[1]栾开宁,庄 重,杨世海,等.基于优化小波变换神经网络的分布式新能源信息预测方法[J].南京师范大学学报(工程技术版),2024,24(02):011-19.[doi:10.3969/j.issn.1672-1292.2024.02.002]
　Luan Kaining,Zhuang Zhong,Yang Shihai,et al.Optimized Wavelet Transform Neural Networks for Accurat Distributed Renewable Energy Information Prediction[J].Journal of Nanjing Normal University(Engineering and Technology),2024,24(02):011-19.[doi:10.3969/j.issn.1672-1292.2024.02.002]
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基于优化小波变换神经网络的分布式新能源信息预测方法
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
24卷
期数:
2024年02期
页码:
011-19
栏目:
电气工程
出版日期:
2024-06-15

文章信息/Info

Title:
Optimized Wavelet Transform Neural Networks for Accurat Distributed Renewable Energy Information Prediction
文章编号:
1672-1292(2024)02-0011-09
作者:
栾开宁1; 庄 重2; 杨世海2; 段梅梅2; 孔月萍2; 周雨奇2; 张汀荃2; 丁泽诚2
(1.国网江苏省电力有限公司,江苏 南京 210019)
(2.国网江苏省电力有限公司营销服务中心,江苏 南京 210019)
Author(s):
Luan Kaining1; Zhuang Zhong2; Yang Shihai2; Duan Meimei2; Kong Yueping2; Zhou Yuqi2; Zhang Tingquan2; Ding Zecheng2
(1.State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210019,China)
(2.Marketing Service Center of State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210019,China)
关键词:
分布式新能源; 负荷预测; 辐照强度预测; 城市电网; 小波变换神经网络
Keywords:
distributed renewable energy; load prediction; irradiation intensity prediction; urban power grid; wavelet transform neural network
分类号:
TM615
DOI:
10.3969/j.issn.1672-1292.2024.02.002
文献标志码:
A
摘要:
分布式新能源发电是低碳化电力系统中重要的一部分. 随着分布式新能源在城市电网中的占比逐渐增加,负荷随机波动和天气随机变化对于城市电网的影响日益增强,对分布式新能源信息的预测准确性提出了更高的要求. 目前,分布式新能源的主要发电方式是分布式光伏发电以及分布式风力发电. 城市用电负荷的变化兼具周期性和随机性,而风速和辐照强度等因素分别对于分布式风力发电和分布式光伏发电有重要影响. 为了准确预测出分布式新能源的信息,构建了基于小波变换神经网络的分布式新能源信息预测方法. 首先,通过分析分布式新能源的工作原理,建立分布式新能源的模型; 然后,优化小波变换神经网络,以风力发电和光伏发电为例对负荷用电功率和辐照强度等对电网作用显著的参数进行预测; 最后,算例验证模型对分布式新能源信息进行预测的准确性.
Abstract:
Distributed renewable energy generation is a crucial component of low-carbon power systems. As the proportion of distributed renewable energy in urban power grids is gradually increasing,and the impacts of random load fluctuations and random weather changes on urban power grids are increasing,placing higher demands on the accuracy of distributed renewable energy information forecasting. Currently,the primary generation methods of distributed renewable energy are distributed photovoltaic power generation and distributed wind power generation. The changes of urban electricity load are both cyclical and random,while factors such as wind speed and solar irradiance have significant impacts on distributed wind power generation and distributed photovoltaic power generation,respectively. Therefore,based on wavelet transform neural network,a distributed renewable energy information prediction method is constructed. Firstly,the model of distributed renewable energy is established by analyzing the working principle of distributed renewable energy. Then,the wavelet transform neural network is optimized to predict the parameters that play a significant role in the renewable energy grid,such as the load power change and the irradiation intensity,using wind power generation and photovoltaic power generation as examples. Finally,the example verifies that the proposed model can accurately predict the information of distributed renewable energy.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2024-04-10.
基金项目:国家电网有限公司科技项目(J2022045).
通讯作者:栾开宁,博士,高级工程师,研究方向:电力负荷预测与调控、智能用电. E-mail:luankaining2000@163.com
更新日期/Last Update: 2024-06-15