基于隐式最大似然估计的风电出力场景生成

Translated title of the contribution: Scenario generation of wind power output based on implicit maximum likelihood estimation

Wenlong Liao, Xiang Ren, Zhe Yang*, Wenqing Yang, Chao Wei

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

With the increasing penetration of wind power,how to effectively describe the uncertainty of wind power output has become a huge challenge for the operation and planning of distribution network,for which,a scenario generation method of wind power output is proposed based on implicit maximum likelihood estimation. According to the data characteristics of wind power output curves,the loss function and network structure suitable for scenario generation of wind power output are designed. Through unsupervised training,the scenario generator can learn the mapping relationship between Gaussian noise and wind power output scenarios. The wind power output scenarios with different time scales can be generated with the proposed method by only adjusting the relevant parameters in the model. The simulative results show that both the forecasting interval average width and forecasting interval coverage percentage of the proposed method are better than those of the existing generative adversarial network,and the proposed method has certain univer⁃ sality for different wind farms.

Translated title of the contributionScenario generation of wind power output based on implicit maximum likelihood estimation
Original languageChinese (Traditional)
JournalDianli Zidonghua Shebei/Electric Power Automation Equipment
Volume42
Issue number11
Pages (from-to)56-63
Number of pages8
ISSN1006-6047
DOIs
Publication statusPublished - Nov 2022

Bibliographical note

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© 2022 Electric Power Automation Equipment Press. All rights reserved.

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