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

doi:10.19595/j.cnki.1000-6753.tces.211855

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (2784 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The all-vanadium redox flow energy storage system can smooth the power fluctuations due to photovoltaic and wind power generation in the DC microgrid. The problem of reasonable power distribution in the system can be solved by an effective control strategy, to improve the operation efficiency of the energy storage system. The total cost of the energy storage battery unit determines the economic applicability of the battery, the loss rate of the energy storage battery can indirectly reflect the working efficiency of the energy storage battery unit, and the SOC consistency can reflect the effectiveness of the power distribution strategy. In this regard, we proposed a power distribution strategy for all vanadium flow battery energy storage system based on the whale optimization algorithm of adaptive weight adjustment and simulated annealing strategy. Firstly, under the constraints of charge state and power balance, the optimization objective function with the total cost of the energy storage system, the lowest average loss rate, and the best charge balance degree is established. Secondly, the adaptive adjustment of the weights-based whale optimization algorithm is used to allocate the optimal power of its energy storage unit. To find a better solution in the global optimization, the simulated annealing algorithm is introduced into the whale optimization algorithm iteration process; Finally, the simulation is run under scenario 1 and scenario 2 as examples. In Scenario 1, a fixed power of 220kW was used as the total power demand, and the priority level of charge and discharge of the energy storage unit was set. The lower the SOC value is, the higher the priority of the VRB energy storage unit is. The energy storage unit allocates power under different distribution strategies according to the priority level; In Scenario 2, the photovoltaic, wind power, total load and total energy storage power demand data of DC microgrid in a certain period were selected as test examples. The parameters of the optimization objective function are the same as in scenario 1. In order to more intuitively reflect the changes of each energy storage unit index in the scheduling cycle, the power distribution curves of VRB energy storage units under different distribution strategies, the total battery cost comparison diagram, the loss rate comparison diagram and the optimal combination Gantt chart were respectively drawn. The economic cost of VRB energy storage unit was measured by taking kilowatt hour cost as the target value,the loss rate was calculated by the equivalent circuit model, the charge-discharge balance was defined by the battery SOC value, and the number of battery charge-discharge times under each scheduling period was calculated by the optimal Gantt chart. Based on the analysis of the operating indexes of the electrochemical energy storage battery, the power distribution effect strategy of VRB energy storage system under the condition of meeting the total objective function was proposed. Compared with the traditional strategy, the results showed that the optimal allocation strategy effectively reduces the operation cost and loss rate of energy storage battery cells, the battery charge and discharge times are significantly reduced, and the state of charge consistency is good. Importantly, the allocation strategy converges faster than the target value under the traditional strategy, which verified the accuracy of the proposed optimization method and the applicability of the model.

Key words： A-SA-WOA vanadium redox battery energy storage system power allocation strategy multi objective optimization

Received: 18 November 2021

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Fu Hua
	Lu Peng
	Zhang Junnan

Cite this article:

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.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.211855 OR https://dgjsxb.ces-transaction.com/EN/Y2023/V38/I7/1826

[1] Yang Zhen, Xia Li, Guan Xiaohong.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] Braz Pontes L R, Percy Molina Rodriguez Y, Luyo Kuong J, 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] 郑浩, 谢丽蓉, 叶林, 等. 考虑光伏双评价指标的混合储能平滑出力波动策略[J]. 电工技术学报, 2021, 36(9): 1805-1817.
Zheng Hao, Xie Lirong, Ye Lin, et al.Hybrid energy storage smoothing output fluctuation strategy considering photovoltaic dual evaluation indicators[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1805-1817.
[5] 廉茂航, 任永峰, 韩鹏, 等. 双馈风电系统中VRB储能型网侧九开关变换器[J]. 电工技术学报, 2018, 33(6): 1197-1207.
Lian Maohang, Ren Yongfeng, Han Peng, et al.Grid side nine-switch converter based on VRB energy storage for doubly-fed induction generator wind turbine system[J]. Transactions of China Electrotechnical Society, 2018, 33(6): 1197-1207.
[6] 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.
[7] 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.
[8] 闫林芳, 刘巨, 石梦璇, 等. 基于模糊逻辑算法的直流微电网复合储能系统功率自适应分配策略[J]. 中国电机工程学报, 2019, 39(9): 2658-2670.
Yan Linfang, Liu Ju, Shi Mengxuan, et al.Adaptive power allocation strategy based on fuzzy logic algorithm for hybrid energy storage system in DC microgrid[J]. Proceedings of the CSEE, 2019, 39(9): 2658-2670.
[9] 谢丽蓉, 郑浩, 魏成伟, 等. 兼顾补偿预测误差和平抑波动的光伏混合储能协调控制策略[J]. 电力系统自动化, 2021, 45(3): 130-138.
Xie Lirong, Zheng Hao, Wei Chengwei, et al.Coordinated control strategy of photovoltaic hybrid energy storage considering prediction error compensation and fluctuation suppression[J]. Automation of Electric Power Systems, 2021, 45(3): 130-138.
[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): 1639-1646.
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): 1639-1646.
[11] 李征, 陈佳瑜, 石坤. 风电功率波动频率域分析及储能平滑功率算法优化[J]. 太阳能学报, 2020, 41(4): 184-193.
Li Zheng, Chen Jiayu, Shi Kun.Frequency domain analysis of wind power fluctuation and control strategy optimization of power smoothing[J]. Acta Energiae Solaris Sinica, 2020, 41(4): 184-193.
[12] 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): 157-164.
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): 157-164.
[13] 任凯, 蒋玮, 杨波, 等. 用于平抑间歇性负荷的混合储能系统优化分频定容技术[J]. 电力自动化设备, 2021, 41(7): 81-87.
Ren Kai, Jiang Wei, Yang Bo, et al.Optimal frequency division and capacity determination technology of hybrid energy storage system for suppressing intermittent load[J]. Electric Power Automation Equipment, 2021, 41(7): 81-87.
[14] 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.
[15] 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.
[16] 王玮, 余向阳, 高春阳, 等. 全钒液流电池储能在配电网中优化配置策略[J]. 电网与清洁能源, 2020, 36(5): 83-89.
Wang Wei, Yu Xiangyang, Gao Chunyang, et al.Optimal configuration strategy of vanadium redox flow battery energy storage in distribution networks[J]. Power System and Clean Energy, 2020, 36(5): 83-89.
[17] 任永峰, 胡宏彬, 薛宇, 等. 全钒液流电池-超级电容混合储能平抑直驱式风电功率波动研究[J]. 高电压技术, 2015, 41(7): 2127-2134.
Ren Yongfeng, Hu Hongbin, Xue Yu, et al.Vanadium redox battery-super capacitor hybrid energy storage system for smooth direct-drive wind turbine power fluctuation[J]. High Voltage Engineering, 2015, 41(7): 2127-2134.
[18] Meng Tingyang, Lin Zongli, Wan Yan, et al.State-of-charge balancing for battery energy storage systems in DC microgrids by distributed adaptive power distribution[J]. IEEE Control Systems Letters, 2022, 6: 512-517.
[19] 邵军康, 李鑫, 邱亚, 等. 全钒液流电池多场耦合建模研究[J]. 高电压技术, 2021, 47(5): 1881-1891.
Shao Junkang, Li Xin, Qiu Ya, et al.Multi-field coupling modeling of vanadium redox battery[J]. High Voltage Engineering, 2021, 47(5): 1881-1891.
[20] 谢克桓, 李传常, 陈荐, 等. 全钒液流电池储能仿真模型及荷电状态监测方法研究[J]. 储能科学与技术, 2021, 10(6): 2363-2372.
Xie Kehuan, Li Chuanchang, Chen Jian, et al.Simulation model advances in vanadium redox flow battery energy storage and monitoring method for state of charge[J]. Energy Storage Science and Technology, 2021, 10(6): 2363-2372.
[21] Trovò A, di Noto V, Epoupa Mengou J, et al. Fast response of kW-class vanadium redox flow batteries[J]. IEEE Transactions on Sustainable Energy, 2021, 12(4): 2413-2422.
[22] 迟晓妮, 朱敏刚, 吴秋轩. 基于等效模型的全钒液流电池运行优化控制研究[J]. 储能科学与技术, 2018, 7(3): 530-538.
Chi Xiaoni, Zhu Mingang, Wu Qiuxuan.Research on optimal operation control based on the equivalent model of VRFB system[J]. Energy Storage Science and Technology, 2018, 7(3): 530-538.
[23] Mirjalili S, Lewis A.The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95(12): 51-67.
[24] 褚鼎立, 陈红, 王旭光. 基于自适应权重和模拟退火的鲸鱼优化算法[J]. 电子学报, 2019, 47(5): 992-999.
Chu Dingli, Chen Hong, Wang Xuguang.Whale optimization algorithm based on adaptive weight and simulated annealing[J]. Acta Electronica Sinica, 2019, 47(5): 992-999.