Impact of wind speed correlation on optimal power flow

被引：0

作者：

机构：

[1] Grid Planning and Research Center, Yunnan Power Grid Corporation

[2] School of Electrical and Electronic Engineering, North China Electric Power University

[3] Postgraduate Workstation of Yunnan Power Gird Corporation, North China Electric Power University

[4] School of Control and Computer Engineering, North China Electric Power University

来源：

Pan, X. (panxiong2004@163.com) | 2013年 / Automation of Electric Power Systems Press卷 / 37期

关键词：

Correlation; Inverse Nataf transformation; Node price; Optimal power flow; Wind farms;

D O I：

10.7500/AEPS201203112

中图分类号：

学科分类号：

摘要：

There are always some different correlations between wind farms in a power grid. This paper uses inverse Nataf transformation to generate correlated wind speed samples to get the correlating wind farms' generations. Considering the uncertainty of wind speed, the actual production of each wind power plant is considered as a negative Weibull random distribution load and characterized by its historical time series data. The spatial correlations of wind power plants are properly modeled through stationary variance-covariance matrices. Wind farms are included in modified IEEE 30-bus and IEEE 118-bus systems. Monte Carlo simulation is adopted to quantitatively analyze how the indicators of optimal power flow results volatilities as a result of an increasing correlation among wind farms. © 2013 State Grid Electric Power Research Institute Press.

引用

页码：37 / 41

页数：4

共 16 条

[1]

Lei Y., Wang W., Yin Y., Et al., Wind power penetration limit calculation based on chance constrained programming, Proceedings of the CSEE, 22, 5, pp. 32-35, (2002)

[2]

Lei Y., Wang W., Yin Y., Et al., Optimal real power flow in wind power integrated system, Proceedings of the CSEE, 26, 6, pp. 18-21, (2002)

[3]

Chen J., Chen H., Duan X., Multi-period dynamic optimal power flow in wind power integrated system, Proceedings of the CSEE, 26, 3, pp. 31-35, (2006)

[4]

Morales J.M., Minguez R., Conejo A.J., A methodology to generate statistically dependent wind speed scenarios, Applied Energy, 87, 3, pp. 843-855, (2010)

[5]

Xiao C., Wang N., Zhi J., Et al., Power characteristics of Jiuquan wind power base, Automation of Electric Power Systems, 34, 17, pp. 64-67, (2010)

[6]

Yu D., Han X., Liang J., Et al., Study on the profiling of China's regional wind power fluctuation using GEOS-5 data assimilation system of national aeronautics and space administration of America, Automation of Electric Power Systems, 35, 5, pp. 77-80, (2011)

[7]

Fan R., Chen J., Duan X., Et al., Impact of wind speed correlation on probabilistic load flow, Automation of Electric Power Systems, 35, 4, pp. 18-22, (2011)

[8]

Morales J.M., Conejo A.J., Perez-Ruiz J., Simulating the impact of wind production on locational marginal prices, IEEE Trans on Power Systems, 26, 2, pp. 820-828, (2011)

[9]

Bowden G.J., Barker P.R., Shestopal V.O., Et al., The Weibull distribution function and wind power statistics, Wind Engineering, 7, 2, pp. 85-98, (1983)

[10]

Feijoo A.E., Cidras J., Modeling of wind farms in the load flow analysis, IEEE Trans on Power Systems, 15, 1, pp. 110-115, (2000)

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