Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster - HOU Hui, WAN Yi, WANG Zhenguo, JIANG Kaihua, WANG Shaohua, LI Te, LI Zhengtian, LIN Xiangning (Dianli jianshe)
In the context of global climate change intensification， large-scale and rapid development of new energy， meteorological conditions have become one of the key factors affecting power grid security and power supply. A wind and photovoltaic generation prediction bi-level model based on uncertainty scenarios under typhoon disaster model is proposed. First， the upper layer with Wasserstein generative adversarial network of uncertainty processing model is established. Through the combination of Wasserstein generative adversarial network model and improved K-means clustering， typical scenarios of uncertainties are established to realize the reasonable optimize of renewable energy uncertainty. Second， the lower layer with wind and photovoltaic generation prediction model based on typhoon disaster is established. It has used the Stacking and long short-term memory. Based on the loop iteration between the upper layer and the lower layer， the wind and photovoltaic generation prediction value with the highest accuracy are obtained. Finally， the 2022 super typhoon “Muifa” in Zhoushan port， Zhejiang， is taken as an example. The results verify that the optimization results have an impact on the prediction results and the advanced nature of the proposed model.