Research on the Allocated Capacity of Energy Storage Based on Adjustment of SOC

Article Preview

Abstract:

In order to remedy errors in wind power prediction, a common method adopted in wind farms is the configuration of a certain capacity of energy storage at the grid interface. The traditional approach for configuring capacity ignores the problems of excessive allocation and the adjustment of SOC, and for this reason, the paper proposes a model of wind power coupling by adding a resistance load at the grid connection point. In addition, this paper proposes a control strategy to improve the operation of energy storage systems through the adjustment of SOC in order to maintain a high level of SOC, and proposes a new approach to deploying storage capacity based on this. According to the analysis of a wind farm in Northeast China, a new approach to the configuration of the energy storage can use local utility of a little electrical energy to reduce the allocated capacity, and furthermore, this method also has better control characteristics.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

3502-3507

Citation:

Online since:

November 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Chi Yongning, Liu Yanhua, Wang Weisheng. Study on impact of wind power integration on power system. [J]. Power System Technology, 2007, 31(3): 77-81.

Google Scholar

[2] Zhang Liying, Ye Tinglu, Xin Yaozhong. Problems and measures of power grid accommodating large scale wind power. [J]. Proceedings of the CSEE, 2010, 30(25): 1-9.

Google Scholar

[3] Gu Xingkai, Fan Gaofeng, Wang Xiaorong. Summarization of wind power prediction technology. [J]. Power System Technology, 2007, 31(2): 335-338.

Google Scholar

[4] Nan Xiaoqiang, Li Qunzhan, Qiu Daqiang. Short-term wind speed syntheses correcting forecasting model and its application. [J]. International Journal of Electrical Power & Energy Systems, 2013, 49: 264-268.

DOI: 10.1016/j.ijepes.2013.01.014

Google Scholar

[5] Alexiadis M C. Dokopoulos P S. Sahsamanoglou H S Wind speed and power forecasting based on spatialcorrelation models. [J]. IEEE Transactions on Energy Conversion, 1999, 14   (3): 836-842.

DOI: 10.1109/60.790962

Google Scholar

[6] Du Ying, Lu Jiling, Li Qing. Short term wind speed forecasting of wind farm based on east square-support vector machine. [J]. Power System Technology, 2008, 32(15): 62-66.

Google Scholar

[7] Fan Gaofeng, Wang Weisheng, Liu Chun. Wind power prediction based on artificial   neural network [J]. Proceedings of the CSEE, 2008, 28(34): 118-123.

Google Scholar

[8] Zhang Wenliang, Qiu Ming, Lai Xiaokang. Application of energy storage technologies in   power grids[J], Power System Technology, 2008, 32(7): 1-9.

Google Scholar

[9] Sun Guoqiang, Wei Zhinong, Zhai Weixing. Short term wind speed forecasting based on RVM and ARMA error correcting. [J]. Transaction of China Electrotechnical Society, 2012, 27(8): 188-193.

DOI: 10.1109/appeec.2016.7779542

Google Scholar

[10] Yang Wenjia, Kang Chongqing, Xia Qing. Short term probabilistic load forecasting based on statistics of probability distribution of forecasting errors. [J]. Automation of Electric Power Systems, 2006, 30(19): 47-52.

Google Scholar

[11] Li Bei, Guo Jianbo. A control strategy for battery energy storage system to level wind power output [J]. Power System Technology, 2012, 36(8): 8-12.

Google Scholar