Probabilistic Load Flow Based on Cumulant Method Considering Multi-Slack Balance

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Abstract:

With constant improvement of intermittent energy source, its agglomeration effect and stochastic volatility will greatly influence the power system operation. It’s an inexorable trend that probabilistic power flow calculation shifts from offline analysis to real-time operation online. Among all probabilistic trend algorithms, probability flow calculation based on cumulant method is the fastest one; it has better prospect of online application. However, it requires random variables independent to each other, so the single slack bus always undertakes all unbalance power in conventional cumulant methods. When the fluctuation of system power injection becomes larger and larger, this calculation model will hardly adapted to the actual demand of the grid in the future. To improve the practicability, a improvement of probabilistic load flow based on cumulant method is proposed in this paper. By modify the calculation of the sensitivity matrix, the distribution of system unbalance power can be considered. The accuracy of this method is verified by the simulated analysis of the standard examples and actual power system model.

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Advanced Materials Research (Volumes 1070-1072)

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943-951

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December 2014

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] WEI Shu-zuo CAI Bin. Relations Between Rapid Development of Power Grids in China and Power Generation by Renewable Energy Resources. Power System Technology, 2008, 32(5): 26-30.

Google Scholar

[2] Wang Xifan, Wang Xiuli. Probabilistic load flow analysis in power system [J]. Journal of Xi'an Jiaotong University, 1988, 22(3): 87-97.

Google Scholar

[3] Yi Chiwei, Hu Zechun, Song Yonghua. A stochastic optimal power flow method considering power injection distributions [J]. Power System Technology, 2013, 37(2): 367-371.

DOI: 10.1109/pesgm.2012.6345295

Google Scholar

[4] B. Borkowska. Probabilistic load Flow [J]. IEEE Trans on PAS, 1974, PAS-93(3): 752-759.

DOI: 10.1109/tpas.1974.293973

Google Scholar

[5] A. M. Leite de Silva, V. L. Arienti. Probabilistic load flow by a multilinear simulation algorithm [J]. IEE Proceedings C Generation, Transmission and Distribution, 1990, 137(4): 276-282.

DOI: 10.1049/ip-c.1990.0037

Google Scholar

[6] DING Ming, LI Sheng-hu, HUANG Kai. Probabilistic load flow analysis based on Monte-Carlo simulation [J]. Power System Technology, 2001, 25(11): 10-22.

Google Scholar

[7] Cai Defu, Shi Dongyuan, Chen Jinfu. Probabilistic load flow calculation method based on polynomial normal transformation and latin hypercube sampling [J]. Proceedings of the CSEE, 2013, 33(13): 92-100.

DOI: 10.1109/pesgm.2012.6343972

Google Scholar

[8] Su C L. Probabilistic load-flow computation using point estimate method [J]. IEEE Transactions on Power Systems, 2005, 20(4): 1843-1851.

DOI: 10.1109/tpwrs.2005.857921

Google Scholar

[9] Morales J M, Perez-Ruiz J. Point estimate schemes to solve the probabilistic power flow [J]. IEEE Transactions on Power System, 2007, 22(4): 1594-1601.

DOI: 10.1109/tpwrs.2007.907515

Google Scholar

[10] Yang Huan, Zou Bin. A three-point estimate method for solving probabilistic power flow problems with correlated random variables [J]. Automation of Electric Power systems, 2012, 36(15): 51-56.

Google Scholar

[11] R. N. Allan, A. M. Leite Da Silva, R. C. Burchett. Evaluation methods and accuracy in probabilistic load Flow solutions [J]. IEEE Trans. on PAS, 1981, PAS-100(5): 2539-2546.

DOI: 10.1109/tpas.1981.316721

Google Scholar

[12] Hu Zechun , Wang Xifan . Error analysis of the probabilistic load flow based on cumulant method [J]. Power System Technology, 2009 , 33(18) : 32-37.

Google Scholar

[13] LIU Xiao-tuan, ZHAO Jin-quan, LUO Wei-hua. A TPNT and cumulants based probabilistic load flow approach considering the correlation variables [J]. 2013, 41(22): 13-18.

Google Scholar

[14] SHI Dong-yuan, CAI De-fu, CHEN Jin-fu, et al. Probabilistic load flow calculation based on cumulant method considering correlation between input variables [J]. Proceedings of the CSEE, 2012, 32(28): 104-113.

Google Scholar

[15] ZHU Xingyang, LIU Wenxia, ZHANG Jianhua et al. Probabilistic Load Flow Method Considering Function of Frequency Modulation s[J]. Proceedings of the CSEE, 2014, 34(1): 168-178.

Google Scholar

[16] Zhang Jietan , Wang Maochun , Xu Yourei , et al . Generation unit maintenance scheduling based on minimum cumulative risk algorithm for power system containing wind farms [J]. Power System Technology, 2011, 35(5): 97-102.

Google Scholar

[17] Nezhad A R, Mokhtari G, Davari M, et al. A new high accuracy method for calculation of LMP as a random variable [C] /International Conference on Electric Powerand Energy Conversion Systems. Sharjah, United Arab Emirates: IEEE, 2009: 1-5.

Google Scholar

[18] Liu Yifang, Zhang Buhan, Li Junfang, et al. Probabilistic load flow algorithm considering static security risk of the power system[J]. Proceedings of the CSEE, 2011, 31(1): 59-64.

Google Scholar

[19] Zhang Jietan, Cheng Haozhong, Yao Liangzhong, et al. Study on siting and sizing of distributed wind generation [J]. Proceedings of the CSEE, 2009, 29(16): 1-7.

Google Scholar

[20] ZHANG Pei, Lee S T. Probabilistic load flow computation using the method of combined cumulants and Gram-Charlier expansion [J]. IEEE Trans on Power Systems, 2004, 19(1): 676-682.

DOI: 10.1109/tpwrs.2003.818743

Google Scholar