TPA改进GCN-LSTM的光伏电站群调群控优化策略研究OA

As the proportion of installed photovoltaic(PV)capacity increases year by year,accurate prediction of PV output and the realization of group control and management of PV clusters become crucial.A multi-site PV output prediction method that integrates deep fusion of graph convolutional neural network(GCN),long short-term memory(LSTM),and temporal pattern attention(TPA)was proposed.First,the input features of multi-site PV output time series curves and numerical weather forecast data were transformed into a graph structure to establish a GCN-LSTM model,which extracts the hidden spatio-temporal dependencies among PV clusters.Second,an attention mechanism was introduced to weight and correct input data features,enhancing the value of key data.Then,based on the spatio-temporal prediction results of the PV clusters,dominant nodes that sensitively reflect the voltage changes of the cluster were selected,and an inter-cluster coordinated optimization strategy was constructed with the goals of ensuring no voltage limit violations in the entire regional nodes and minimizing the system's network losses.Following that,an intra-cluster autonomous optimization and control strategy was constructed within the cluster based on the coordinated optimization strategy results,aiming for the safe operation of cluster node voltages,minimum cluster network losses,and maximum local consumption of distributed PVs.Finally,simulation results of actual multi-site PV cluster output data show that the proposed method can efficiently extract the spatio-temporal correlations between different PV stations,reduce the prediction error of PV output,and effectively improve the safety and economy of PV clusters.