考虑异方差效应的风电不确定性建模及其在调度中的应用

李力行; 苗世洪; 涂青宇; 李姚旺; 李超; 段偲默

doi:10.7500/AEPS20190430016

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考虑异方差效应的风电不确定性建模及其在调度中的应用

学术研究 | 更新时间：2025-06-16

- 考虑异方差效应的风电不确定性建模及其在调度中的应用
- Modelling of Wind Power Uncertainty Considering Heteroskedasticity Effect and Its Application in Power System Dispatching
- 电力系统自动化-方正平台 2020年44卷第8期页码：36-47
- 作者机构：
  
  1.华中科技大学电气与电子工程学院，湖北省武汉市 430074
  2.强电磁工程与新技术国家重点实验室（华中科技大学），湖北省武汉市 430074
  3.电力安全与高效湖北省重点实验室（华中科技大学），湖北省武汉市 430074
- 作者简介：
  
  李力行(1992—)，男，博士研究生，主要研究方向：电力系统不确定性分析、大电网不确定性调度及电力市场多主体博弈。E-mail：lilixing123123@126.com
  苗世洪(1963—)，男，通信作者，博士，教授，主要研究方向：电力系统继电保护与运行控制、大电网协同调度。E-mail：shmiao@hust.edu.cn
  涂青宇（1994—），男，博士研究生，主要研究方向：考虑不确定性的电力系统源荷双侧建模。E-mail：tuqy_d@hust.edu.cn
- 基金信息：
  
  国家重点研发计划资助项目(2017YFB0902600);国家电网公司科技项目(SGJS0000DKJS1700840);国家自然科学基金资助项目(51777088)
- DOI：10.7500/AEPS20190430016
  中图分类号：
- 收稿日期：2019-04-30，
  
  修回日期：2019-10-18，
  
  网络出版日期：2020-03-05，
  
  纸质出版日期：2020-04-25
- 稿件说明：
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李力行,苗世洪,涂青宇等.考虑异方差效应的风电不确定性建模及其在调度中的应用[J].电力系统自动化,2020,44(8):36-47.

LI Lixing,MIAO Shihong,TU Qingyu,et al.Modelling of Wind Power Uncertainty Considering Heteroskedasticity Effect and Its Application in Power System Dispatching[J].Automation of Electric Power Systems,2020,44(8):36-47.
李力行,苗世洪,涂青宇等.考虑异方差效应的风电不确定性建模及其在调度中的应用[J].电力系统自动化,2020,44(8):36-47. DOI： 10.7500/AEPS20190430016.

LI Lixing,MIAO Shihong,TU Qingyu,et al.Modelling of Wind Power Uncertainty Considering Heteroskedasticity Effect and Its Application in Power System Dispatching[J].Automation of Electric Power Systems,2020,44(8):36-47. DOI： 10.7500/AEPS20190430016.

摘要

随着风电在电力系统中渗透率的不断提升，其不确定性为电网的安全经济运行带来了重大挑战。为获得精准的风电不确定性模型，帮助运行人员实现系统的安全经济运行，文中提出了考虑异方差效应的风电预测误差条件概率分布建模方法。首先，分析了风电预测误差与各类因素的相依性水平，并基于分析结果与动态Copula理论，建立了风电波动性与风电预测误差的动态相依性模型；之后，针对边缘分布所显示出的时域特征，结合差分整合移动平均自回归（ARIMA）模型与广义自回归条件异方差（GARCH）模型，考虑异方差效应，建立了时变边缘分布模型；最后，将两模型相结合，给出了不同波动水平下的风电条件预测误差分布情况，并在不确定性机组组合模型中进行验证，证明了模型的有效性。

Abstract

As the penetration rate of wind power in the power system continues to increase

its uncertainty poses a great challenge to the safe and economic operation of the power system. In order to obtain accurate wind power uncertainty model and help operators to achieve safe and economic operation of the system

this paper proposes a conditional probability distribution modelling method of wind power forecasting error considering the heteroskedasticity effect. Firstly

the dependence of wind power forecasting error and various factors is analyzed. Based on the results and the dynamic Copula theory

a dynamic dependence model of wind power forecast error is established. Then

based on the time-domain features displayed by the edge distribution

combined with the autoregressive integrated moving average (ARIMA) model and the generalized autoregressive conditional heteroskedasticity (GARCH) model

this paper develops a time-varying edge distribution model with the consideration of heteroskedasticity effect. Finally

the two models are combined to give the forecasting error distribution of wind power conditions at different fluctuation levels

and the verification is performed in the uncertain unit combination model

which proves the validity of the model.

关键词

Keywords

references

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