大电网可靠性的序贯和非序贯蒙特卡洛仿真的收敛性分析及比较

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Abstract The convergence performance of sequential and non-sequential Monte Carlo simulation for power grid reliability assessment is researched in detail. The probabilistic dependence relationship between computation accuracy (variance coefficient) and sample size (total years simulated or the sampling amount of states) of the two methods is analyzed. Based on the central limit theorem, the confidence interval formulae for computation accuracy and sample size of the two methods are derived, and then the conclusion is obtained: ①the convergence of sequential Monte Carlo simulation depends on the ratio of standard variance of reliability index to its expected value, the smaller the ratio, the better the convergence performance; ②the convergence of non-sequential Monte Carlo simulation depends on the expected value of loss of load probability (LOLP), the larger the value, the faster the convergence. By means of the assessment analysis in RBTS, IEEE-RTS 79 and IEEE-RT S96 test systems, the correctness of the proposed method is verified.

Key words： Reliability assessment Monte-Carlo simulation convergence confidence interval

Received: 11 October 2007 Published: 18 February 2014

PACS:

TM732

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	Zhao Yuan
	Zhou Jiaqi
	Liu Zhihong

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Zhao Yuan,Zhou Jiaqi,Liu Zhihong. Convergence Analysis and Comparison of Sequential and Nonsequential Monte-Carlo Simulation for Bulk Power System Reliability Assessment[J]. Transactions of China Electrotechnical Society, 2009, 24(11): 127-133.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2009/V24/I11/127

[1] Billinton R, Li W Y. Reliability evaluation of electric power systems using Monte Carlo methods[M]. New York: Plenue Press, 1994.
[2] 李文沅. 电力系统风险评估模型、方法和应用[M]. 北京: 科学出版社, 2006.
[3] 宋云亭, 郭永基, 程林. 大规模发输电系统充裕度评估的蒙特卡罗仿真[J]. 电网技术, 2003, 27(8): 24-28.
[4] 丁明, 张静, 李生虎. 基于序贯蒙特卡罗仿真的配电网可靠性评估模型[J]. 电网技术, 2004, 28(3): 38-42.
[5] 石文辉, 别朝红, 王锡凡. 大型电力系统可靠性评估中的马尔可链蒙特卡洛方法[J]. 中国电机工程学报, 2008, 28(4): 9-15.
[6] 刘洋, 周家启, 谢开贵. 基于Beowulf集群的大电力系统可靠性评估蒙特卡罗并行仿真[J]. 中国电机工程学报, 2006, 26(20): 9-14.
[7] Billinton R, Wangdee W. Delivery point reliability indices of a bulk electric system using sequential Monte Carlo simulation[J]. IEEE Trans. on Power Systems, 2006, 21(1): 345-351.
[8] Wangdee W, Billinton R. Bulk electric system well-being analysis using sequential Monte Carlo simulation[J]. IEEE Transactions on Power Systems, 2006, 21(1): 188-193.
[9] Billinton R, Wangdee W. Predicting bulk electricity system reliability performance indices using sequential Monte Carlo simulation[J]. IEEE Transactions on Power Delivery, 2006, 21(2): 909- 917.
[10] Billinton R, Wangdee W. Impact of utilising sequential and nonsequential simulation techniques in bulk- electric-system reliability assessment[J]. IEE Proceedings of Generation, Transmission and Distribution, 2005, 152(5): 623-628.
[11] 刘洋, 谢开贵, 周家启. 大电力系统可靠性评估高性能计算平台设计与实现[J]. 电力系统自动化, 2006, 30(18): 89-93.
[12] 赵渊, 沈智健, 周念成. 基于序贯仿真和非参数核密度估计的大电网可靠性评估[J]. 电力系统自动化, 2008, 32(6): 14-19.
[13] Billinton R, Kumar S N, Chowdhury N, et al. A reliability test system for educational purposes-basic data[J]. IEEE Trans. on Power Systems, 1989, 4(3): 1238-1244.
[14] IEEE Subcommittee. IEEE reliability test system[J]. IEEE Trans. on Power Apparatus and Systems, 1979, PAS-98(6): 2047-2054.
[15] IEEE Subcommittee. The IEEE reliability test system-1996[J]. IEEE Trans. on Power Systems, 1999, 14(3): 1010-1020.
[16] 赵渊, 周家启. 发输电组合系统可靠性评估的最优负荷削减模型分析[J]. 电网技术, 2004, 28(10): 34-37.
[17] Silverman B W. Density estimation for statistics and data analysis [M]. London: Chapman and Hall, 1986.
[18] Wand M P, Jones M C. Kernel Smoothing[M]. London: Chapman and Hall, 1995.