Abstract：This article discusses an active control strategy for composited energy storage system (CESS) with compressed air energy storage (CAES) in AC-DC hybrid micro-grid. Firstly, considering the requirements of accuracy and computing speed in engineering practice, a combined prediction method based on OP-ELM and SVR is applied to implement multi-time scale prediction for output power of distributed generation (DG). Accordingly, the day-ahead scheduling of CESS is schemed. Furthermore, the day-ahead scheduling of CESS is decomposed into several IMFs using empirical mode decomposition (EMD). Each IMF is analyzed by the theory of instantaneous frequency so that the objective function based on energy storage cycle life and SOC balance is built up. All IMFs are reorganized into three groups. Lastly, a real data based simulation validates the effectiveness of the proposed strategy. It is shown that this strategy can consider the power of DG and CESS as a whole and regulate its output power actively on the basis of optimizing the cycling life and the SOC balance of energy storage, thus effectively improving the energy utilization ratio as well as economic benefits.
严毅, 张承慧, 李珂, 田崇翼, 王帆. 含压缩空气的微网复合储能系统主动控制策略[J]. 电工技术学报, 2017, 32(20): 231-240.
Yan Yi, Zhang Chenghui, Li Ke, Tian Chongyi, Wang Fan. An Active Control Strategy for Composited Energy Storage with Compressed Air Energy Storage in Micro-Grid. Transactions of China Electrotechnical Society, 2017, 32(20): 231-240.
[1] Boicea V A. Energy storage technologies: the past and the present[J]. Proceedings of the IEEE, 2014, 102(11): 1777-1794. [2] 田春光, 田利, 李德鑫, 等. 基于混合储能系统跟踪光伏发电输出功率的控制策略[J]. 电工技术学报, 2016, 31(14): 75-83. Tian Chunguang, Tian Li, Li Dexin, et al. Control strategy for tracking the output power of photovoltaic power generation based on hybrid energy storage system[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 75-83. [3] 袁小明, 程时杰, 文劲宇. 储能技术在解决大规模风电并网问题中的应用前景分析[J]. 电力系统自动化, 2013, 37(1): 14-18. Yuan Xiaoming, Cheng Shijie, Wen Jinyu. Prospects analysis of energy storage application in grid integration of large-scale wind[J]. Automation of Electric Power Systems, 2013, 37(1): 14-18. [4] Wang X Y, Mahinda Vilathgamuwa D, Choi S S. Determination of battery storage capacity in energy buffer for wind farm[J]. IEEE Transactions on Energy Conversion, 2008, 23(3): 868-878. [5] 张纯江, 董杰, 刘君, 等. 蓄电池与超级电容混合储能系统的控制策略[J]. 电工技术学报, 2014, 29(4): 334-340. Zhang Chunjiang, Dong Jie, Liu Jun, et al. A control strategy for battery-ultracapacitor hybrid energy storage system[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 334-340. [6] 蒋小平, 彭朝阳, 魏立彬, 等. 基于模糊控制的混合储能平抑风电功率波动[J]. 电力系统保护与控制, 2016, 44(17): 126-132. Jiang Xiaoping, Peng Chaoyang, Wei Libin, et al. Hybrid energy storage for smoothing wind power fluctuations based on fuzzy control[J]. Power System Protectionand Control, 2016, 44(17): 126-132. [7] Yucekaya A. The operational economics of compressed air energy storage systems under uncer- tainty[J]. Renewable & Sustainable Energy Reviews, 2013, 22(8): 298-305. [8] Abbaspour, Satkin M, Mohammadi-Ivatloo B, et al. Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)[J]. Renewable Energy, 2013, 51(7): 53-59. [9] 陈来军, 梅生伟, 王俊杰, 等. 面向智能电网的大规模压缩空气储能技术[J]. 电工电能新技术, 2014, 33(6): 1-6. Chen Laijun, Mei Shengwei, Wang Junjie, et al. Smart grid oriented large-scale compressed air energy storage technology[J]. Advanced Technology of Electrical Engineering & Energy, 2014, 33(6): 1-6. [10] Mei S W, Wang J J, Tian F, et al. Design and engineering implementation of non-supplementary fired compressed air energy storage system: TICC- 500[J]. Science China Technological Sciences, 2015, 58(4): 600-611. [11] 薛小代, 陈晓弢, 梅生伟, 等. 采用熔融盐蓄热的非补燃压缩空气储能发电系统性能[J]. 电工技术学报, 2016, 31(14): 11-20. Xue Xiaodai, Chen Xiaotao, Mei Shengwei, et al. Performance of non-supplementary fired compressed air energy storage with molten salt heat storage[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 11-20. [12] 陈海生, 刘金超, 郭欢, 等. 压缩空气储能技术原理[J]. 储能科学与技术, 2013, 2(2): 146-151. Chen Haisheng, Liu Jinchao, Guo Huan, et al. Technical principle of compressed air energy storage system[J]. Energy Storage Science and Technology, 2013, 2(2): 146-151. [13] 王成山, 武震, 李鹏. 微电网关键技术研究[J]. 电工技术学报, 2014, 29(2): 1-12. Wang Chengshang, Wu Zhen, Li Peng. Research on key technologies of microgrid[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 1-12. [14] 邹见效, 戴碧蓉, 彭超, 等. 基于荷电状态分级优化的混合储能风电功率平抑方法[J]. 电力系统自动化, 2013, 37(24): 1-6. Zou Jianxiao, Dai Birong, Peng Chao, et al. Wind power smoothing method using hybrid energy storage system based on SOC hierarchical optimization[J]. Automation of Electric Power Systems, 2013, 37(24): 1-6. [15] Jin C, Lu N, Lu S, et al. Coordinated control algorithm for hybrid energy storage systems[C]//IEEE Power and Energy Society General Meeting, Detroit, MI, 2011: 1-7. [16] 张野, 郭力, 贾宏杰, 等. 基于平滑控制的混合储能系统能量管理方法[J]. 电力系统自动化, 2012, 36(16): 36-41. Zhang Ye, Guo Li, Jia Hongjie, et al. An energy management method of hybrid energy storage system based on smoothing control[J]. Automation of Electric Power Systems, 2012, 36(16): 36-41. [17] 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 storage technology based on wavelet packet decomposition in smoothing the fluctuations of wind power[J]. Proceedings of the Chinese Society of Electrical Engineering, 2013, 33(19): 8-13. [18] 卢芸, 徐骏. 基于小波包分解的风电混合储能容量配置方法[J]. 电力系统保护与控制, 2016, 44(11): 149-154. Lu Yun, Xu Jun. Wind power hybrid energy storage capacity configuration based on wavelet packet decomposition[J]. Power System Protection & Control, 2016, 44(11): 149-154. [19] 刘兴杰, 米增强, 杨奇逊, 等. 一种基于EMD的短期风速多步预测方法[J]. 电工技术学报, 2010, 25(4): 165-170. Liu Xingjie, Mi Zengqiang, Yang Qixun, et al. A novel multi-step prediction for wind speed based on EMD[J]. Transactions of China Electrotechnical Society, 2010, 25(4): 165-170. [20] 陈昊, 万秋兰, 王玉荣. 基于厚尾均值广义自回归条件异方差族模型的短期风电功率预测[J]. 电工技术学报, 2016, 31(5): 91-98. Chen Hao, Wan Qiulan, Wang Yurong. Short-term wind power forecast based on fat-tailed generalized autoregressive conditional heteroscedasticity-in-mean type models[J]. Transactions of China Electro- technical Society, 2016, 31(5): 91-98. [21] 孟安波, 陈育成. 基于虚拟预测与小波包变换的风电功率组合预测[J]. 电力系统保护与控制, 2014, 42(3): 71-76. Meng Anbo, Chen Yucheng. Wind power combination forecasting based on wavelet packet transform and virtual forecasting method[J]. Power System Protection & Control, 2014, 42(3): 71-76. [22] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications[J]. Neurocom- puting, 2006, 70(1-3): 489-501. [23] Huang G B, Zhou H, Ding X, et al. Extreme learning machine for regression and multiclass classifi- cation[J]. IEEE Transactions on Systems Man & Cybernetics Part B (Cybernetics), 2012, 42(2): 513- 529. [24] Huang N E. Computer implemented empirical mode decomposition method, apparatus and article of manufacture: US, US5983162[P]. 1999. [25] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society: A Mathematical Physical & Engineering Sciences, 1998, 454(1971): 903-995. [26] Huang N E, Shen Z, Long S R. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annual Review of Fluid Mechanics, 2003, 31(1): 417-457. [27] Lundsager P, Abdulwahid U, Baring-Gould I, et al. Lifetime modelling of lead acid batteries[J]. Journal of Power Sources, 2005, 53(1): 111-117. [28] Downing S D, Socie D F. Simple rainflow counting algorithms[J]. International Journal of Fatigue, 1982, 1(1): 31-40. [29] 田崇翼, 张承慧, 李珂, 等. 含压缩空气储能的微网复合储能技术及其成本分析[J]. 电力系统自动化, 2015, 39(10): 36-41. Tian Chongyi, Zhang Chenghui, Li Ke, et al. Com- posite energy storage technology with compressed air energy storage in microgrid and its cost analysis[J]. Automation of Electric Power Systems, 2015, 39(10): 36-41. [30] 中国电力出版社. Q/GDW 432—2010风电调度运行管理规范[M]. 北京: 中国电力出版社, 2010.
2017, Vol. 32 
