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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
作者:
作者单位:

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

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基金项目:

国家重点研发计划资助项目(2017YFB0902600);国家自然科学基金资助项目(51777088);国家电网公司科技项目(SGJS0000DKJS1700840)。


Modelling of Wind Power Uncertainty Considering Heteroskedasticity Effect and Its Application in Power System Dispatching
Author:
Affiliation:

1.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;2.State Key Laboratory of Advanced Electromagnetic Engineering and Technology (Huazhong University of Science and Technology), Wuhan 430074, China;3.Key Laboratory of Electric Power Security and High Efficiency of Hubei Province (Huazhong University of Science and Technology), Wuhan 430074, China

Fund Project:

This work is supported by National Key R&D Program of China (No. 2017YFB0902600), National Natural Science Foundation of China (No. 51777088), and State Grid Corporation of China (No. SGJS0000DKJS1700840).

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    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 forecast error considering the heteroskedasticity effect. Firstly, the dependence of wind power forecast 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 temporal 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 forecast 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.

    表 7 Table 7
    表 3 动静态Copula的AIC分析结果Table 3 AIC analysis result of both static and dynamic Copula
    表 4 不同波动水平下预测误差均值Table 4 Mean value of forecast error at different fluctuation levels
    表 9 Table 9
    图1 本文所提模型的建模及参数估计流程Fig.1 Modelling and parameter estimation process of the proposed model
    图2 不同波动水平预测误差的分布情况Fig.2 Distribution of forecast errors at different fluctuation levels
    图3 预测出力、实际出力及不同置信度下的误差区间Fig.3 Forecast output, actual output and error intervals at different confidence levels
    图4 系统旋转备用Fig.4 Spinning reserve of system
    图5 不同场景下的弃风情况对比Fig.5 Comparison of wind abandonment in different scenarios
    图 修改版PJM-5节点系统Fig. Modified PJM-5-bus system
    图 118节点系统风电负荷曲线Fig. Wind power and load curve of 118 bus system
    图 118节点系统调度弃风情况Fig. Wind curtailment of 118 bus system
    表 1 风电预测误差与风电出力水平/风电波动量的秩相关系数Table 1 Rank correlation coefficient of wind power forecast error and wind power output level/wind power fluctuation
    表 6 滚动调度结果Table 6 Result of rolling dispatch
    表 2 风电预测误差与风电波动量在不同时间段内的相关性系数Table 2 Correlation coefficient between wind power forecast error and wind power fluctuation in different time periods
    表 5 单日日前调度结果Table 5 Result of day-ahead dispatch in a day
    表 8 Table 8
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引用本文

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/OL].电力系统自动化,http://doi.org/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/OL].Automation of Electric Power Systems,http://doi.org/10.7500/AEPS20190430016.

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  • 收稿日期:2019-04-30
  • 最后修改日期:2020-02-21
  • 录用日期:2019-10-18
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