The uncertainty in the long-term growth of renewable energy generation output and load has heightened the complexity of power grid planning.Conducting an uncertainty analysis on the long-term scale of renewable energy output and load is of significant importance for the planning and construction of the power grid.To address this issue,a multi-stage scenario tree generation method for wind-solar load based on complex feature extraction and Sinkhorn distance was proposed.Firstly,to enhance the clustering efficiency of wind-solar load scenarios,a method based on stacked sparse autoencoders for feature extraction of wind-solar load scenarios was introduced.The feature set of wind-solar load scenarios was clustered by using an improved affinity propagation algorithm based on density peak,and typical curves of wind-solar load were obtained as the root nodes of the scenario tree.Subsequently,by considering different growth rates in load,a yearly generation of wind-solar load scenario trees was performed,and a scenario tree reduction method based on Sinkhorn distance was proposed to reduce the size of the scenario tree.Finally,a simulation example showed that the proposed method had high calculation efficiency,and the generated multi-stage scenario tree for wind-solar load can reflect the uncertainty of wind-solar output and load growth.
维普
万方数据