基于多次滑动均值滤波的混合储能功率分配与定容研究

doi:10.19595/j.cnki.1000-6753.tces.222348

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Abstract When the hybrid energy storage system (HESS) allocates power in the scenario of suppressing wind power fluctuations, the power distribution is inaccurate due to mode aliasing, which leads to the poor economy of the hybrid energy storage system. To address these issues, this paper suggests a method of multiple moving average filtering (MMAF) to decompose the total power command of hybrid energy storage.
Firstly, the minimum flat total power command of hybrid energy storage meeting the flat requirements was obtained, and the obtained total power command of hybrid energy storage was filtered through MMAF to make the battery respond to the filtered low frequency part and the supercapacitor respond to the high frequency part. Pearson correlation coefficient was used as the index to quantify modal aliasing, and Pearson correlation coefficient was used as the basis to determine the optimal filtering sliding window size and the optimal filtering times to ensure that the maximum limit of modal aliasing can be achieved under the sliding window size and filtering times. Taked the power command of the battery and the supercapacitor as the feasible region of the energy storage capacity, the minimum rated power and rated capacity of the hybrid energy storage system can be determined by comprehensively considering the battery state of charge (SOC) constraints. Secondly, based on the equivalent operating time, a quantitative model of battery life cycle was established, which provided a basis for the economic analysis of hybrid energy storage system. The cumulative effect of battery charging and discharging on its life loss was considered in the model, and the actual number of battery cycles was converted into the equivalent number of cycles at the rated discharge depth. Finally, the incremental factor of battery maintenance cost was introduced into the hybrid energy storage life cycle cost model (LCC) to describe the nonlinear increase of maintenance cost caused by the cumulative effect of battery aging every ten years, so as to better conform to the actual operation of the energy storage battery and improve the practicability of the LCC model. The effectiveness of the proposed method to reduce the comprehensive cost of hybrid energy storage was verified by simulation experiments.
According to the actual simulation experiment, the method in this paper can well smooth the power fluctuation of wind power grid connection, made it meet the grid connection requirements, and the hybrid energy storage configuration result was better than other configuration methods. The simulation results under different working conditions showed that MMAF method can ensure the battery operation life during power distribution, and can minimize the mode aliasing phenomenon and reduce the comprehensive cost of hybrid energy storage. Under normal working conditions, the LCC of MMAF was reduced by 87.6% compared with the empirical mode decomposition method (EMD). Compared with the variational mode decomposition method (VMD), the LCC of MMAF was reduced by 3.3%. Compared with the wavelet transform (WT) method, the LCC of MMAF was reduced by 56.8%. In case of failure, the battery and supercapacitor form a standby working mode for each other. The total power command of mixed energy storage was redistributed by MMAF method, which avoided the situation that the power command cannot respond due to battery failure, curbs the possibility of wind turbine disconnection, and avoided greater economic losses.

Key words： Hybrid energy storage power distribution mode aliasing MMAF algorithm quantitative model of battery life cycle

Received: 21 December 2022

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TM711

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	Tian Bowen
	Zhang Zhiyu
	Yang Mengfei

Cite this article:

Tian Bowen,Zhang Zhiyu,Yang Mengfei. Research on Hybrid Energy Storage Power Allocation and Capacity Determination Based on Multiple Moving Average Filtering[J]. Transactions of China Electrotechnical Society, 2024, 39(5): 1548-1564.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.222348 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I5/1548

[1] 刘其辉, 逄思敏, 吴林林, 等. 大规模风电汇集系统电压不平衡机理、因素及影响规律[J]. 电工技术学报, 2022, 37(21): 5435-5450.
Liu Qihui, Pang Simin, Wu Linlin, et al.The mechanism, factors and influence rules of voltage imbalance in wind power integration areas[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5435-5450.
[2] 顾雪平, 白岩松, 李少岩, 等. 考虑风电不确定性的电力系统恢复全过程两阶段鲁棒优化方法[J]. 电工技术学报, 2022, 37(21): 5462-5477.
Gu Xueping, Bai Yansong, Li Shaoyan, et al.Two stage robust optimization method for the whole-process power system restoration considering wind power uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(21): 5462-5477.
[3] de Siqueira L M S, Peng Wei. Control strategy to smooth wind power output using battery energy storage system: a review[J]. Journal of Energy Storage, 2021, 35: 102252.
[4] 齐先军, 郑夕炜, 王晓蓉, 等. 基于时频分析的改进小波包风电功率波动平抑方法[J]. 太阳能学报, 2022, 43(7): 302-309.
Qi Xianjun, Zheng Xiwei, Wang Xiaorong, et al.Improved wavelet packet method of smoothing wind power fluctuations based on time-frequency analysis[J]. Acta Energiae Solaris Sinica, 2022, 43(7): 302-309.
[5] 李政, 刘宏伟, 康健, 等. 混合储能参与自动发电控制容量优化配置[J]. 电气技术, 2021, 22(9): 34-40.
Li Zheng, Liu Hongwei, Kang Jian, et al.Participation of hybrid energy storage in capacity optimization configuration of automatic generation control system[J]. Electrical Engineering, 2021, 22(9): 34-40.
[6] Wang Huaqing, Xie Zhuoshi, Pu Lei, et al.Energy management strategy of hybrid energy storage based on Pareto optimality[J]. Applied Energy, 2022, 327: 120095.
[7] Zhou Shiyao, Chen Ziqiang, Huang Deyang, et al.Model prediction and rule based energy management strategy for a plug-in hybrid electric vehicle with hybrid energy storage system[J]. IEEE Transactions on Power Electronics, 2021, 36(5): 5926-5940.
[8] Xu Yanhui, Xu Yijia, Huang Yan.Generation of typical operation curves for hydrogen storage applied to the wind power fluctuation smoothing mode[J]. Global Energy Interconnection, 2022, 5(4): 353-361.
[9] Jin He, Liu Pei, Li Zheng.Dynamic modeling and design of a hybrid compressed air energy storage and wind turbine system for wind power fluctuation reduction[J]. Computers & Chemical Engineering, 2019, 122: 59-65.
[10] 孙玉树, 唐西胜, 孙晓哲, 等. 基于MPC-HHT的多类型储能协调控制策略研究[J]. 中国电机工程学报, 2018, 38(9): 2580-2588, 2826.
Sun Yushu, Tang Xisheng, Sun Xiaozhe, et al.Research on multi-type energy storage coordination control strategy based on MPC-HHT[J]. Proceedings of the CSEE, 2018, 38(9): 2580-2588, 2826.
[11] 袁铁江, 张昱, 栗磊, 等. 计及功率密度约束含氢储能的预装式多元储能电站容量优化配置研究[J]. 电工技术学报, 2021, 36(3): 496-506.
Yuan Tiejiang, Zhang Yu, Li Lei, et al.Capacity optimization configuration of pre-installed multi-energy storage power station considering power density constrained hydrogen storage[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 496-506.
[12] 李奇, 赵淑丹, 蒲雨辰, 等. 考虑电氢耦合的混合储能微电网容量配置优化[J]. 电工技术学报, 2021, 36(3): 486-495.
Li Qi, Zhao Shudan, Pu Yuchen, et al.Capacity optimization of hybrid energy storage microgrid considering electricity-hydrogen coupling[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 486-495.
[13] 钱韦廷, 赵长飞, 万灿, 等. 基于概率预测的混合储能平抑风电波动随机优化调控方法[J]. 电力系统自动化, 2021, 45(18): 18-27.
Qian Weiting, Zhao Changfei, Wan Can, et al.Probabilistic forecasting based stochastic optimal dispatch and control method of hybrid energy storage for smoothing wind power fluctuations[J]. Automation of Electric Power Systems, 2021, 45(18): 18-27.
[14] 李建林, 牛萌, 周喜超, 等. 能源互联网中微能源系统储能容量规划及投资效益分析[J]. 电工技术学报, 2020, 35(4): 874-884.
Li Jianlin, Niu Meng, Zhou Xichao, et al.Energy storage capacity planning and investment benefit analysis of micro-energy system in energy interconnection[J]. Transactions of China Electrote-chnical Society, 2020, 35(4): 874-884.
[15] Yang Yuqing, Bremner S, Menictas C, et al.Modelling and optimal energy management for battery energy storage systems in renewable energy systems: a review[J]. Renewable and Sustainable Energy Reviews, 2022, 167: 112671.
[16] 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.
[17] 郭玲娟, 魏斌, 韩肖清, 等. 基于集合经验模态分解的交直流混合微电网混合储能容量优化配置[J]. 高电压技术, 2020, 46(2): 527-537.
Guo Lingjuan, Wei Bin, Han Xiaoqing, et al.Capacity optimal configuration of hybrid energy storage in hybrid AC/DC micro-grid based on ensemble empirical mode decomposition[J]. High Voltage Engineering, 2020, 46(2): 527-537.
[18] 李亚楠, 王倩, 宋文峰, 等. 基于变分模态分解和Hilbert变换的平滑风电出力混合储能容量优化配置[J]. 电测与仪表, 2019, 56(1): 82-88, 95.
Li Yanan, Wang Qian, Song Wenfeng, et al.Capacity configuration of hybrid energy storage for smoothing wind power outputs based on variational mode decomposition and Hilbert transform[J]. Electrical Measurement & Instrumentation, 2019, 56(1): 82-88, 95.
[19] 李鑫, 王娟, 邱亚, 等. 基于VMD的混合储能容量优化配置[J]. 太阳能学报, 2022, 43(2): 88-96.
Li Xin, Wang Juan, Qiu Ya, et al.Optimal allocation of hybrid energy storage capacity based on variational mode decomposition[J]. Acta Energiae Solaris Sinica, 2022, 43(2): 88-96.
[20] 杜佳耘, 雷勇, 李永凯, 等. 基于参数优化变分模态分解的混合储能功率分配策略[J]. 现代电力, 2021, 38(1): 51-59.
Du Jiayun, Lei Yong, Li Yongkai, et al.Hybrid energy storage strategy based on parameter optimized variational mode decomposition[J]. Modern Electric Power, 2021, 38(1): 51-59.
[21] 李建林, 郭斌琪, 牛萌, 等. 风光储系统储能容量优化配置策略[J]. 电工技术学报, 2018, 33(6): 1189-1196.
Li Jianlin, Guo Binqi, Niu Meng, et al.Optimal configuration strategy of energy storage capacity in wind/PV/storage hybrid system[J]. Transactions of China Electrotechnical Society, 2018, 33(6): 1189-1196.
[22] Zhu Honglu, Hou Ruyin, Jiang Tingting, et al.Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications[J]. Global Energy Interconnection, 2021, 4(6): 608-618.
[23] Hou Hui, Xu Tao, Wu Xixiu, et al.Optimal capacity configuration of the wind-photovoltaic-storage hybrid power system based on gravity energy storage system[J]. Applied Energy, 2020, 271: 115052.
[24] 王再闯, 陈来军, 李笑竹, 等. 基于合作博弈的产销者社区分布式光伏与共享储能容量优化[J]. 电工技术学报, 2022, 37(23): 5922-5932.
Wang Zaichuang, Chen Laijun, Li Xiaozhu, et al.Capacity optimization of distributed PV and shared energy storage of prosumer community based on cooperative game[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5922-5932.
[25] Ye Lin, Zhang Cihang, Tang Yong, et al.Hierarchical model predictive control strategy based on dynamic active power dispatch for wind power cluster integration[J]. IEEE Transactions on Power Systems, 2019, 34(6): 4617-4629.
[26] Guo Tingting, Liu Youbo, Zhao Junbo, et al.A dynamic wavelet-based robust wind power smoothing approach using hybrid energy storage system[J]. International Journal of Electrical Power & Energy Systems, 2020, 116: 105579.
[27] 陈亚爱, 林演康, 王赛, 等. 基于滤波分配法的混合储能优化控制策略[J]. 电工技术学报, 2020, 35(19): 4009-4018.
Chen Yaai, Lin Yankang, Wang Sai, et al.Optimal control strategy of hybrid energy storage based on filter allocation method[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4009-4018.
[28] 张明龙, 张振宇, 高源, 等. 基于变分模态分解的暂态扰动波形去噪算法[J]. 电力系统保护与控制, 2022, 50(8): 43-49.
Zhang Minglong, Zhang Zhenyu, Gao Yuan, et al.Transient disturbance waveform denoising algorithm based on variational mode decomposition[J]. Power System Protection and Control, 2022, 50(8): 43-49.
[29] Omar A I, Ali Z M, Al-Gabalawy M, et al.Multi-objective environmental economic dispatch of an electricity system considering integrated natural gas units and variable renewable energy sources[J]. Mathematics, 2020, 8(7): 1100.
[30] 葛玉友, 尚策. 寿命约束的储能规划[J]. 中国电机工程学报, 2020, 40(19): 6150-6161.
Ge Yuyou, Shang Ce.Energy storage planning constrained by its life[J]. Proceedings of the CSEE, 2020, 40(19): 6150-6161.
[31] 丁明, 吴杰, 张晶晶. 面向风电平抑的混合储能系统容量配置方法[J]. 太阳能学报, 2019, 40(3): 593-599.
Ding Ming, Wu Jie, Zhang Jingjing.Capacity optimization method of hybrid energy storage system for wind power smoothing[J]. Acta Energiae Solaris Sinica, 2019, 40(3): 593-599.
[32] 马兰, 谢丽蓉, 叶林, 等. 基于混合储能双层规划模型的风电波动平抑策略[J]. 电网技术, 2022, 46(3): 1016-1029.
Ma Lan, Xie Lirong, Ye Lin, et al.Wind power fluctuation suppression strategy based on hybrid energy storage Bi-level programming model[J]. Power System Technology, 2022, 46(3): 1016-1029.