite consistency algorithm

Received: 18 April 2024

PACS:

TM91

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yu Yang
	Li Menglu
	Wang Boxiao
	Xiang Xiaoping
	Liu Weiliang

Cite this article:

Yu Yang,Li Menglu,Wang Boxiao等. Low Loss Balanced Power Allocation Strategy of BESS Based on Competitive Cooperation Mechanism and IBCA[J]. Transactions of China Electrotechnical Society, 0, (): 240593-.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240593 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/240593

[1] 张祥宇, 舒一楠, 付媛. 基于虚拟储能的直流微电网源荷储多时间尺度能量优化与分区协调控制[J]. 电工技术学报, 2022, 37(23): 6011-6024.
Zhang Xiangyu, Shu Yinan, Fu Yuan.Multi-time-scale energy optimization and zone coordinated control of DC microgrid source-load-storage based on virtual energy storage[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 6011-6024.
[2] 王力, 胡佳成, 曾祥君, 等. 基于混合储能的交直流混联微电网功率分级协调控制策略[J]. 电工技术学报, 2024, 39(8): 2311-2324.
Wang Li, Hu Jiacheng, Zeng Xiangjun, et al.Hierarchical coordinated power control strategy for AC-DC hybrid microgrid with hybrid energy storage[J]. Transactions of China Electrotechnical Society, 2024, 39(8): 2311-2324.
[3] 杨晓辉, 袁志鑫, 肖锦扬, 等. 考虑电池寿命的混合储能微电网优化配置[J]. 电力系统保护与控制, 2023, 51(4): 22-31.
Yang Xiaohui, Yuan Zhixin, Xiao Jinyang, et al.Optimal configuration of hybrid energy storage microgrid considering battery life[J]. Power System Protection and Control, 2023, 51(4): 22-31.
[4] 付华, 陆鹏, 张俊男. 基于A-SA-WOA算法的直流微电网全钒液流电池储能系统功率分配策略[J]. 电工技术学报, 2023, 38(7): 1826-1837.
Fu Hua, Lu Peng, Zhang Junnan.Power allocation strategy of DC microgrid all vanadium redox flow battery energy storage system based on A-SA-WOA algorithm[J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1826-1837.
[5] 严干贵, 蔡长兴, 段双明, 等. 考虑电池储能单元分组优化的微电网运行控制策略[J]. 电力系统自动化, 2020, 44(23): 38-46.
Yan Gangui, Cai Changxing, Duan Shuangming, et al.Operation control strategy of microgrid considering grouping optimization of battery energy storage units[J]. Automation of Electric Power Systems, 2020, 44(23): 38-46.
[6] Lin Li, Jia Yuanqi, Ma Minghui, et al.Long-term stable operation control method of dual-battery energy storage system for smoothing wind power fluctuations[J]. International Journal of Electrical Power & Energy Systems, 2021, 129: 106878.
[7] 余洋, 王卜潇, 吴玉威, 等. 面向光伏平抑考虑SOH与SOC的电池储能系统功率分配方法[J]. 太阳能学报, 2024, 45(3): 377-388.
Yu Yang, Wang Buxiao, Wu Yuwei, et al.Power distribution method of battery energy storage system for photovoltaic power suppression considering SOH and SOC[J]. Acta Energiae Solaris Sinica, 2024, 45(3): 377-388.
[8] 段双明, 于航, 刘聪, 等. 考虑储能单元健康状态与荷电状态一致性的BESS功率分配策略[J]. 电力系统自动化, 2023, 47(5): 65-73.
Duan Shuangming, Yu Hang, Liu Cong, et al.Power allocation strategy for battery energy storage system considering consistency of state of health and state of charge of energy storage units[J]. Automation of Electric Power Systems, 2023, 47(5): 65-73.
[9] Long Jiang, Guo Yangming, Liu Zun, et al.Fully distributed adaptive consensus tracking of uncertain nonlinear multi-agent systems: an augmented system approach[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, 70(6): 2151-2155.
[10] 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.
[11] Lai Xin, Chen Quanwei, Tang Xiaopeng, et al.Critical review of life cycle assessment of lithium-ion batteries for electric vehicles: a lifespan perspective[J]. eTransportation, 2022, 12: 100169.
[12] 蔡新雷, 董锴, 孟子杰, 等. 基于优化动态分组技术的电池储能电站跟踪AGC指令控制策略[J]. 储能科学与技术, 2022, 11(5): 1475-1481.
Cai Xinlei, Dong Kai, Meng Zijie, et al.AGC command tracking control strategy for battery energy storage power station based on optimized dynamic grouping technology[J]. Energy Storage Science and Technology, 2022, 11(5): 1475-1481.
[13] 郭伟. 混合储能阵列系统协调控制策略研究[D]. 北京: 华北电力大学, 2020.
Guo Wei.Research on coordinated control strategy of hybrid energy storage array system[D]. Beijing: North China Electric Power University, 2020.
[14] 余洋, 吴玉威, 陈东阳, 等. 面向风电波动平抑基于改进C-DCA的电池储能分组控制策略[J]. 高电压技术, 2023, 49(10): 4096-4108.
Yu Yang, Wu Yuwei, Chen Dongyang, et al.Battery energy storage grouping control strategy based on improved C-DCA for wind power fluctuation suppression[J]. High Voltage Engineering, 2023, 49(10): 4096-4108.
[15] 杨珺, 侯俊浩, 刘亚威, 等. 分布式协同控制方法及在电力系统中的应用综述[J]. 电工技术学报, 2021, 36(19): 4035-4049.
Yang Jun, Hou Junhao, Liu Yawei, et al.Distributed cooperative control method and application in power system[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4035-4049.
[16] 徐成司, 董树锋, 华一波, 等. 基于改进一致性算法的工业园区分布式综合需求响应[J]. 电工技术学报, 2022, 37(20): 5175-5187.
Xu Chengsi, Dong Shufeng, Hua Yibo, et al.Distributed integrated demand response of industrial park based on improved consensus algorithm[J]. Transactions of China Electrotechnical Society, 2022, 37(20): 5175-5187.
[17] 张圣祺, 刘何毓, 汪飞, 等. 面向电网二次调频需求的 “PXP” 储能集群分布式均衡控制策略[J]. 中国电机工程学报, 2022, 42(3): 886-898.
Zhang Shengqi, Liu Heyu, Wang Fei, et al.A balancing control strategy for “power-X-power” energy storage cluster in system load frequency control[J]. Proceedings of the CSEE, 2022, 42(3): 886-898.
[18] Gao Yang, Ai Qian, Yousif M, et al.Source-load-storage consistency collaborative optimization control of flexible DC distribution network considering multi-energy complementarity[J]. International Journal of Electrical Power & Energy Systems, 2019, 107: 273-281.
[19] 龙本锦, 张靖, 何宇, 等. 基于DMPC和储能单元约束的分组一致性控制策略[J]. 电力系统保护与控制, 2022, 50(24): 23-36.
Long Benjin, Zhang Jing, He Yu, et al.Grouping consistency control strategy based on DMPC and energy storage unit constraints[J]. Power System Protection and Control, 2022, 50(24): 23-36.
[20] 纪良浩, 廖晓峰, 刘群. 时延多智能体系统分组一致性分析[J]. 物理学报, 2012, 61(22): 5-12.
Ji Lianghao, Liao Xiaofeng, Liu Qun.Group consensus analysis of multi-agent systems with delays[J]. Acta Physica Sinica, 2012, 61(22): 5-12.
[21] 杨瑞田. 基于不连续控制策略的多智能体系统二分一致性研究[D]. 无锡: 江南大学, 2021.
[22] 邵海滨, 潘鹿鹿, 席裕庚, 等. 符号网络下多智能体系统二分一致性的牵制控制问题[J]. 控制与决策, 2019, 34(8): 1695-1701.
Shao Haibin, Pan Lulu, Xi Yugeng, et al.Leader-following bipartite consensus of multi-agent systems under signed networks[J]. Control and Decision, 2019, 34(8): 1695-1701.
[23] 郭伟, 赵洪山. 基于改进分布式一致性算法的电池储能阵列分组控制策略[J]. 电工技术学报, 2019, 34(23): 4991-5000.
Guo Wei, Zhao Hongshan.Grouping control strategy of battery energy storage array system based on an improved distributed consensus algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(23): 4991-5000.
[24] Huang Bonan, Zheng Shun, Wang Rui, et al.Distributed optimal control of DC microgrid considering balance of charge state[J]. IEEE Transactions on Energy Conversion, 2022, 37(3): 2162-2174.
[25] 郭伟, 赵洪山. 基于DMPC加权一致性算法的电池储能阵列分组控制策略[J]. 电力自动化设备, 2020, 40(1): 133-140.
Guo Wei, Zhao Hongshan.Grouping control strategy of battery energy storage array based on DMPC weighted consensus algorithm[J]. Electric Power Automation Equipment, 2020, 40(1): 133-140.
[26] Xiong Rui, Lv Liang, Mu Hao.A novel grouping method for lithium-ion battery pack considering cell divergence[C]//2017 Chinese Automation Congress (CAC), Jinan, China, 2017: 5269-5273.
[27] Su Jialei, Li Kang, Li Yongfei, et al.A novel state-of-charge-based droop control for battery energy storage systems to support coordinated operation of DC microgrids[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(1): 312-324.
[28] Maharjan S, Zhu Quanyan, Zhang Yan, et al.Dependable demand response management in the smart grid: a stackelberg game approach[J]. IEEE Transactions on Smart Grid, 2013, 4(1): 120-132.
[29] 邸鹏宇, 蔡新雷, 孟子杰, 等. 基于考虑通信延迟改进分布一致性算法的多储能电站快速低寿命损耗功率均衡分配策略[J/OL]. 电力自动化设备, 1-13[2024-04-15].https://doi.org/10.16081/j.epae.202312027
Di Pengyu, Cai Xinlei, Meng Zijie, et al.Fast and balanced power allocation strategy of battery energy storage stations based on IDCA with communication delay considering low life loss[J/OL]. Electric Power Automation Equipment, 1-13[2024-04-15].https://doi.org/10.16081/j.epae.202312027.
[30] Yu Yang, Chen Dongyang, Wang Boxiao, et al.Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction[J]. IET Power Electronics, 2022, 15(15): 1629-1651.
[31] 田桂珍, 卢栋, 刘广忱, 等. 基于零相位低通滤波器的混合储能平抑直驱风电机组功率波动控制策略的研究[J]. 太阳能学报, 2021, 42(6): 72-78.
Tian Guizhen, Lu Dong, Liu Guangchen, et al.Research on control strategy of suppressing power fluctuation of direct-drive wind power system with hybrid energy storage based on zero-phase low-pass filter[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 72-78.
[32] Yang Xiaoyong, Wang Shunli, Takyi-Aninakwa P, et al.Improved noise bias compensation-equivalent circuit modeling strategy for battery state of charge estimation adaptive to strong electromagnetic interference[J]. Journal of Energy Storage, 2023, 73: 108974.