Ultra-short-term Prediction of Photovoltaic Power Generation Based on Digital Twins

被引：0

作者：

Sun R. ^{[1
]}

Wang L. ^{[2
]}

Wang Y. ^{[1
]}

Ding R. ^{[1
]}

Xu H. ^{[1
]}

Wang J. ^{[1
]}

Li Q. ^{[2
]}

机构：

[1] State Grid Jibei Electric Power Company, Xicheng District, Beijing

[2] School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan

来源：

Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 04期

关键词：

Digital twin; Photovoltaic power generation; Power prediction; Prediction accuracy;

D O I：

10.13335/j.1000-3673.pst.2020.0711

中图分类号：

学科分类号：

摘要：

Ultra-short-term prediction of photovoltaic power generation is of great significance to reduce the impact of grid-connected PV System on the power grid and maintain the safe operation of the power grid. In this paper, an ultra-short-term prediction mechanism of photovoltaic power generation based on digital twin is proposed. By constructing digital twins, the real-time and high-precision photovoltaic power prediction is carried out. Firstly, a virtual model of photovoltaic power prediction is established based on the GA-BP neural network, and the twin data of the photovoltaic cells and the surrounding environment are collected with the multi-dimensional sensors, and the historical database is updated. Then, based on the collected twin data, the preliminary prediction results are obtained. Finally, through the similar weather condition searching, the actual power value and the predicted power values at those times under the similar conditions are found, and then the preliminary prediction results are modified to obtain the final predicted power. Experimental results show that the proposed mechanism can effectively improve the ultra-short-term prediction accuracy of the photovoltaic power output. © 2021, Power System Technology Press. All right reserved.

引用

页码：1258 / 1264

页数：6

共 25 条

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