| 引用本文: | 杜刚,赵冬梅,刘鑫,吴志强,李超.基于重尾分布的风电功率波动特性概率分布[J].电力自动化设备,2021,41(7): |
| DU Gang,ZHAO Dongmei,LIU Xin,WU Zhiqiang,LI Chao.Probability distribution of wind power fluctuation characteristics based on heavy-tailed distribution[J].Electric Power Automation Equipment,2021,41(7): |
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| 摘要: |
| 研究风电功率波动特性对于提高风电功率预测精度、促进风电并网消纳、抑制风电并网对电力系统安全运行的不利影响等均具有重要意义。利用风电场实测数据，归纳风电功率波动特性的时变性、异方差性、波动集聚性和“尖峰厚尾”4个基本特征；为定量描述风电功率概率分布，在不同的时空尺度下分别采用正态分布、混合高斯分布以及重尾分布中的t Location-scale分布、稳定分布、拉普拉斯分布对风电功率波动率进行拟合，引入相对熵作为衡量拟合分布优劣的评价指标，并对比分析不同拟合分布的评价结果。仿真结果表明风电功率概率分布更适合采用重尾分布函数来描述，且重尾分布中的t Location-scale分布函数具有最佳的拟合效果。 |
| 关键词: 风电功率 波动特性 概率分布 尖峰厚尾 信息熵 相对熵 |
| DOI：10.16081/j.epae.202104003 |
| 分类号:TM614 |
| 基金项目: |
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| Probability distribution of wind power fluctuation characteristics based on heavy-tailed distribution |
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DU Gang1, ZHAO Dongmei1, LIU Xin2, WU Zhiqiang2, LI Chao3
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1.School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China;2.Changchun Power Supply Company of State Grid Jilin Electric Power Co.,Ltd.,Changchun 130000, China;3.Training Center of State Grid Jilin Electric Power Co.,Ltd.,Changchun 130000, China
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| Abstract: |
| The research of wind power fluctuation characteristics is of great significance for improving the accuracy of wind power prediction, promoting the consumption of wind power integration, and restraining the adverse effects of wind power integration on safe operation of power system. Based on the measured data of wind farms, four basic characteristics of wind power fluctuation characteristics of time-varying, heteroscedasticity, fluctuation agglomeration, and “leptokurtosis and fat-tail” are summarized. In order to quantitatively describe the probability distribution of wind power, the normal distribution, mixed Gaussian distribution and t Location-scale distribution, stable distribution and Laplacian distribution in the heavy-tailed distribution are respectively adopted to fit the wind power fluctuation rate under different time and space scales. The relative entropy is introduced as an evaluation index to measure the pros and cons of fitting distributions, and the evaluation results of different fitting distributions are compared and analyzed. The simulative results show that the probability distribution of wind power is more suitable to be described by the heavy-tailed distribution function, in which the t Location-scale distribution function has the best fitting effect. |
| Key words: wind power fluctuation characteristics probability distribution leptokurtosis and fat-tail information entropy relative entropy |
