计及风速-电锅炉等电采暖负荷相关性的配电网可靠性评估

李娟; 周红莲; 周二彪; 刘自发; 王威

doi:10.16081/j.issn.1006-6047.2018.10.005

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计及风速-电锅炉等电采暖负荷相关性的配电网可靠性评估

智能配电系统专题 | 更新时间：2025-04-25

- 计及风速-电锅炉等电采暖负荷相关性的配电网可靠性评估
- Reliability evaluation of distribution network considering correlation between wind speed and electricity heating load such as electricity boiler
- 电力自动化设备 2018年38卷第10期页码：26-31
- 作者机构：
  
  1. 国网新疆电力公司经济技术研究院
  2. 华北电力大学电气与电子工程学院
  3. 山东科技大学机电工程系
- 作者简介：
- 基金信息：
  
  国家电网公司科技项目（5230JY16000U）~~
- DOI：10.16081/j.issn.1006-6047.2018.10.005
  中图分类号： TM732
- 纸质出版：2018
- 稿件说明：
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[1]李娟,周红莲,周二彪,刘自发,王威.计及风速-电锅炉等电采暖负荷相关性的配电网可靠性评估[J].电力自动化设备,2018,38(10):26-31.

LI Juan, ZHOU Honglian, ZHOU Erbiao, et al. Reliability evaluation of distribution network considering correlation between wind speed and electricity heating load such as electricity boiler[J]. 2018, 38(10): 26-31.
[1]李娟,周红莲,周二彪,刘自发,王威.计及风速-电锅炉等电采暖负荷相关性的配电网可靠性评估[J].电力自动化设备,2018,38(10):26-31. DOI： 10.16081/j.issn.1006-6047.2018.10.005.

LI Juan, ZHOU Honglian, ZHOU Erbiao, et al. Reliability evaluation of distribution network considering correlation between wind speed and electricity heating load such as electricity boiler[J]. 2018, 38(10): 26-31. DOI： 10.16081/j.issn.1006-6047.2018.10.005.

摘要

风力发电和电锅炉等电采暖负荷接入配电网的可靠性评估过程中

风速-负荷联合二元正态分布函数不能够反映风速-电锅炉等电采暖负荷之间的相关性

从而影响了计算的准确性。基于Copula理论

建立风速-电锅炉等电采暖负荷相关性的Gumbel-Copula函数关系

通过极大似然估计确定Copula函数中的具体参数

得到其联合概率密度分布函数

并利用蒙特卡洛模拟法计算配电网可靠性指标。通过算例分析结果表明

Gumbel-Copula函数能够较好地反映风速和电锅炉等电采暖负荷之间的相关性

基于所提模型可有效、准确地计算风电和电采暖负荷接入配电网的可靠性。

Abstract

In the reliability evaluation process of the distribution network connected with wind power and electricity heating load such as electricity boiler

the joint bivariate normal distribution function of wind speed-load cannot accurately describe the correlation between wind speed and electricity heating load

which decreases the calculation accuracy. The Gumbel-Copula function of the correlation between wind speed and electricity heating load is established based on Copula theory

and the parameters in Copula function are determined by the maximum likelihood estimate method. Consequently

the joint probability density distribution function can be obtained

and the reliability indices of distribution network are calculated by using Monte Carlo simulation method. Case study results show that the Gumbel-Copula function can well describe the correlation between wind speed and electricity heating load

which contributes to the effective and accurate calculation of distribution network reliability.

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