Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster
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 sce...
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Editorial Department of Electric Power Construction
2025-03-01
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| Series: | Dianli jianshe |
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| Online Access: | https://www.cepc.com.cn/fileup/1000-7229/PDF/1740549190357-181314373.pdf |
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| author | HOU Hui, WAN Yi, WANG Zhenguo, JIANG Kaihua, WANG Shaohua, LI Te, LI Zhengtian, LIN Xiangning |
| author_facet | HOU Hui, WAN Yi, WANG Zhenguo, JIANG Kaihua, WANG Shaohua, LI Te, LI Zhengtian, LIN Xiangning |
| author_sort | HOU Hui, WAN Yi, WANG Zhenguo, JIANG Kaihua, WANG Shaohua, LI Te, LI Zhengtian, LIN Xiangning |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dd6c19c5da3444329e4927d80722ac9c |
| institution | OA Journals |
| issn | 1000-7229 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Editorial Department of Electric Power Construction |
| record_format | Article |
| series | Dianli jianshe |
| spelling | doaj-art-dd6c19c5da3444329e4927d80722ac9c2025-08-20T02:04:11ZzhoEditorial Department of Electric Power ConstructionDianli jianshe1000-72292025-03-0146314615410.12204/j.issn.1000-7229.2025.03.012Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon DisasterHOU Hui, WAN Yi, WANG Zhenguo, JIANG Kaihua, WANG Shaohua, LI Te, LI Zhengtian, LIN Xiangning01. School of Automation， Wuhan University of Technology， Wuhan 430070， China;2. State Grid Zhejiang Electric Power Research Institute， Hangzhou 310014， China;3. State Key Laboratory of Advanced Electromagnetic Technology， Huazhong University of Science and Technology， Wuhan 430074， ChinaIn 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.https://www.cepc.com.cn/fileup/1000-7229/PDF/1740549190357-181314373.pdftyphoon disaster|wind and photovoltaic generation prediction|uncertainty|bi-level model|loop iteration |
| spellingShingle | HOU Hui, WAN Yi, WANG Zhenguo, JIANG Kaihua, WANG Shaohua, LI Te, LI Zhengtian, LIN Xiangning Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster Dianli jianshe typhoon disaster|wind and photovoltaic generation prediction|uncertainty|bi-level model|loop iteration |
| title | Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster |
| title_full | Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster |
| title_fullStr | Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster |
| title_full_unstemmed | Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster |
| title_short | Wind and Photovoltaic Generation Prediction Bi-level Model Based on Uncertainty Scenarios Under Typhoon Disaster |
| title_sort | wind and photovoltaic generation prediction bi level model based on uncertainty scenarios under typhoon disaster |
| topic | typhoon disaster|wind and photovoltaic generation prediction|uncertainty|bi-level model|loop iteration |
| url | https://www.cepc.com.cn/fileup/1000-7229/PDF/1740549190357-181314373.pdf |
| work_keys_str_mv | AT houhuiwanyiwangzhenguojiangkaihuawangshaohualitelizhengtianlinxiangning windandphotovoltaicgenerationpredictionbilevelmodelbasedonuncertaintyscenariosundertyphoondisaster |