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

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

陈晨^{1, 2},
袁绍军¹,
尹兆磊^1, ,,
贺晓红¹,
杨慢慢¹,
李润鑫¹

1.
国网冀北电力有限公司承德供电公司承德 067000
2.
华北电力大学能源动力与机械工程学院保定 071066

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
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- 被引次数: 0
出版历程
- 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

A Fluctuation Quantitative Evaluation Method for Distributed Energy Power Time Series

1.
State Grid Jibei Electric Power Company Limited, Chengde 067000, China
2.
School of Energy Power Mechanical Engineering, North China Electric Power University, Baoding 071066, China

摘要

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。
- 分布式发电 /
- 时间序列 /
- 波动性 /
- 包络线 /
- 勒贝格积分
Abstract: Future smart grid will incorporate an increasing number of Distributed Energy Resources (DERs), which have the potential to enhance the system energy efficiency, economics, resilience, and sustainability. However, The DERs, dominated by wind power and photovoltaic power generation, would lead to many problems for a power system with large-scale DERs integrated due to their inherent fluctuation characteristic. Therefore, quantitatively evaluating the fluctuation level of the DERs’ power is of great importance for modern power system. To this end, by defining time window, using envelope and Lebesgue integration theory, the fluctuation quantitative index of DERs’ power—fluctuation rate—is defined by extracting the fluctuations of high frequency information and trends of the DER’s output power time series. The validity of the fluctuation rate for measuring the fluctuation of DERs’ power is validated by testing the fluctuation, smoothing effects of wind power and conducting comparative analysis with prediction error and existing fluctuation index.
- Distributed energy /
- Time series /
- Fluctuation /
- Envelope /
- Lebesgue integration

HTML全文

图 1 不同时间序列及其上下包络线

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图 2 波动性不同的风电功率时间序列

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图 3 风电功率时间序列a和b在不同l选取下r的变化

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图 4 风力机汇聚出力下的r

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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e和f

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表 1 序列a和b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(%)
a	0.0061	0.0105	0.0019	0.6414	15.41	12.23
b	0.0373	0.0462	0.0085	1.6183	22.62	17.48

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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

	c	d
本文r值	0.0342	0.0095
文献[17] r值	0.0383	0.0144
文献[18] r值	0.0078	0.0023
文献[19] r值	1.5372	0.8590
持续预测法RMSE(%)	21.32	16.58
ARMA预测法RMSE(%)	16.86	14.17
文献[13]预测法RMSE(%)	11.50	8.76
文献[25]预测法RMSE(%)	15.43	9.89
文献[26]预测法RMSE(%)	12.71	8.84

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表 3 两个风电功率时间序列的不同波动性衡量指标对比

	e	f
本文${r_1}$值	0.0404	0.0397
本文${r_2}$值	0.0270	0.0601
本文r值	0.0305	0.0499
文献[17] r值	0.0324	0.0308
文献[18] r值	0.0074	0.0065
持续预测法RMSE(%)	20.41	22.33
ARMA预测法RMSE(%)	14.28	18.94
文献[13]预测法RMSE(%)	10.07	13.04
文献[25]预测法RMSE(%)	14.13	16.33
文献[26]预测法RMSE(%)	11.77	14.90

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