Optimization of Restoration Paths of Power System after Blackout

Article Preview

Abstract:

Optimization of restoration paths of power system after blackout is a multi-stage, multi-target, multi-variable combinatorial problem in the power system restoration. This paper presents a reasonable model and effectually method. The proposed model is considered as a typical partial minimum spanning tree problem from the mathematical point of view which considering all kinds of constraints. Improved data envelopment analysis (DEA) was used to get the weight which considering line charging reactive power, weather conditions, operation time and betweenness of transmission lines. The improved genetic algorithm method is employed to solve this problem. Finally, an example is given which proves the strategy of the line restoration can effectively handle the uncertainty of the system recovery process, to guarantee the system successfully restored after the catastrophic accidents.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1467-1472

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Zhou Xiaoxin, Zheng Jianchao, Shen Guorong, et a1.Draw lessons from large scope blackout of interconnected North America power grid[J].Power System Technology, 2003, 27(9):1.

Google Scholar

[2] Zhang Wenliang, Zhou Xiaoxin, Bai Xiaomin, et al.Countermeasures against sudden events to ensure the security of urban power supply systems[J].Proceedings of the CSEE, 2008, 28(22):1-7.

Google Scholar

[3] LIN Jikeng', LI Tongfei, et al.Assessment on Power System Black-Start Schemes Based on Entropy-Weighted Fuzzy Comprehensive Evaluation Model[J].Power System Technology, 2012, 35(2):115-120.

Google Scholar

[4] Gu Xueping, Zhao Shuqiang, Liu Yan, et al.A practical decision support system for power system black start[J].Power System Technology, 2004, 28(9):54-57.

Google Scholar

[5] Zhou Yunhai, Liu Yingshang, Hu Xiangyong.Power system network reconstruction after blackout[J].Proceedings of the CSEE, 2008, 28(10):32-36.

Google Scholar

[6] HAN Zhong-hui, GU Xue-ping, LIU Yan.Optimization of Restoration Paths Considering Unit Start-up Time Requirements at Early Stage of Power System Restoration[J].Proceedings of the CSEE, 2009, 29(4):21-26.

Google Scholar

[7] LIU Qiang, SHI Li-bao2, NI Yi-xin,  et al.Intelligent Optimization Strategy of the Power Grid Reconfiguration During Power System Restoration[J].Proceedings of the CSEE, 2009, 29(13):8-15.

Google Scholar

[8] Nagata T, Sasaki H.A multi-agent approach to power system restoration[J].IEEE Trans.on Power Systems, 2002, 17(2):457-452.

DOI: 10.1109/tpwrs.2002.1007918

Google Scholar

[9] Yen J, Yan Yonghe, Contreras J, et al.Multi-agent approach to the planning of power transmission expansion[J] . Decision Support System, 2000, 28(3):279-290.

DOI: 10.1016/s0167-9236(99)00092-5

Google Scholar

[10] Liu Yan, Gu Xueping.Node importance assessment based skeleton-network reconfiguration[J].Proceedings of the CSEE, 2007, 27(10):20-27.

Google Scholar

[11] ZENG Shunqi, WEN Fushuan, XUE Yusheng, at al.Optimization of Network Reconfiguration Strategy for Power Systems Consideration Operating Time Uncertainty[J].Automation of Electric Power Systems, 2011, 35(23):43-46.

Google Scholar

[12] X. Gu.H. Zhong Optimisation of network reconfiguration based on a two-layer unit-restarting framework for power system restoration[J].IET Generation, Transmission & Distribution, 2012, 6(7):693-700.

DOI: 10.1049/iet-gtd.2011.0591

Google Scholar

[13] Guo Jiayang, Wu Tao, Zhang Renwei, et al.Test and research of blackstart in North China power network[J].North China Electric Power, 2001(5):3-18.

Google Scholar

[14] J Sarkis.A comparative analysis of DEA as a discrete alternative multiple criteria decision tool[J].Eur. J Opl Res, 2000, 123: 547-557.

Google Scholar

[15] WANG Liang, LIU Yan, GU Xuping, et al.Skeleton-network Reconfiguration Base on Node Importance and Line Berweenness[J].Automation of Electric Power Systems, 2010, 34(12):29-33.

Google Scholar