Liudong Zhang ^1* Zhen Lei ¹ Zhigang Ye ² Zhiqiang Peng ²

• ¹ Gird Dispatch Control Center, State Grid Jiangsu Electric Power Company, Nanjing, China, State Grid Jiangsu Electric Power Co., LTD, Nanjing, Jiangsu Province, China
• ² State Grid Jiangsu Electric Power Research Institute, Nanjing, China, State Grid Jiangsu Electric Power Co., LTD, Nanjing, Jiangsu Province, China

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, and thus the assessment of distributed PV carrying capacity is of great significance for distribution network planning. Therefore, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson's correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system, the simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale.