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New Energy Scenario Generation Based on SA/WGAN
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Yuhao WANG1, Haitao LIU1, 2, Kangkai ZHU1, Cong ZHONG1, Jiayi MA1
Electric Drive | 2024, 54(6) : 45 - 53
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Electric Drive | 2024, 54(6): 45-53
New Energy Scenario Generation Based on SA/WGAN
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Yuhao WANG1, Haitao LIU1, 2, Kangkai ZHU1, Cong ZHONG1, Jiayi MA1
Affiliations
  • 1 School of Electric Power Engineering，Nanjing Institute of Technology，Nanjing 210000，Jiangsu，China
  • 2 Jiangsu Collaborative Innovation Center of Smart Distribution Network，Nanjing 210000，Jiangsu，China
Published: 2024-06-20 doi: 10.19457/j.1001-2095.dqcd24775
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With the increased penetration rate of new energy year by year，it is difficult to accurately predict the randomness and fluctuation characteristics of its output，causing a severe challenge to the operation，planning and scheduling of electrical power system. Therefore，modeling for the uncertainty of new energy has attracted more and more attention. To obtain the time sequence characteristics of new energy output scenario more effectively，a new energy scenario generation method was proposed based on data drive，and combined self-attention mechanism with generative adversarial network discriminator with gradient penalty through applying the SA/WGAN model. Through building a deep learning model based on the combination of two models，effectively highlight the timing sequence characteristics of new energy output scenario and enhancing the nonlinear fitting capability in scenario generation. The example results show that，compared with the scenario generation results of original WGAN and WGAN-LSTM，the new energy generation scenario of proposed model can not only effectively improve the accuracy，but also possess the advantages of stable WGAN-GP training results and quick SA calculation speed，which can achieve a more efficient generation of scenarios that is close to the distribution of real new energy scenario.

unsupervised learning  /  self-attention （SA）  /  generative adversarial networks（GAN）  /  new energy  /  scenario generation
Yuhao WANG, Haitao LIU, Kangkai ZHU, Cong ZHONG, Jiayi MA. New Energy Scenario Generation Based on SA/WGAN[J]. Electric Drive, 2024 , 54 (6) : 45 -53 . DOI: 10.19457/j.1001-2095.dqcd24775
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Year 2024 volume 54 Issue 6
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Article Info
doi: 10.19457/j.1001-2095.dqcd24775
  • Receive Date：2022-11-16
  • Online Date：2025-11-26
  • Published：2024-06-20
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  • Received：2022-11-16
  • Revised：2022-12-24
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    1 School of Electric Power Engineering，Nanjing Institute of Technology，Nanjing 210000，Jiangsu，China
    2 Jiangsu Collaborative Innovation Center of Smart Distribution Network，Nanjing 210000，Jiangsu，China
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