Capacity Allocation Method of Hybrid Energy Storage System Based on Empirical Mode Decomposition and Fuzzy Chance Constrained Programming

Chao CAO; Yuxin MA; Yue CHANG; Ruifeng GUAN

doi:10.16513/j.cnki.10-1427/tk.2016.03.007

TSINGHUA JOURNALS HOME

Source Journal for Chinese Scientific and Technical Papers and Citations

RCCSE Chinese Core Academic Journals

Classification Catalogue of High Quality Scientific Journals in the field of Energy and Power

Classification Catalogue of High Quality Scientific Journals in Coal Field

PDF(1080 KB)

Distributed Energy ›› 2016, Vol. 1 ›› Issue (3) : 43-48. DOI: 10.16513/j.cnki.10-1427/tk.2016.03.007

Application Technology

Capacity Allocation Method of Hybrid Energy Storage System Based on Empirical Mode Decomposition and Fuzzy Chance Constrained Programming

Author information +

History +

Abstract

Empirical mode decomposition (EMD) method was introduced to make capacity configuration for the hybrid energy storage system (HESS), dividing the energy storage power in frequency domain and assigning to energy density and power density batteries. Capacity allocation model, based on fuzzy chance constrained programming, was built with the minimum annual cost and state of charge(SOC) confidence level as constraints. The fuzzy simulation-based genetic algorithm was used to solve the fuzzy model. The capacity and power of storage battery and super capacitor were obtained by simulation calculation. The hybrid energy storage system can be introduced to smooth the fluctuations of wind power-output and control the load of storage battery and super capacitor, so as to guarantee the stable operation of storage system.

Key words

new energy / wind power / hybrid energy storage system(HESS) smoothing / capacity allocation / genetic algorithm

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Chao CAO , Yuxin MA , Yue CHANG , et al. Capacity Allocation Method of Hybrid Energy Storage System Based on Empirical Mode Decomposition and Fuzzy Chance Constrained Programming[J]. Distributed Energy Resources. 2016, 1(3): 43-48 https://doi.org/10.16513/j.cnki.10-1427/tk.2016.03.007

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	周孝信，鲁宗相，刘应梅，等. 中国未来电网的发展模式和关键技术[J]. 中国电机工程学报，2014, 34(29): 4999-5008. ZHOU Xiaoxin, LU Zongxiang, LIU Yingmei, et al. Development models and key technologies of future grid in China[J]. Proceedings of the CSEE, 2014, 34(29): 4999-5008. Cited in this article [1]

[2]	李建林，田立亭，来小康. 能源互联网背景下的电力储能技术展望[J]. 电力系统自动化，2015, 39(23): 15-22.. LI Jianlin, TIAN Liting, LAI Xiaokang, et al. Outlook of electrical energy storage technologies under energy internet background [J]. Automation of Electric Power Systems, 2015, 39(23): 15-22. Cited in this article [1]

[3]

胡娟，杨水丽，侯朝勇，等. 规模化储能技术典型示范应用的现状分析与启示[J]. 电网技术，2015, 39(4): 879-885.

Juan

, YANG

Shuili

, HOU

Chaoyong

, et al. Present condition analysis on typical demonstration application of large-scale energy storage technology and its enlightenment[J]. Power System Technology, 2015. 39(4): 879-885.

Cited in this article [1]

[4]	LI X. Fuzzy adaptive Kalman filter for wind power output smoothing with battery energy storage system[J]. IET Renewable Power Generation, 2012, 6(5): 340-347. Cited in this article [1]

[5]	LI X, LI J, XU L, et al. Online management of lithium-ion battery based on time-triggered controller area network for fuel-cell hybrid vehicle applications[J]. Journal of Power Sources, 2010, 195(10): 3338-3343. Cited in this article [1]

[6]	韩涛，卢继平，乔梁，等. 大型并网风电场储能容量优化方案[J]. 电网技术，2010, 34(1): 169-173. HAN Tao, LU Jiping, QIAO Liang, et al. Optimized scheme of energy-storage capacity for grid-connected large-scale wind farm[J]. Power System Technology, 2010, 34(1): 169-173. Cited in this article [1]

[7]	吴云亮，孙元章，徐箭，等. 基于饱和控制理论的储能装置容量配置方法[J]. 中国电机工程学报，2011, 31(22): 32-39. WU Yunyao, SUN Yuanzhang, XU Jian, et al. Determination methodology for energy storage capacity based on saturation control theory[J]. Proceedings of the CSEE, 2011, 31(22): 32-39. Cited in this article [1]

[8]	韩晓娟，程成，籍天明，等. 计及电池使用寿命的混合储能系统容量优化模型[J]. 中国电机工程学报，2013, 33(34): 91-97. HAN Xiaojuan, CHENG Cheng, JI Tianming, et al. Capacity optimal modeling of hybrid energy storage systems considering battery life[J]. Proceedings of the CSEE, 2013, 33(34): 91-97. Cited in this article [1]

[9]	YOSHIMOTO K, NANAHARA T, KOSHIMIZU G. New control method for regulating state-of-charge of a battery in hybrid wind power/battery energy storage system[C]//Power Systems Conference and Exposition, Atlanta, 2006: 1244-1251. Cited in this article [1]

[10]	孟顺，谢桦，基于经验模态分解的平滑可再生能源功率波动的储能容量优化[J]. 电源学报，2014, 5(5): 7-11. MENG Shun, XIE Hua. Energy storage capacity optimization in smoothing renewable energy power fluctuation based on empirical mode decomposition[J]. Journal of Power Supply, 2014, 5(5): 7-11. Cited in this article [1]

[11]

韩晓娟，陈跃燕，张浩，等. 基于小波包分解的混合储能技术在平抑风电场功率波动中的应用[J]. 中国电机工程学报，2013, 33(19): 8-13.

HAN

Xiaojuan

, CHEN

Yueyan

, ZHANG

Hao

, et al. Application of hybrid energy stor age technology based on wavelet packet decomposition in smoothing the fluctuations of wind power [J]. Proceedings of the CSEE, 2013, 33(19): 8-13.

Cited in this article [1]

[12]	PATRICH F, GABRIEL B, PAULO G. Empirical mode decomposition as a filter bank[J]. IEEE Transactions on Signal Processing Letters, 2004, 11(2): 112-114. Cited in this article [1]

[13]	LI X, HUI D, LAI X. Battery energy storage station (bess)-based smoothing control of photovoltaic (PV) and wind power generation fluctuations[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2): 464-473. Cited in this article [1]

[14]	谢石骁，杨莉，李丽娜. 基于机会约束规划的混合储能优化配置方法[J]. 电网技术，2012, 36(5): 79-84. XIE Shixiao, YANG Li, LI Lina, et al. A chance constrained programming based optimal configuration method of hybrid energy storage system[J]. 2012, 36(5): 79-84. Cited in this article [1]

[15]	雷亚洲，王伟胜，印永华，等. 基于机会约束规划的风电穿透功率极限计算[J]. 中国电机工程学报. 2002. 22(5): 32-35. LEI Yazhou, WANG Weisheng, YIN Yonghua, et al. Wind power penetration limit calculation based on chance contrained programming[J]. Proceedings of the CSEE, 2002. 22(5): 32-35. Cited in this article [1]

[16]	赵国波，刘天琪，李兴源，等. 基于灰色机会约束规划的输电系统规划[J]. 电网技术，2009, 33(1): 22-25. ZHAO Guobo, LIU Tianqi, LI Xingyuan, et al. Power transmission system planning based on grey chance constrained programming[J]. Power System Technology, 2009, 33(1): 22-25. Cited in this article [1]

[17]	杨宁，文福栓. 基于机会约束规划的输电系统的规划方案[J]. 电力系统自动化，2004, 28(14): 23-27. YANG Ning, WEN Fushuan. Transmission system expansion planning based on chance constrained programming[J]. Automation of Electric Power Systems, 2004, 28(14): 23-27. Cited in this article [1]

[18]	YUE Xiaofeng, SHAO Haihe. Fault diagnosis of rolling element bearing based on improved ensemble empirical mode decomposition[C]//Proceedings of International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou: IEEE, 2015: 52-55. Cited in this article [1]