As an emerging power system form, the power output characteristics of optical storage-charging integrated microgrids are characterized by significant intermittency and stochasticity, which bring new challenges to the safe and stable operation of distribution grids. Aiming at the problem of evaluating the impact of power fluctuation of optical storage-charging microgrids on the reliability of distribution grids, this paper constructs a full-time simulation and analysis model based on time series data and a hybrid CNN-GRU neural network prediction model. By analyzing different fault scenarios, a microgrid reliability assessment index system is established, and the method of extracting spatial features by convolutional neural network and capturing timing features by gated recurrent unit is applied to realize accurate prediction of PV power. The case analysis based on the IEEE RBTS test system shows that the SAIFI index of the distribution system decreases from 1.6354 to 1.4795 after microgrid access, and the power supply availability rate increases from 0.99936 to 0.99947, which significantly improves the system reliability. The prediction accuracies of the CNN-GRU model are better than those of the traditional methods in all four seasons, and the NMAI indexes of the CNN-GRU model are better than those of the traditional methods, and the NMAI indexes are better than the traditional methods in all four seasons. Attention model with a maximum reduction of 59.2% in NMAE metrics and 45.4% in NRMSE metrics. The results verify the effectiveness of the proposed model in microgrid output fluctuation assessment and prediction, and provide theoretical support for distribution network planning and operation.
