考虑光伏和负荷相关性的概率潮流计算

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Abstract The correlations between power outputs of photovoltaic (PV) generators and loads at adjacent locations actually exist and are not accurately considered in the current research on the probabilistic power flow analysis. Based on the parametric and nonparametric probabilistic modeling theories, the comprehensive model of PV generation is proposed and the randomness of PV outputs can be accurately reflected without any assumption of theoretical distributions. The correlations between the PV outputs and loads are considered by combining the marginal transformation and Cholesky decomposition techniques. Furthermore, the Monte Carlo based probabilistic power flow analysis method is developed, which can capture the correlations between PV outputs and loads obeying different probability distributions. The Latin Hypercube Sampling method, an efficient sampling method, is also introduced to decrease the computation burden. The measured power data of PV generator in USA and the 69-node distribution network are used to demonstrate the correctness and effectiveness of the presented probabilistic power flow analysis method.

Key words： Photovoltaic generation correlation Monte Carlo probabilistic power flow

Received: 12 December 2013 Published: 30 December 2015

PACS:

TM744

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	Ren Zhouyang
	Yan Wei
	Xiang Bo
	Zhao Xia
	Wang Qianggang
	Yu Juan

Cite this article:

Ren Zhouyang,Yan Wei,Xiang Bo等. Probabilistic Power Flow Analysis Incorporating the Correlations between PV Power Outputs and Loads[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 181-187.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I24/181

[1] Arthouros Zervos. Renewables 2013 global status report. renewable energy policy network for the 21st century[M]. Paris, France, 2013.
[2] Chen Yan, Wen Jinyu, Cheng Shijie. Probabilistic load flow method based on nataf transformation and latin hypercube sampling[J]. IEEE Transactions on Sustainable Energy, 2013, 4(2): 394-301.
[3] 董雷, 杨以涵, 张传成, 等. 综合考虑网络结构不确定性的概率潮流计算方法[J]. 电工技术学报, 2012, 27(1): 210-216.
Dong lei, Yang Yihan, Zhang Chuancheng, et al. Probabilistic load flow considering network configuration uncertainties[J]. Transactions of China Electrotechnical Society, 2012, 27(1): 210-216.
[4] 洪芦诚, 石立宝, 姚良忠, 等. 计及风电场发电功率不确定性的电力系统模糊潮流[J]. 电工技术学报, 2010, 25(8): 116-122.
Hong Lucheng, Shi Libao, Yao Liangzhong, et al. Fuzzy modelling and solution of load flow incorporating uncertainties of wind farm generation[J]. Transactions of China Electrotechnical Society, 2010, 25(8): 116-122.
[5] 张谦, 奚巍民, 黄俊辉, 等. 考虑系统运行方式的江苏电网过江断面概率潮流实证分析[J]. 电力系统保护与控制, 2013, 41(1): 47-52.
Zhang Qian, Xi Weimin, Huang Junhui, et al. Probabilistic load flow evaluation considering system operation mode for transmission lines across the Yangzi river of Jiangsu power grid[J]. Power Systems Protection and Control, 2013, 41(1): 47-52.
[6] 文旭, 颜伟, 黄淼, 等. 计及绿色能源出力不确定性的输电网线损率概率评估[J]. 电力系统保护与控制, 2013, 41(1): 169-175.
Wen Xu, Yan Wei, Huang Miao, et al. Probabilistic transmission loss rate evaluation under green energy generation uncertainty[J]. Power Systems Protection and Control, 2013, 41(1): 169-175.
[7] 刘志刚, 刘欢, 柳杰. 计及风电场概率模型的多目标无功优化[J]. 电力系统保护与控制, 2013, 41(1): 197-203.
Liu Zhigang, Liu Huan, Liu Jie. Multi-objective reactive power optimization considering wind farm probabilistic model[J]. Power Systems Protection and Control, 2013, 41(1): 197-203.
[8] 王成山, 郑海峰, 谢莹华, 等. 计及分布式发电的配电系统随机潮流计算[J]. 电力系统自动化, 2005, 29(24): 39-44.
Wang Chengshan, Zheng Haifeng, Xie Yinghua, et al. Probabilistic power flow containing distribution generation in distribution system[J]. Automation of Electric Power Systems, 2005, 29(24): 39-44.
[9] Ruiz-Rodriguez F J, Hernandez J C, Jurado F. Probabilistic load flow for photovoltaic distributed generation using the cornish-fisher expansion[J]. Electric Power Systems Research, 2012, 89(1): 129-138.
[10] Ruiz-Rodriguez F J, Hernandez J C, Jurado F. Probabilistic load flow for radial distribution networks with photovoltaic generators[J]. IET Renewable Power Generation, 2012, 6(2): 110-121.
[11] 段玉兵, 龚宇雷, 谭兴国, 等. 基于蒙特卡罗模拟的微电网随机潮流计算方法[J]. 电工技术学报, 2011, 26(增刊1): 274-278.
Duan Yubing, Gong Yulei, Tan Xingguo, et al. Pro- babilistic power flow calculation in microgrid based on monte-carlo simulation[J]. Transactions of China Electrotechnical Society, 2011, 26(S1): 274-278.
[12] Conti S, Raiti S. Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators[J]. Solar Energy, 2007, 81(12): 1473-1481.
[13] Hollands K G T, Huget R G. A probability density function for the clearness index with applications[J], Solar Energy, 1983, 30(3): 195-209.
[14] Miao F, Vijay V, Gerald T H, et al. Probabilistic power flow studies for transmission systems with photovoltaic generation using cumulants[J]. IEEE Transactions on Power Systems, 2012, 27(4): 2251- 2261.
[15] 颜伟, 任洲洋, 赵霞, 等. 光伏电源输出功率的非参数核密度估计模型[J]. 电力系统自动化, 2013, 37(10): 35-40.
Yan Wei, Ren Zhouyang, Zhao Xia, et al. Probabilistic photovoltaic power modeling based on nonparametric kernel density estimation[J]. Automation of Electric Power Systems, 2013, 37(10): 35-40.
[16] 蔡德福, 陈金福, 石东源, 等. 风速相关性对配电网运行特性的影响[J]. 电网技术, 2013, 37(1): 150-155.
Cai Defu, Chen Jinfu, Shi Dongyuan, et al. Impact of wind speed correlation on operation characteristics of distribution network[J]. Power System Technology, 2013, 37(1): 150-155.
[17] 杨虎, 刘琼荪, 钟波. 数理统计[M]. 北京: 高等教育出版社, 2004.
[18] Karaki S H, Chedid R B, Ramadan R. Probabilistic performance assessment of autonomous solar-wind energy conversion systems[J]. IEEE Transactions on Energy Conversion, 1999, 14(3): 766-772.
[19] 赵渊, 沈智健, 周念成, 等. 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.
Zhao Yuan, Shen Zhijian, Zhou Niancheng, et al. Reliability assessment of bulk power systems utilizing sequential simulation and nonparametric Kernel density estimation[J]. Automation of Electric Power Systems, 2008, 32(6): 14-19.
[20] Liu P L, Der Kiureghian A. Multivariate distribution models with prescribed marginals and covariances[J]. Probabilistic Engineering Mechanics, 1986, 1(2): 105-112.
[21] 盛骤, 谢式千, 潘承毅. 概率论和数理统计[M]. 北京: 高等教育出版社, 2001.
[22] Qin Zhilong, Li Wenyuan, Xiong Xiaofu. Generation system reliability evaluation incorporating correlations of wind speeds with different distributions[J]. IEEE Transactions on Power Systems, 2013, 28(1): 551- 558.
[23] Yu H, Chung C Y, Wong K P, et al. Probabilistic load flow evaluation with hybrid latin hypercube sampling and Cholesky decomposition[J]. IEEE Transactions on Power Systems, 2009, 24(2): 661- 667.
[24] Baran M E, Wu F. Optimal capacitor placement on radial distribution systems[J]. IEEE Transactions on Power Delivery, 1989, 4(1): 725-734.
[25] 光伏电站小时输出功率数据. 网址: http://solardata.
uoregon. edu/SelectArchival.html.