A wind speed modeling method for multiple wind farms considering variation regularities

被引：0
|
作者：
Wu F. [1 ]
Kong W. [1 ]
Zhou Y. [2 ]
Huang J. [3 ]
Qiao L. [3 ]
机构：
[1] College of Energy and Electrical Engineering, Hohai University, Nanjing, 211100, Jiangsu Province
[2] Suzhou Power Supply Company, Suzhou, 215004, Jiangsu Province
[3] Economic Research Institute, Jiangsu Electric Power Company, Nanjing, 210098, Jiangsu Province
来源：
| 1600年 / Power System Technology Press卷 / 40期
基金：
中国国家自然科学基金;
关键词：
Correlation; Fluctuation characteristics; Multiple wind farms; Time-shifting; Wind speed;
D O I：
10.13335/j.1000-3673.pst.2016.07.016
中图分类号：
学科分类号：
摘要：
It is important to deal with wind power integrated in power system by simulating wind speed data considering variation regularities. Wind speed data should adapt not only to probability distribution and fluctuation characteristics of single-wind farm, but also to correlation of multiple wind farms. In this paper a wind speed modeling method is proposed considering these characteristics. Firstly, time shifting technique and Copula function are used to analyze and produce multiple wind speed sequences. Secondly, the sequences are adjusted with stochastic partial differential equation and time-shifting technique. Results show that simulated wind speed sequences can reflect regularities of their own and nearby wind farms. Applying the proposed model to IEEE-RBTS test system, efficiency and applicability of this modeling method are validated with simulation results. © 2016, Power System Technology Press. All right reserved.
引用
收藏
页码：2038 / 2044
页数：6
相关论文
共 16 条
  • [1] Wan Y., Milligan M., Parsons B., Output power correlation between adjacent wind power plants, Journal of Solar Energy Engineering, 125, 4, pp. 551-555, (2003)
  • [2] Philippopoulos K., Deligiorgi D., Statistical simulation of wind speed in Athens Greece based on Weibull and ARMA models, International Journal of Energy and Environment, 4, 3, pp. 151-158, (2009)
  • [3] Doquet M., Use of a stochastic process to sample wind power curves in planning studies, Proceedings of IEEE Power Tech, pp. 663-670, (2007)
  • [4] Zhang H., Yin Y., Shen H., Et al., A wind speed time series modelling method based on probability measure transformation, Automation of Electric Power Systems, 37, 2, (2013)
  • [5] Bechrakis D.A., Sparis P.D., Correlation of wind speed between neighboring measuring stations, IEEE Transactions on Energy Conversion, 19, 2, pp. 400-406, (2004)
  • [6] Xie K., Billinton R., Considering wind speed correlation of WECS in reliability evaluation using the time-shifting technique, Electric Power Systems Research, 79, 4, pp. 687-693, (2009)
  • [7] Wang S., Yu J., Li H., Et al., A wind speed modelling for multiple wind farms considering correlation and statistical characteristics, Automation of Electric Power System, 37, 6, (2013)
  • [8] Li J., Wen J., Cheng S., Et al., A scene generation method considering copula correlation relationship of multi-wind farm power, Proceedings of the CSEE, 33, 16, pp. 30-36, (2013)
  • [9] Papaefthymiou G., Kurowicka D., Using copulas for modelling stochastic dependence in power system uncertainty analysis, IEEE Transactions on Power Systems, 24, 1, pp. 40-49, (2009)
  • [10] Cai F., Yan Z., Zhao J., Et al., Dependence structure models for wind speed and wind power among different wind farms based on copula theory, Automation of Electric Power Systems, 37, 17, pp. 9-16, (2013)
12