Distributed Photovoltaic Real-Time Output Estimation Based on Graph Convolutional Networks

作     者：陈利跃 洪道鉴 何星 卢东祁 张乾 谢妮娜 徐一洲 应煌浩 CHEN Liyue;HONG Daojian;HE Xing;LU Dongqi;ZHANG Qian;XIE Nina;XU Yizhou;YING Huanghao

作者机构：State Grid Zhejiang Electric Power Co.Ltd.Hangzhou 310007China State Grid Zhejiang Taizhou Power Supply CompanyTaizhou 318000ZhejiangChina Department of AutomationSchool of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2024年第29卷第2期

页      面：290-296页

核心收录：

学科分类：080703[工学-动力机械及工程] 08[工学] 081203[工学-计算机应用技术] 0807[工学-动力工程及工程热物理] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Science and Technology Program of State Grid Corporation of China(No.5211TZ1900S6)。 

主　　题：distributed photovoltaic(PV) graph convolution network power estimation 

摘      要：The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.