Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations
In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/14/3838 |
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| author | Dong Liu Guodong Guo Zhidong Wang Fan Li Kaiyuan Jia Chenzhenghan Zhu Haotian Wang Yingyun Sun |
| author_facet | Dong Liu Guodong Guo Zhidong Wang Fan Li Kaiyuan Jia Chenzhenghan Zhu Haotian Wang Yingyun Sun |
| author_sort | Dong Liu |
| collection | DOAJ |
| description | In recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency in historical operation data is low, which affects the modeling accuracy for scenario generation. Meanwhile, extreme operation scenarios in the form of discrete temporal data lack corresponding modeling methods. Therefore, this paper proposes a definition and generation framework for extreme power grid operation scenarios triggered by extreme weather events. Extreme operation scenario expansion is realized based on the sequential Monte Carlo sampling method and the distribution shifting algorithm. To generate equipment failure scenarios in discrete temporal data form and extreme output scenarios in continuous temporal data form for renewable energy, a Gumbel-Softmax variational autoencoder and an extreme conditional generative adversarial network are respectively proposed. Numerical examples show that the proposed models can effectively overcome limitations related to insufficient historical extreme data and discrete extreme scenario training. Additionally, they can generate improved-quality equipment failure scenarios and renewable energy extreme output scenarios and provide scenario support for power grid planning and operation. |
| format | Article |
| id | doaj-art-b83ce7d04efd46e5b4590c0795f95ca5 |
| institution | Matheson Library |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-b83ce7d04efd46e5b4590c0795f95ca52025-07-25T13:21:47ZengMDPI AGEnergies1996-10732025-07-011814383810.3390/en18143838Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output VariationsDong Liu0Guodong Guo1Zhidong Wang2Fan Li3Kaiyuan Jia4Chenzhenghan Zhu5Haotian Wang6Yingyun Sun7State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaState Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, ChinaSchool of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaIn recent years, extreme weather events have occurred more frequently. The resulting equipment failure, renewable energy extreme output, and other extreme operation scenarios affect the smooth operation of power grids. The occurrence probability of extreme operation scenarios is small, and the occurrence frequency in historical operation data is low, which affects the modeling accuracy for scenario generation. Meanwhile, extreme operation scenarios in the form of discrete temporal data lack corresponding modeling methods. Therefore, this paper proposes a definition and generation framework for extreme power grid operation scenarios triggered by extreme weather events. Extreme operation scenario expansion is realized based on the sequential Monte Carlo sampling method and the distribution shifting algorithm. To generate equipment failure scenarios in discrete temporal data form and extreme output scenarios in continuous temporal data form for renewable energy, a Gumbel-Softmax variational autoencoder and an extreme conditional generative adversarial network are respectively proposed. Numerical examples show that the proposed models can effectively overcome limitations related to insufficient historical extreme data and discrete extreme scenario training. Additionally, they can generate improved-quality equipment failure scenarios and renewable energy extreme output scenarios and provide scenario support for power grid planning and operation.https://www.mdpi.com/1996-1073/18/14/3838extreme operation scenariovariational autoencoderGumbel-Softmaxextreme conditional generative adversarial networksdistribution shifting |
| spellingShingle | Dong Liu Guodong Guo Zhidong Wang Fan Li Kaiyuan Jia Chenzhenghan Zhu Haotian Wang Yingyun Sun Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations Energies extreme operation scenario variational autoencoder Gumbel-Softmax extreme conditional generative adversarial networks distribution shifting |
| title | Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations |
| title_full | Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations |
| title_fullStr | Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations |
| title_full_unstemmed | Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations |
| title_short | Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations |
| title_sort | extreme grid operation scenario generation framework considering discrete failures and continuous output variations |
| topic | extreme operation scenario variational autoencoder Gumbel-Softmax extreme conditional generative adversarial networks distribution shifting |
| url | https://www.mdpi.com/1996-1073/18/14/3838 |
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