1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;2.School of New Energy, North China Electric Power University, Beijing 102206, China;3.State Key Laboratory of Operation and Control of Renewable Energy＆Storage Systems (China Electric Power Research Institute Co., Ltd.), Beijing 100192, China;4.State Grid Ningxia Electric Power Co., Ltd., Yinchuan 750001, China
The quantitative analysis method of the complementarity between wind power output and photovoltaic power output based on weather classification can scientifically guide the optimal dispatch of wind-photovoltaic complementary power generation systems. In order to overcome the shortcomings of the existing weather classification methods that principal component analysis cannot extract nonlinear features and t-distributed stochastic neighbor embedding (t-SNE) based algorithm does not consider the actual distribution of samples, a weather classification and complementarity analysis method for wind and photovoltaic power output based on the kernel principal component analysis (KPCA) and the self-organizing feature map (SOFM) neural network is proposed. Firstly, the KPCA is employed to extract the feature vectors based on numerical weather prediction data. Then, a weather pattern classification model based on the SOFM neural network is constructed by using the feature vectors as input conditions. Finally, Based on the evaluation indicators for complementary ratio of fluctuation and complementary ratio of ramp, the complementary degree and the optimal grid-connected capacity ratio of wind and photovoltaic power output under different weather patterns are quantitatively analyzed from two perspectives of fluctuation and ramp. The results demonstrate that the fluctuation complementarity and the optimal grid-connected capacity ratio of wind and photovoltaic power output have obvious difference under different weather patterns, which verifies the effectiveness of the proposed method.
This work is supported by State Grid Corporation of China “Research on Technology of Medium- and Long-term Power Balance Considering Fluctuation Characteristics of Renewable Energy” (No. 4000-201955194A-0-0-00).
|||QIAO Yanhui, HAN Shuang, XU Yanping, et al. Analysis Method for Complementarity Between Wind and Photovoltaic Power Outputs Based on Weather Classification[J]. Automation of Electric Power Systems,2021,45(2):82-88. DOI:10.7500/AEPS20200812006|