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

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.