Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
Wind Power Scenario Generation Using Graph Convolutional Generative Adversarial Network
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DOI：
10.1109/pesgm52003.2023.10253042
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Young-Ho Cho;Shaohui Liu;Duehee Lee;Hao Zhu
中科院分区：
文献类型：
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作者：
Young-Ho Cho;Shaohui Liu;Duehee Lee;Hao Zhu
风力发电场景对于研究多个风电场与电网互联的影响非常重要。我们开发了一种图卷积生成对抗网络（GCGAN）方法，利用GAN在不使用统计建模的情况下生成大量现实场景的能力。与现有的基于GAN的风电数据生成方法不同，我们设计了GAN的隐藏层以匹配底层的空间和时间特征。我们提倡使用图过滤器来嵌入多个风电场之间的空间相关性，以及一维（1D）卷积层来表示时间特征过滤器。所提出的图和特征过滤器设计显著降低了GAN模型的复杂性，从而提高了训练效率和计算复杂度。使用澳大利亚真实的风电数据的数值结果表明，所提出的GCGAN产生的场景比其他基于GAN的输出表现出更真实的空间和时间统计。
Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid. We develop a graph convolutional generative adversarial network (GCGAN) approach by leveraging GAN’s capability in generating large number of realistic scenarios without using statistical modeling. Unlike existing GAN-based wind power data generation approaches, we design GAN’s hidden layers to match the underlying spatial and temporal characteristics. We advocate the use of graph filters to embed the spatial correlation among multiple wind farms, and a one-dimensional (1D) convolutional layer to represent the temporal feature filters. The proposed graph and feature filter design significantly reduce the GAN model complexity, leading to improvements in training efficiency and computation complexity. Numerical results using real wind power data from Australia demonstrate that the scenarios generated by the proposed GCGAN exhibit more realistic spatial and temporal statistics than other GAN-based outputs.