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基于空间相关性的风电功率预测研究综述
作者:
作者单位:

(中国农业大学信息与电气工程学院, 北京市 100083)

摘要:

由于风电具有很强的随机性和波动性,因此大规模风电并网会对电力系统的运行和稳定性造成很大的影响。如何准确预测区域风电场的功率已经成为当今电力系统亟待解决的研究课题。现有的风电功率预测方法未考虑空间相关因素,预测体系有待进一步完善。基于空间相关性的风电功率预测是一种考虑了本地信息和空间相关信息的综合预测方法。文中给出了基于空间相关性的风电功率预测的定义、概念和基本特点,分别从统计模型、物理模型、空间降尺度过程和空间升尺度过程4个方面详细阐述了基于空间相关性的风电功率预测的实现方法,并对空间相关性在风电功率预测方面应用的最新国内外研究进展作了系统的分析评述。最后,针对该领域尚存在的问题与不足,总结了今后的发展方向和需要进一步探索的研究内容。

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基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)

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A Review on Wind Power Prediction Based on Spatial Correlation Approach
Author:
Affiliation:

(College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China)

Abstract:

Large-scale integration of wind power into grid has significant impact on system operation and stability due to the inherent intermittency of wind power. Consequently, accurate prediction of regional wind farm output has become one of the vital challenges for the power industry. However, most of the existing prediction methods only involve mathematical models and rarely consider spatial correlation factors. As such, the prediction system remains to be further improved. Wind power prediction based on spatial correlation is a comprehensive prediction method that combines both local information and spatially correlated information. This paper introduces the definition, concepts and basic characteristics of the wind power prediction based on spatial correlation. Specific aspects such as statistical model, physical model, down-scaling and up-scaling procedure in terms of wind power prediction based on spatial correlation are also explained in detail. The state-of-the-art of spatial correlation approaches and techniques used in wind power prediction around the world are systematically reviewed and summarized. Finally, the future development trend and further investigations are presented for the existing problems and shortcomings in this field.

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引用本文
[1]叶林,赵永宁.基于空间相关性的风电功率预测研究综述[J].电力系统自动化,2014,38(14):126-135. DOI:10.7500/AEPS20130911004.
YE Lin, ZHAO Yongning. A Review on Wind Power Prediction Based on Spatial Correlation Approach[J]. Automation of Electric Power Systems, 2014, 38(14):126-135. DOI:10.7500/AEPS20130911004.
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  • 收稿日期:2013-09-11
  • 最后修改日期:2014-05-13
  • 录用日期:2014-02-20
  • 在线发布日期: 2014-05-06
  • 出版日期:
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