Abstract：After analyzing different methods, a means using moving average method and empirical mode decomposition (EMD) was presented to obtain power and capacity allocation of hybrid energy storage system (HESS). Based on the life-cycle cost theory, considering the benefits when the quantity of spinning reserve and transmission network decreases, the wind power fluctuations were smoothed with the maximum net benefit. First, EMD was used to decompose the HESS reference power which was derived by moving average, and then a series of intrinsic mode functions (IMFs) were obtained. From the instantaneous frequency-time profiles of the IMFs, the so-call gap frequency was identified. Subsequently, the HESS reference power was decomposed into high and low frequency components. Power smoothing was then achieved by regulating the reference power of the power and energy storage to mitigate the high and lower frequency fluctuating components respectively. Then, taken the HESS charge-discharge efficiency and state of charge (SOC) into account, the required power and capacities of different schemes were determined. Finally, the cost-benefit model of HESS was established to compare the net benefit of each scheme and select the optimal one.
张 晴, 李欣然, 杨 明, 曹一家, 李培强. 净效益最大的平抑风电功率波动的混合储能容量配置方法[J]. 电工技术学报, 2016, 31(14): 40-48.
Zhang Qing, Li Xinran, Yang Ming, Cao Yijia, Li Peiqiang. Capacity Determination of Hybrid Energy Storage System for Smoothing Wind Power Fluctuations with Maximum Net Benefit. Transactions of China Electrotechnical Society, 2016, 31(14): 40-48.
[1] 艾小猛, 韩杏宁, 文劲宇, 等. 考虑风电爬坡事件的鲁棒机组组合[J]. 电工技术学报, 2015, 30(24): 188-195. Ai Xiaomeng, Han Xingning, Wen Jinyu, et al. Robust unit commitment considering wind power ramp events[J]. Transactions of China Electro- technical Society, 2015, 30(24): 188-195. [2] Fox B, Flynn D, Bryans L, et al. Wind power integration: connection and system operational aspect[M]. LET, 2007. [3] 国家电网公司. 风电场接入电网技术规定[S]. 修订版, 2009. [4] 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 windpower[J]. Automation of Electric Power Systems, 2013, 37(1): 14-18. [5] 袁铁江, 陈洁, 刘沛汉, 等. 储能系统改善大规模风电场出力波动的策略[J]. 电力系统保护与控制, 2014, 42(4): 47-53. Yuan Tiejiang, Chen Jie, Liu Peihan, et al. Strategy of improving large-scale wind farm output fluctuation based on energy storage system[J]. Power System Protection and Control, 2014, 42(4): 47-53. [6] 田军, 朱永强, 陈彩虹. 储能技术在分布式发电中的应用[J]. 电气技术, 2010, 11(8): 28-32, 42. Tian Jun, Zhu Yongqiang, Chen Caihong. Appli- cation of energy storage technologies in distributed generation[J]. Electrical Engineering, 2010, 11(8): 28-32, 42. [7] Wei Li, Joos G. A power electronic interface for a battery super capacitor hybrid energy storage system for wind applications[C]//Proceedings of IEEE Annual Power Electrics Specialists Conference, Rhodes, Greece, 2008: 1762-1768. [8] 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 Electro- technical Society, 2014, 29(4): 334-340. [9] 宇航. 利用储能系统平抑风电功率波动的仿真研究[D]. 吉林: 东北电力大学, 2010. [10] 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, 24. 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 CSEE, 2013, 33(19): 8-13, 24. [11] 韩晓娟, 田春光, 程成, 等. 基于经验模态分解的混合储能系统功率分配方法[J]. 太阳能学报, 2014, 35(10): 1889-1896. Han Xiaojuan, Tian Chunguang, Cheng Cheng, et al. Power allocation method of hybrid energy storage system based on empirical mode decomposition[J]. Acta Energiae Solaris Sinca, 2014, 35(10): 1889-1896. [12] 孙承晨, 袁越, San Shing CHOI, 等. 基于经验模态分解和神经网络的微网混合储能容量优化配置[J]. 电力系统自动化, 2015, 39(8): 19-26. Sun Chengchen, Yuan Yue, San Shing CHOI, et al. Capacity optimization of hybrid energy storage systems in microgrid using empirical mode decom- position and neural network[J]. Automation of Electric Power Systems, 2015, 39(8): 19-26. [13] 田崇翼, 李珂, 严毅, 等. 基于经验模式分解的风电场多时间尺度复合储能控制策略[J]. 电网技术, 2015, 39(8): 2167-2172. Tian Chongyi, Li Ke, Yan Yi, et al. A multi-time scale control strategy of hybrid energy storage system in wind farm based on empirical mode decom- position[J]. Power System Technology, 2015, 39(8): 2167-2172. [14] 杨裕生, 程杰, 曹高萍. 规模储能装置经济效益的判据[J]. 电池, 2011, 41(1): 19-21. Yang Yusheng, Cheng Jie, Cao Gaoping. A gauge for direct economic benefits of energy storage devices[J]. Battery Bimonthly, 2011, 41(1): 19-21. [15] 熊雄, 杨仁刚, 叶林, 等. 电力需求侧大规模储能系统经济性评估[J]. 电工技术学报, 2013, 28(9): 224-230. Xiong Xiong, Yang Rengang, Ye Lin, et al. Economic evaluation of large-scale energy storage allocation in power demand side[J]. Transactions of China Electrotechnical Society, 2013, 28(9): 224-230. [16] 熊雄, 叶林, 杨仁刚. 电力需求侧规模储能容量优化和经济性分析[J]. 电力系统自动化, 2015, 39(17): 42-48, 88. Xiong Xiong, Ye Lin, Yang Rengang. Optimal allocation and economic benefits analysis of energy storage system on power demand side[J]. Automation of Electric Power Systems, 2015, 39(17): 42-48, 88. [17] 王贺, 胡志坚, 张翌晖, 等. 基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测[J]. 电工技术学报, 2014, 29(4): 237-245. Wang He, Hu Zhijian, Zhang Yihui, et al. A hybrid model for short-term wind speed forecasting based on ensemble empirical mode decomposition and least squares support vector machine[J]. Transactions of China Electrotechnical Society, 2014, 29(4): 237-245. [18] You S, Rasmussen C N. Generic modelling frame- work for economic analysis of battery systems[C]// IET Conference Renewable Power Generation, Edin- burgh, 2011: 1-6. [19] 郭思琪, 袁越, 张新松, 等. 多时间尺度协调控制的独立微网能量管理策略[J]. 电工技术学报, 2014, 29(2): 122-129. Guo Siqi, Yuan Yue, Zhang Xinsong, et al. Energy management strategy of isolated microgrid based on multi-time scale coordinated control[J]. Transactions of China Electrotechnical Society, 2014, 29(2): 122-129. [20] 严干贵, 朱星旭, 李军徽, 等. 内蕴运行寿命测算的混合储能系统控制策略设计[J]. 电力系统自动化, 2013, 37(1): 110-114. Yan Gangui, Zhu Xingxu, Li Junhui, et al. Control strategy design for hybrid energy storage system with intrinsic operation life measurement and calcu- lation[J]. Automation of Electric Power Systems, 2013, 37(1): 110-114. [21] 朱星旭. 平抑风电功率波动的混合储能控制策略研究[D]. 吉林: 东北电力大学, 2014. [22] 陈丽, 张晋国, 苏海锋. 考虑并网光伏电源出力时序特性的配电网无功规划[J]. 电工技术学报, 2014, 29(12): 120-127. Chen Li, Zhang Jinguo, Su Haifeng. Reactive power planning for distribution system with grid-connected photovoltaic system considering time-sequence characteristics[J]. Transactions of China Electro- technical Society, 2014, 29(12): 120-127. [23] Electric Power Research Institute. Cost-effectiveness of energy storage in California[Z]. Palo Alto: EPRI, 2013. [24] Denholm P, Jorgenson J, Hummon M, et al. The value of energy storage for grid applications[Z]. USA: NREL, 2013. [25] Zakeri B, Syri S. Electrical energy storage systems: A comparative life cycle cost analysis[J]. Renewable & Sustainable Energy Reviews, 2015, 42(2): 569-596. [26] 连湛伟, 石欣, 克潇, 等. 电动汽车充换电站动力电池全寿命周期在线检测管理系统[J]. 电力系统保护与控制, 2014, 42(12): 137-142. Lian Zhanwei, Shi Xin, Ke Xiao, et al. The whole life cycle on-line detection and management system of power battery in the electric vehicle charging and exchanging station[J]. Power System Protection and Control, 2014, 42(12): 137-142. [27] 袁铁江, 晁勤, 李义岩, 等. 基于风电极限穿透功率的经济调度优化模型研究[J]. 电力系统保护与控制, 2011, 39(1): 15-22. Yuan Tiejiang, Chao Qin, Li Yiyan, et al. Research on optimization model of economic dispatch based on wind power penetration limit[J]. Power System Protection and Control, 2011, 39(1): 15-22. [28] 郭为民, 魏强, 唐耀华. 一种可在特高压或风电大量接入后减少旋转备用的新方法[J]. 电力系统保护与控制, 2014, 42(7): 1-7. Guo Weimin, Wei Qiang, Tang Yaohua. A new approach to decrease reserve demand when bulky wind power or UHV is connected[J]. Power System Protection and Control, 2014, 42(7): 1-7. [29] 颜志敏. 智能电网中蓄电池储能的价值评估研究[D]. 上海: 上海交通大学, 2012.
2016, Vol. 31 
