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一种分布式发电功率时间序列波动性量化评估方法

陈晨 袁绍军 尹兆磊 贺晓红 杨慢慢 李润鑫

陈晨, 袁绍军, 尹兆磊, 贺晓红, 杨慢慢, 李润鑫. 一种分布式发电功率时间序列波动性量化评估方法[J]. 电子与信息学报, 2022, 44(11): 3825-3832. doi: 10.11999/JEIT220096
引用本文: 陈晨, 袁绍军, 尹兆磊, 贺晓红, 杨慢慢, 李润鑫. 一种分布式发电功率时间序列波动性量化评估方法[J]. 电子与信息学报, 2022, 44(11): 3825-3832. doi: 10.11999/JEIT220096
CHEN Chen, YUAN Shaojun, YIN Zhaolei, HE Xiaohong, YANG Manman, LI Runxin. A Fluctuation Quantitative Evaluation Method for Distributed Energy Power Time Series[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3825-3832. doi: 10.11999/JEIT220096
Citation: CHEN Chen, YUAN Shaojun, YIN Zhaolei, HE Xiaohong, YANG Manman, LI Runxin. A Fluctuation Quantitative Evaluation Method for Distributed Energy Power Time Series[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3825-3832. doi: 10.11999/JEIT220096

一种分布式发电功率时间序列波动性量化评估方法

doi: 10.11999/JEIT220096
详细信息
    作者简介:

    陈晨:男,工程师,研究方向为电网运行方式及分布式电源管理

    袁绍军:男,高级工程师,研究方向为电网调度及新能源运行管理

    尹兆磊:男,工程师,研究方向为电网运行方式及新能源管理

    贺晓红:女,高级工程师,研究方向为电网运行

    杨慢慢:女,高级工程师,研究方向为无功电压管理

    李润鑫:男,高级工程师,研究方向为电网调度运行

    通讯作者:

    尹兆磊 cdgrid@163.com

  • 中图分类号: TM614

A Fluctuation Quantitative Evaluation Method for Distributed Energy Power Time Series

  • 摘要: 未来智能电网将接纳越来越多的分布式能源,而分布式能源的广泛接入具有提高系统的能源效率、经济性、韧性以及可持续性的潜力。然而,以风力发电和光伏发电为主的分布式能源由于其固有的波动特性,在大规模接入电网时会给系统带来诸多问题。因此,定量刻画分布式发电功率的波动性对于现代电力系统而言至关重要。基于此,该文借助时间窗、包络线和勒贝格积分,通过提取分布式发电功率中高频信息和变化趋势的波动性特征,定义了量化分布式发电功率波动性的指标——波动率。通过检验风电功率时间序列的波动性、验证平滑效应以及与预测误差和已有指标进行对比分析,验证了所提出的波动率在衡量分布式发电功率波动性的有效性。
  • 图  1  不同时间序列及其上下包络线

    图  2  波动性不同的风电功率时间序列

    图  3  风电功率时间序列ab在不同l选取下r的变化

    图  4  风力机汇聚出力下的r

    图  5  高频波动基本相同但变化趋势波动不同的风电功率时间序列ef

    表  1  序列ab的不同波动性衡量指标值和预测RMES

    序列波动率r文献[17]指标文献[18]指标文献[19]指标持续预测法(%)ARMA预测法(%)
    a0.00610.01050.00190.641415.4112.23
    b0.03730.04620.00851.618322.6217.48
    下载: 导出CSV

    表  2  不同装机容量和时间长度下风电功率时间序列的波动性对比验证

    cd
    本文r0.03420.0095
    文献[17] r0.03830.0144
    文献[18] r0.00780.0023
    文献[19] r1.53720.8590
    持续预测法RMSE(%)21.3216.58
    ARMA预测法RMSE(%)16.8614.17
    文献[13]预测法RMSE(%)11.508.76
    文献[25]预测法RMSE(%)15.439.89
    文献[26]预测法RMSE(%)12.718.84
    下载: 导出CSV

    表  3  两个风电功率时间序列的不同波动性衡量指标对比

    ef
    本文${r_1}$值0.04040.0397
    本文${r_2}$值0.02700.0601
    本文r0.03050.0499
    文献[17] r0.03240.0308
    文献[18] r0.00740.0065
    持续预测法RMSE(%)20.4122.33
    ARMA预测法RMSE(%)14.2818.94
    文献[13]预测法RMSE(%)10.0713.04
    文献[25]预测法RMSE(%)14.1316.33
    文献[26]预测法RMSE(%)11.7714.90
    下载: 导出CSV
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  • 收稿日期:  2022-01-24
  • 修回日期:  2022-03-17
  • 网络出版日期:  2022-06-25
  • 刊出日期:  2022-11-14

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