In order to analyze the impact of large-scale photovoltaic system on the power system, a photovoltaic output prediction method considering the correlation is proposed and the optimal power flow is calculated. Firstly, establish a photovoltaic output model to obtain the attenuation coefficient and fluctuation amount, and analyze the correlation among the multiple photovoltaic power plants through the k-means method. Secondly, the long short-term memory (LSTM) neural network is used as the photovoltaic output prediction model, and the clustered photovoltaic output data is brought into the LSTM model to generate large-scale photovoltaic prediction results with the consideration of the spatial correlation. And an optimal power flow model that takes grid loss and voltage offset as targets is established. Finally, MATLAB is used to verify that the proposed large-scale photovoltaic forecasting method has higher accuracy. The multi-objective optimal power flow calculation is performed based on the NSGA-II algorithm and the modified IEEE systems, and the optimal power flow with photovoltaic output at different times is compared and analyzed.

为了分析大型光伏系统对电力系统的影响，提出了一种考虑相关性的光伏发电量预测方法，并计算了最优潮流。首先，建立光伏输出模型，获取衰减系数和波动量，并通过k均值法分析多个光伏电站之间的相关性。其次，将长短期记忆（LSTM）神经网络用作光伏输出预测模型，并考虑空间相关性，将聚类的光伏输出数据引入LSTM模型以生成大规模光伏预测结果。建立了以电网损耗和电压补偿为目标的最优潮流模型。最后，使用MATLAB验证了所提出的大规模光伏预测方法具有较高的准确性。基于NSGA-II算法和改进的IEEE系统进行了多目标最优潮流计算，并对不同时间光伏输出的最优潮流进行了比较和分析。