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基于天气分型的风光出力互补性分析方法
作者:
作者单位:

1.新能源电力系统国家重点实验室,华北电力大学,北京市 102206;2.华北电力大学新能源学院,北京市 102206;3.中国电力科学研究院有限公司,新能源与储能运行控制国家重点实验室,北京市 100192;4.国网宁夏电力有限公司,宁夏回族自治区银川市 750001

摘要:

基于天气分型的风光出力互补性定量分析方法能够科学指导风光互补发电系统优化调度。针对现有天气分型方法中主成分分析法无法提取非线性特征,分布领域嵌入(t-SNE)算法未考虑样本实际分布等不足,提出了基于核主成分分析(KPCA)和自组织特征映射(SOFM)神经网络的天气分型及风光出力互补性分析方法。首先,基于数值天气预报数据,利用KPCA进行特征向量提取;然后,以特征向量为输入条件,构建基于SOFM神经网络的天气类型划分模型;最后,基于波动互补率和爬坡互补率评估指标,从波动性和爬坡性2个角度定量分析不同天气类型下风光出力互补程度和最佳并网容量比例。结果表明不同天气类型下风光出力波动互补性及最佳并网容量比例差异明显,验证了所提方法的有效性。

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基金项目:

国家电网公司科技项目“计及出力时序波动特性的新能源纳入中长期电力电量平衡技术研究”(4000-201955194A-0-0-00)。

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作者简介:


Analysis Method for Complementarity Between Wind and Photovoltaic Power Outputs Based on Weather Classification
Author:
Affiliation:

1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;2.School of New Energy, North China Electric Power University, Beijing 102206, China;3.China Electric Power Research Institute Co., Ltd., State Key Laboratory of Operation and Control of Renewable Energy&Storage Systems, Beijing 100192, China;4.State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750001, China

Abstract:

The quantitative analysis method of the complementarity between wind power output and photovoltaic power output based on weather classification can scientifically guide the optimal dispatch of wind-photovoltaic complementary power generation system. In order to overcome the shortcomings of the existing weather classification methods that principal component analysis cannot extract nonlinear features, and t-SNE based algorithm does not consider the actual distribution of samples, a weather classification and complementarity analysis method for wind and photovoltaic power output based on kernel principal component analysis (KPCA) and self-organizing feature map (SOFM) neural network is proposed. Firstly, the KPCA is employed to extract the feature vectors based on numerical weather prediction data. Then, a weather pattern classification model based on SOFM neural network is constructed by using the feature vectors as input conditions. Finally, Based on the evaluation indicators for complementary rate of fluctuation and complementary rate of ramp, the complementary degree and the optimal grid-connected capacity ratio of wind and photovoltaic power output under different weather patterns are quantitatively analyzed from two perspectives of flucaturation and ramp. The results demonstrate that the fluctuation complementarity and the optimal grid-connected capacity ratio of wind and photovoltaic power output have obvious difference under different weather patterns, which verified the effectiveness of the proposed method.

Keywords:

Foundation:
This work is supported by State Grid Corporation of China "Research on Technology of Medium- and Long-term Power Balance Considering Fluctuation Characteristics of Renewable Energy" (No. 4000-201955194A-0-0-00).
引用本文
[1]乔延辉,韩爽,许彦平,等.基于天气分型的风光出力互补性分析方法[J/OL].电力系统自动化,http://doi. org/10.7500/AEPS20200812006.
QIAO Yanhui, HAN Shuang, XU Yanping, et al. Analysis Method for Complementarity Between Wind and Photovoltaic Power Outputs Based on Weather Classification[J/OL]. Automation of Electric Power Systems, http://doi. org/10.7500/AEPS20200812006.
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  • 收稿日期:2020-08-12
  • 最后修改日期:2020-11-11
  • 录用日期:2020-11-04
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