Abstract
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 difficult 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.
摘要
分布式光伏的快速发展, 对电力系统的安全经济运行产生了显著影响. 由于地理分布广泛, 分布式光伏电站数量众多, 当前难以接入调度数据网络进行数据采集. 根据分布式光伏的时空特性, 本文提出了一种基于邻接矩阵自适应学习的图卷积算法, 用来估计区域电网中的分布式光伏的实时输出. 实际案例研究表明, 自适应图卷积模型对不同的光伏电站给出了不同的邻接矩阵, 可以获得更高的精度输出估计.
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Foundation item: the Science and Technology Program of State Grid Corporation of China (No. 5211TZ1900S6)
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Chen, L., Hong, D., He, X. et al. Distributed Photovoltaic Real-Time Output Estimation Based on Graph Convolutional Networks. J. Shanghai Jiaotong Univ. (Sci.) 29, 290–296 (2024). https://doi.org/10.1007/s12204-022-2522-6
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DOI: https://doi.org/10.1007/s12204-022-2522-6