Extreme Grid Operation Scenario Generation Framework Considering Discrete Failures and Continuous Output Variations

Dong Liu, Guodong Guo (), Zhidong Wang, Fan Li, Kaiyuan Jia, Chenzhenghan Zhu, Haotian Wang and Yingyun Sun
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Dong Liu: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Guodong Guo: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Zhidong Wang: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Fan Li: State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China
Kaiyuan Jia: School of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Chenzhenghan Zhu: School of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Haotian Wang: School of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Yingyun Sun: School of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 14, 1-18

Abstract: 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.

Keywords: extreme operation scenario; variational autoencoder; Gumbel-Softmax; extreme conditional generative adversarial networks; distribution shifting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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