时滞环境下基于改进分组模型的HBESS固定时间比例功率控制

doi:10.19595/j.cnki.1000-6753.tces.241828

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

Download: PDF (1566 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

With the increasing integration of renewable energy sources and stochastic loads, the existing battery energy storage systems (BESS) in microgrids often fail to meet power balance requirements, necessitating further expansion. The expanded energy storage system comprises multiple BESS units with varying electrical parameters and operational states, forming a heterogeneous BESS (HBESS). Similar to BESS, HBESS faces challenges when mitigating power imbalance in microgrids, including high lifespan degradation, low balance among battery unit (BU), and insufficient control precision.
To address these issues, this paper proposes a fixed-time proportional power control strategy for HBESS based on an improved grouping model in time-delay environments. First, this paper evaluate the regulation capability of BU by considering their electrical parameters and operational states, and construct an improved grouping model based on a competition-cooperation mechanism to divide the HBESS into multiple battery groups. Second, a hierarchical regulation method is developed using the improved grouping model to determine participating battery groups and BUs. This method reduces lifespan degradation by allocating power hierarchically to battery groups with higher regulation capabilities. Subsequently, this paper propose time delay based fixed-time proportional consensus algorithm (TFPCA), analyze its mathematical properties, and investigate methods to enhance its iterative precision. Finally, by integrating the hierarchical regulation method with TFPCA while considering BU operational state balance, this paper design a precise hierarchical power control strategy for battery groups, enabling each BU to accurately track power commands.
This paper employs the typical daily unbalanced power of a microgrid for simulation verification. To demonstrate the advantages of the proposed TFPCA, grouping method, and power control strategy, three different algorithms, three grouping methods, and three power control strategies were selected for comparative analysis. The algorithm comparison results show that in time-delay environments, while all algorithms exhibit oscillatory iteration curves, only TFPCA achieves both fixed-time consensus and proportional consensus while eliminating iteration errors. These characteristics make it particularly suitable for precise power control of HBESS in time-delay environments. The comparative results of grouping methods demonstrate that, throughout the entire regulation cycle, only the proposed grouping method can maintain hierarchical distributions of backup capacity and rated power across all battery groups, which facilitates the implementation of hierarchical regulation. Moreover, the proposed method dynamically adjusts the number of battery groups based on energy storage parameters and command characteristics, ultimately reducing the lifespan degradation of HBESS. Regarding power control strategies, the comparative results indicate that, compared with other power control strategies, the proposed strategy not only promotes state of charge balance both within individual BESS and across the entire HBESS system, but also ensures battery groups participate in regulation according to predetermined hierarchies with superior control accuracy. This effectively addresses the key challenges of high lifespan degradation, poor BU balance, and insufficient control precision.
This paper further constructed an HBESS hardware experimental platform for testing. The experimental results show that all battery groups maintain hierarchical distributions in terms of backup capacity and rated power, with significantly improved BU balance, achieving precise HBESS power control. The experimental results are consistent with the simulation results.

Key words： Heterogeneous battery energy storage system power control grouping model fixed-time proportional consensus algorithm time-delay

Received: 18 October 2024

PACS:

TM91

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.241828 OR https://dgjsxb.ces-transaction.com/EN/Y0/V/I/20241828

[1] 王浩, 仵哲, 康博阳, 等. 考虑电动汽车和蓄电池联合储能的交直流混合微电网功率协调控制策略[J]. 电工技术学报, 2024, 39(19): 6085-6103.
Wang Hao, Wu Zhe, Kang Boyang, et al.Power coordinated control strategy for AC/DC hybrid microgrid considering combined energy storage of electric vehicles and batteries[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 6085-6103.
[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] 谷晴, 李睿, 蔡旭, 等. 面向百兆瓦级应用的电池储能系统拓扑与控制方法[J]. 发电技术, 2022, 43(5): 698-706.
Gu Qing, Li Rui, Cai Xu, et al.Topology and control method of battery energy storage system for application at the scale of hundreds of megawatts[J]. Power Generation Technology, 2022, 43(5): 698-706.
[4] Qiu Xin, Nguyen T A, Crow M L.Heterogeneous energy storage optimization for microgrids[J]. IEEE Transactions on Smart Grid, 2016, 7(3): 1453-1461.
[5] Huang Chongxin, Yang Manting, Ge Hui, et al.DMPC-based load frequency control of multi-area power systems with heterogeneous energy storage system considering SoC consensus[J]. Electric Power Systems Research, 2024, 228: 110064.
[6] 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.
[7] 郭向伟, 王晨, 钱伟, 等. 电池储能系统均衡方法研究综述[J]. 电工技术学报, 2024, 39(13): 4204-4225.
Guo Xiangwei, Wang Chen, Qian Wei, et al.A review of equalization methods for battery energy storage system[J]. Transactions of China Electrotechnical Society, 2024, 39(13): 4204-4225.
[8] Mühlbauer M, Rang Fabian, Palm H, et al.Pareto-optimal power flow control in heterogeneous battery energy storage systems[J]. Journal of Energy Storage, 2022, 48: 103803.
[9] 李相俊, 马锐, 王上行, 等. 考虑电池寿命的商业园区储能电站运行控制策略[J]. 高电压技术, 2020, 46(1): 62-70.
Li Xiangjun, Ma Rui, Wang Shangxing, et al.Operation control strategy for energy storage station after considering battery life in commercial park[J]. High Voltage Engineering, 2020, 46(1): 62-70.
[10] 余洋, 陈东阳, 吴玉威, 等. 风电跟踪调度计划时降低寿命损耗的电池储能分组控制策略[J]. 电力自动化设备, 2023, 43(3): 54-62.
Yu Yang, Chen Dongyang, Wu Yuwei, et al.Grouping control strategy of battery energy storage for reducing life loss under wind power tracking scheduling plan[J]. Electric Power Automation Equipment, 2023, 43(3): 54-62.
[11] 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.
[12] Li Xiangjun, Ma Rui, Yan Ning, et al.Research on optimal scheduling method of hybrid energy storage system considering health state of echelon-use lithium-ion battery[J]. IEEE Transactions on Applied Superconductivity, 2021, 31(8): 0604204.
[13] 吴青峰, 杨凯义, 于少娟, 等. 基于无通讯的微电网储能系统主动SOH协同控制方案[J]. 太阳能学报, 2023, 44(5): 40-47.
Wu Qingfeng, Yang Kaiyi, Yu Shaojuan, et al.Active soh cooperative control scheme of microgrid energy storage systems based on no-communication[J]. Acta Energiae Solaris Sinica, 2023, 44(5): 40-47.
[14] Lin Xinyou, Xi Longliang, Wang Zhaorui.Battery degradation-aware energy management strategy with driving pattern severity factor feedback correction algorithm[J]. Journal of Cleaner Production, 2024, 450: 141969.
[15] Li Xining, Lyu Lixing, Geng Guangchao, et al.Power allocation strategy for battery energy storage system based on cluster switching[J]. IEEE Transactions on Industrial Electronics, 2022, 69(4): 3700-3710.
[16] 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.
[17] 郭光朝, 李相俊, 张亮, 等. 单体电压不一致性对锂电池储能系统容量衰减的影响[J]. 电力建设, 2016, 37(11): 23-28.
Guo Guangchao, Li Xiangjun, Zhang Liang, et al.Impact of cell voltage inconsistency on capacity attenuation of lithium battery energy storage system[J]. Electric Power Construction, 2016, 37(11): 23-28.
[18] 肖家杰, 李培强, 毛志宇, 等. 考虑火电时滞特性的电池储能集群调频综合控制策略研究[J]. 电工技术学报, 2025, 40(3): 689-704.
Xiao Jiajie, Li Peiqiang, Mao Zhiyu, et al.Research on integrated control strategy of battery energy storage cluster for frequency regulation considering thermal power time lag characteristic[J]. Transactions of China Electrotechnical Society, 2025, 40(3): 689-704.
[19] Yu Yang, Wang Boxiao, Li Menglu, et al. Distributed balanced grouping power control for battery energy storage systems to mitigate adjustable capacity discrepancy[J]. IEEE Transactions on Energy Conversion, 2025, PP(99): 1-13.
[20] 鄢仁武, 姜雪儿. 变调节因子的不同容量锂电池储能系统能量控制策略[J]. 电气技术, 2024, 25(2): 21-30.
Yan Renwu, Jiang Xueer.Energy control strategies for lithium battery energy storage systems with different capacities based on variable regulating factors[J]. Electrical Engineering, 2024, 25(2): 21-30.
[21] 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]. 电力自动化设备, 2024, 44(8): 18-26.
Di Pengyu, Cai Xinlei, Meng Zijie, et al.Fast and low life loss power equalization allocation strategy of multiple battery energy storage stations based on improved distributed consistency algorithm considering communication delay[J]. Electric Power Automation Equipment, 2024, 44(8): 18-26.
[22] Olfati-Saber R, Fax J A, Murray R M.Consensus and cooperation in networked multi-agent systems[J]. Proceedings of the IEEE, 2007, 95(1): 215-233.
[23] Zeng Yuji, Zhang Qinjin, Liu Yancheng, et al.Hierarchical cooperative control strategy for battery storage system in islanded DC microgrid[J]. IEEE Transactions on Power Systems, 2022, 37(5): 4028-4039.
[24] Ni Junkang, Shi Peng.Adaptive neural network fixed-time leader-follower consensus for multiagent systems with constraints and disturbances[J]. IEEE Transactions on Cybernetics, 2021, 51(4): 1835-1848.
[25] Ni Junkang, Liu Ling, Liu Chongxin, et al.Fixed-time leader-following consensus for second-order multiagent systems with input delay[J]. IEEE Transactions on Industrial Electronics, 2017, 64(11): 8635-8646.
[26] Meng Deyuan, Jia Yingmin.Robust consensus algorithms for multiscale coordination control of multivehicle systems with disturbances[J]. IEEE Transactions on Industrial Electronics, 2016, 63(2): 1107-1119.
[27] 陈世明, 邵赛, 姜根兰. 基于事件触发二阶多智能体系统的固定时间比例一致性[J]. 自动化学报, 2022, 48(1): 261-270.
Chen Shiming, Shao Sai, Jiang Genlan.Distributed event-triggered fixed-time scaled consensus control for second-order multi-agent systems[J]. Acta Automatica Sinica, 2022, 48(1): 261-270.
[28] Liu Jian, Ran Guangtao, Wu Yongbao, et al.Dynamic event-triggered practical fixed-time consensus for nonlinear multiagent systems[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022, 69(4): 2156-2160.
[29] Orlov Y, Kairuz R I V. Autonomous output feedback stabilization with prescribed settling-time bound[J]. IEEE Transactions on Automatic Control, 2023, 68(4): 2452-2459.
[30] 刘萍, 李泽文, 蔡雨思, 等. 基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法[J]. 电工技术学报, 2024, 39(10): 3232-3243.
Liu Ping, Li Zewen, Cai Yusi, et al.Joint estimation method of SOC and SOH based on fusion of equivalent circuit model and data-driven model[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3232-3243.
[31] 余洋, 李梦璐, 王卜潇, 等. 基于竞争合作机制的电池储能系统分布式功率分配策略[J]. 电工技术学报, 2025, 40(7): 2335-2352.
Yu Yang, Li Menglu, Wang Boxiao, et al.A distributed power allocation strategy for battery energy storage systems based on competitive-cooperative mechanism[J]. Transactions of China Electrotechnical Society, 2025, 40(7): 2335-2352.
[32] Li Jianwei, Yang Luming, Yang Qingqing, et al.Degradation adaptive energy management with a recognition-prediction method and lifetime competition-cooperation control for fuel cell hybrid bus[J]. Energy Conversion and Management, 2022, 271: 116306.
[33] Lin Junjie, Gao Chong, Zeng Jianfeng, et al.Stackelberg-Nash asymmetric bargaining-based scheduling optimization and revenue-allocation for multi-operator regional integrated energy system considering competition-cooperation relationship and source-load uncertainties[J]. Energy, 2024, 291: 130262.
[34] Zuo Zongyu, Han Qinglong, Ning Boda.An explicit estimate for the upper bound of the settling time in fixed-time leader-following consensus of high-order multivariable multiagent systems[J]. IEEE Transactions on Industrial Electronics, 2019, 66(8): 6250-6259.
[35] Deng Qun, Wu Jie, Han Tao, et al.Fixed-time bipartite consensus of multi-agent systems with disturbances[J]. Physica A: Statistical Mechanics and Its Applications, 2019, 516: 37-49.
[36] Olfati-Saber R, Murray R M.Consensus problems in networks of agents with switching topology and time-delays[J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533.
[37] 中国电力企业联合会. 微电网并网调度运行规范: T/CEC 182—2018[S]. 北京:中国电力出版社,2022.
[38] Shi Mengxuan, Chen Xia, Zhou Jianyu, et al.Distributed optimal control of energy storages in a DC microgrid with communication delay[J]. IEEE Transactions on Smart Grid, 2020, 11(3): 2033-2042.