基于MOIBKA算法的电化学储能电站最优功率分配

doi:10.3969/j.issn.2097-0706.2025.06.008

摘要/Abstract

摘要：

针对多种因素影响下电化学储能电站的功率分配问题，提出一种基于改进多目标黑翅鸢优化算法（MOIBKA）的电化学储能电站最优功率分配策略。建立电化学储能电站的拓扑结构，并提出了电站运行的3个评价指标。在储能电站传统功率分配模型的基础上建立包含储能电站总运行成本最低、储能单元健康状态损失最小、荷电状态（SOC）一致性最好的多目标功率分配模型，并通过多策略MOIBKA进行求解。通过对比仿真分析以及运行评价指标证明了所提策略可以有效减少储能单元充放电次数，降低储能单元的健康状态损失以及提高储能单元SOC一致性，实现了储能电站的最优功率分配。

关键词: 新型电力系统, 电化学储能电站, 功率分配, 多目标优化, 黑翅鸢算法, 评价指标, 荷电状态, 健康状态损失

Abstract:

To address the power allocation issue of electrochemical energy storage stations under the influence of multiple factors，an optimal power allocation strategy for electrochemical energy storage power stations based on the multi-objective improved black-winged kite algorithm （MOIBKA） is proposed. The topology of the electrochemical energy storage power station is established，and three evaluation indicators for the operation of the power station are proposed. Based on the traditional power allocation model for energy storage stations，a multi-objective power allocation model is established，aiming to achieve the lowest total operating cost of energy storage power stations，the smallest state of health loss of energy storage units，and the best state of charge （SOC） consistency. The model is solved using the MOIBKA through multiple strategies. Comparative simulation analysis and operational evaluation indicators prove that the proposed strategy could effectively reduce the number of charging and discharging cycles and the state of health loss of energy storage units while improving SOC consistency. This achieves optimal power allocation for energy storage power stations.

Key words: new power system, electrochemical energy storage power station, power allocation, multi-objective optimization, black-winged kite algorithm, evaluation indicator, state of charge, state of health loss

中图分类号:

TM762

王成, 邵冲, 何欣, 董海鹰. 基于MOIBKA算法的电化学储能电站最优功率分配[J]. 综合智慧能源, 2025, 47(6): 74-84.

WANG Cheng, SHAO Chong, HE Xin, DONG Haiying. Optimal power allocation for electrochemical energy storage power stations based on MOIBKA algorithm[J]. Integrated Intelligent Energy, 2025, 47(6): 74-84.

图/表 17

图1

图2

表1

图3

图4

图5

表2

图6

图7

图8

图9

图10

图11

图12

图13

图14

表3

参考文献 28

[1]	YANG Z, XIA L, GUAN X H. Fluctuation reduction of wind power and sizing of battery energy storage systems in microgrids[J]. IEEE Transactions on Automation Science and Engineering, 2020, 17(3): 1195-1207.
[2]	PONTES L R, ESPINOZA H R, MOLINA Y P, et al. Optimal allocation of energy storage system in distribution systems with intermittent renewable energy[J]. IEEE Latin America Transactions, 2021, 19(2): 288-296.
[3]	朱晓荣, 李铮, 孟凡奇. 基于不同网架结构的直流微电网稳定性分析[J]. 电工技术学报, 2021, 36(1): 166-178.
	ZHU Xiaorong, LI Zheng, MENG Fanqi. Stability analysis of DC microgrid based on different grid structures[J]. Transactions of China Electrotechnical Society, 2021, 36(1): 166-178.
[4]	薛福, 马晓明, 游焰军. 储能技术类型及其应用发展综述[J]. 综合智慧能源, 2023, 45(9): 48-58.
	XUE Fu, MA Xiaoming, YOU Yanjun. Energy storage technologies and their applications and development[J]. Integrated Intelligent Energy, 2023, 45(9): 48-58.
[5]	刘聪, 赵臣臣. 新能源发展及混合储能技术的研究与应用[J]. 中国高新科技, 2021(24): 131-132.
	LIU Cong, ZHAO Chenchen. Research and application of hybrid energy storage technology with development of new energy[J]. China High and New Technology, 2021(24): 131-132.
[6]	郑琼, 江丽霞, 徐玉杰, 等. 碳达峰、碳中和背景下储能技术研究进展与发展建议[J]. 中国科学院院刊, 2022, 37(4): 529-540.
	ZHENG Qiong, JIANG Lixia, XU Yujie, et al. Research progress and development suggestions of energy storage technology under background of carbon peak and carbon neutrality[J]. Bulletin of Chinese Academy of Sciences, 2022, 37(4): 529-540.
[7]	杨子龙, 宋振浩, 潘静, 等. 分布式光伏/储能系统多运行模式协调控制策略[J]. 中国电机工程学报, 2019, 39(8): 2213-2220.
	YANG Zilong, SONG Zhenhao, PAN Jing, et al. Multi-mode coordinated control strategy of distributed PV and energy storage system[J]. Proceedings of the CSEE, 2019, 39(8): 2213-2220.
[8]	刘忠, 杨陈, 蒋玮, 等. 基于一致性算法的直流微电网储能系统功率分配技术[J]. 电力系统自动化, 2020, 44(7): 61-69.
	LIU Zhong, YANG Chen, JIANG Wei, et al. Consensus algorithm based power distribution technology for energy storage system in DC microgrid[J]. Automation of Electric Power Systems, 2020, 44(7): 61-69.
[9]	马彦宏, 吕清泉, 张珍珍, 等. 考虑储能系统SOC的双卡尔曼滤波风电功率波动平抑策略[J]. 综合智慧能源, 2023, 45(2): 61-68.
	MA Yanhong, LYU Qingquan, ZHANG Zhenzhen, et al. Wind power fluctuation mitigation strategy based on double Kalman filter considering the SOC of the energy storage system[J]. Integrated Intelligent Energy, 2023, 45(2): 61-68.
[10]	胡卫丰, 侍红兵, 李官军, 等. 基于初始工作点选取的级联多电平混合储能系统功率分配控制[J]. 电网技术, 2020, 44(5): 1639-1651.
	HU Weifeng, SHI Hongbing, LI Guanjun, et al. Power distribution control for cascaded multilevel inverter with hybrid energy sources based on initial operation point selection[J]. Power System Technology, 2020, 44(5): 1639-1651.
[11]	何俊强, 师长立, 马明, 等. 基于元模型优化算法的混合储能系统双层优化配置方法[J]. 电力自动化设备, 2020, 40(7): 157-167.
	HE Junqiang, SHI Changli, MA Ming, et al. Bi-level optimal configuration method of hybrid energy storage system based on meta model optimization algorithm[J]. Electric Power Automation Equipment, 2020, 40(7): 157-167.
[12]	刘颖明, 王瑛玮, 王晓东, 等. 基于蚁狮算法的风电集群储能容量配置优化方法[J]. 太阳能学报, 2021, 42(1): 431-437.
	LIU Yingming, WANG Yingwei, WANG Xiaodong, et al. Optimization of storage capacity allocation in wind farm cluster based on ant lion optimization algorithm[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 431-437.
[13]	朱永强, 王甜婧, 许阔, 等. 基于动态规划-遗传算法的混合储能系统实时协调调度和经济运行[J]. 太阳能学报, 2019, 40(4): 1059-1066.
	ZHU Yongqiang, WANG Tianjing, XU Kuo, et al. Real-time coordinated dispatching and economic operation of hybrid energy storage based on dynamic programming-genetic algorithm[J]. Acta Energiae Solaris Sinica, 2019, 40(4): 1059-1066.
[14]	MENG T Y, LIN Z L, WAN Y, et al. State-of-charge balancing for battery energy storage systems in DC microgrids by distributed adaptive power distribution[J]. IEEE Control Systems Letters, 2021, 6: 512-517.
[15]	赵燚, 史学伟, 王莉斌, 等. 一种规模化电池储能电站功率分配策略[J]. 电网技术, 2022, 46(12): 5004-5012.
	ZHAO Yi, SHI Xuewei, WANG Libin, et al. Power allocation strategy of large-scaled battery energy storage power station[J]. Power System Technology, 2022, 46(12): 5004-5012.
[16]	MA Z, GAO F, GU X, et al. Multilayer SOH equalization scheme for MMC battery energy storage system[J]. IEEE Transactions on Power Electronics, 2020, 35(12): 13514-13527.
[17]	WANG B, WANG C H, MA G L, et al. Power-split strategy based on average power method for semi-active hybrid energy storage system in small electric vehicles[J]. Energy Procedia, 2019, 158: 2994-2999.
[18]	李菁, 窦真兰, 王加祥, 等. 基于RSOC的风光氢能源系统功率分配策略研究[J]. 综合智慧能源, 2023, 45(7): 78-86.
	LI Jing, DOU Zhenlan, WANG Jiaxiang, et al. Research on power distribution strategy of an RSOC-based wind-photovoltaic-hydrogen energy system[J]. Integrated Intelligent Energy, 2023, 45(7): 78-86.
[19]	严干贵, 刘莹, 段双明, 等. 电池储能单元群参与电力系统二次调频的功率分配策略[J]. 电力系统自动化, 2020, 44(14): 26-34.
	YAN Gangui, LIU Ying, DUAN Shuangming, et al. Power distribution strategy for battery energy storage unit group participating in secondary frequency regulation of power system[J]. Automation of Electric Power Systems, 2020, 44(14): 26-34.
[20]	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.
[21]	谭树成, 张辉, 肖曦, 等. 基于SOC不平衡度的储能装置功率分配方法[J]. 电力电子技术, 2016, 50(11): 57-59.
	TAN Shucheng, ZHANG Hui, XIAO Xi, et al. A power sharing method based on SOC unbalanced degree for energy storage equipment[J]. Power Electronics, 2016, 50(11): 57-59.
[22]	刘根才, 陆志欣, 杨智诚, 等. 考虑SOC均衡的分布式储能聚合控制方法[J]. 电力电容器与无功补偿, 2020, 41(3): 174-181.
	LIU Gencai, LU Zhixin, YANG Zhicheng, et al. Distributed energy storage aggregation control method considering SOC equalization[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(3): 174-181.
[23]	国家市场监督管理总局, 国家标准化管理委员会. 电化学储能电站运行指标及评价: GB/T 36549—2018[S]. 北京: 中国标准出版社, 2018.
[24]	WANG J, WANG W C, HU X X, et al. Black-winged kite algorithm: A nature-inspired meta-heuristic for solving benchmark functions and engineering problems[J]. Artificial Intelligence Review, 2024, 57(4): 98.
[25]	李书群, 陈钰, 杨雨婷, 等. 基于佳点集人工鱼群的点云配准算法[J]. 合肥工业大学学报(自然科学版), 2023, 46(9): 1203-1209.
	LI Shuqun, CHEN Yu, YANG Yuting, et al. Point cloud registration method based on artificial fish swarm algorithm using good point set[J]. Journal of Hefei University of Technology (Natural Science), 2023, 46(9): 1203-1209.
[26]	杨小健, 董毅伟. 基于反向学习的自适应快速人工蜂群算法[J]. 系统仿真学报, 2016, 28(11): 2684-2691, 2700.
	YANG Xiaojian, DONG Yiwei. Adaptive quick artificial bee colony algorithm based on opposition learning[J]. Journal of System Simulation, 2016, 28(11): 2684-2691, 2700.
[27]	欧阳城添, 刘裕嘉, 朱东林. 融合精英学习与多项式变异的改进麻雀算法[J]. 计算机仿真, 2023, 40(5): 398-403, 547.
	OUYANG Chengtian, LIU Yujia, ZHU Donglin. Improved sparrow algorithm combining elite learning and polynomial mutation[J]. Computer Simulation, 2023, 40(5): 398-403, 547.
[28]	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.

参数	^#1	^#2	^#3	^#4	^#5	^#6
SOC	0.15	0.20	0.25	0.30	0.35	0.40
额定功率/kW	100	100	100	100	100	100
额定容量/（kW·h）	600	600	600	600	600	600

评价指标	MOEA/D	MOPSO	NSGA-Ⅱ	MOBKA	MOIBKA
HV	0.535 7	0.637 1	0.848 2	0.500 0	0.850 6
IGD	0.412 1	0.245 6	0.053 7	0.500 0	0.023 2

储能单元	充放电切换次数		充放电平衡度		利用系数
储能单元	传统策略	MOIBKA 策略	传统策略	MOIBKA 策略	传统策略	MOIBKA 策略
1	19	11	0.318 5	0.301 1	1	0.683 3
2	19	10	0.312 9	0.298 5	1	0.716 7
3	19	6	0.278 9	0.262 2	1	0.583 3
4	19	5	0.274 0	0.259 6	1	0.633 3
5	19	11	0.261 8	0.240 8	1	0.783 3
6	19	8	0.236 1	0.222 6	1	0.700 0