Static equivalent of distribution network with distributed PV considering correlation between fluctuation of PV and load

With the grid connection of a large number of distributed photovoltaics (PVs), the structure and operation mode of the distribution network are changed. Detailed modeling of the distribution network can accurately analyze the impact of these changes on the power system but leads to high model complexity and large amounts of calculation. Equivalent of the distribution network effectively reduces the model scale, where the static equivalent is the basis for the other equivalents. Most of the existing static equivalent methods target a few typical operation modes. However, they are unsuitable for multiple variable scenarios caused by PV power fluctuation. This paper proposes a static equivalent method of the distribution network with distributed PVs to adapt to complex and changeable operation modes. Firstly, a scenario generation method of PV and load power based on kernel density estimation and a copula function is proposed considering fluctuation and correlation of PV and load. Secondly, a parameter optimization method based on particle swarm optimization (PSO) is proposed to optimize the parameters in the static equivalent model of the distribution network under a single operation mode. Thirdly, an equivalent parameter estimation model based on convolutional neural network (CNN) is proposed to improve the efficiency of model parameter calculation under multiple operation modes. The effectiveness of the proposed method is verified under an example of an actual distribution network in Shandong, China. This method has efficiency and is suitable for multiple operating modes.