With the rapid increase in distributed wind generation, considerable efforts have been devoted to the microgrid day-ahead scheduling. The effectiveness of those methods will highly depend on the selection of the uncertainty sets. We propose a distribution-free approach for wind power scenario generation, using sequence generative adversarial networks. To capture the temporal correlation, the model adopts the long short-term memory architecture and uses generative adversarial networks coupled with reinforcement learning, which, in contrast to the existing methods, avoids manual labeling and captures the complex dynamics of the weather. We conduct case studies based on the data from the Bonneville Power Administration and the National Renewable Energy Laboratory, and show that the generated scenarios can better characterize the variability of wind power and reduce the risk of uncertainties, compared with those produced by Gaussian distribution, vanilla long short-term memory, and multivariate kernel density estimation. Moreover, the proposed method achieves better performance when applied to the day-ahead scheduling of microgrids.

中文翻译：

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用于风力发电场景生成的序列生成对抗网络

随着分布式风力发电的快速增长，微电网日前调度已投入大量精力。这些方法的有效性在很大程度上取决于不确定性集的选择。我们提出了一种使用序列生成对抗网络的风力发电场景生成的无分布方法。为了捕捉时间相关性，该模型采用长短期记忆架构，并使用生成对抗网络结合强化学习，与现有方法相比，避免了手动标记并捕捉天气的复杂动态。我们根据来自邦纳维尔电力管理局和国家可再生能源实验室的数据进行案例研究，并表明，与高斯分布、普通长短期记忆和多元核密度估计产生的情景相比，生成的情景可以更好地表征风电的可变性并降低不确定性的风险。此外，当应用于微电网的日前调度时，所提出的方法获得了更好的性能。