基于深度学习的电力系统场景快速约简
Fast Scenario Reduction for Power Systems by Deep Learning
摘要：
情景约简是随机规划问题中的一个重要课题。由于负荷和可再生能源的随机性，随机规划成为电力系统优化的有效方法。因此，情景约简在近年来得到了越来越多的关注。为了快速减少场景集，已经提出了许多场景缩减方法。但是，场景还原的速度仍然很慢，至少需要几秒钟到几分钟才能完成还原。这种速度的限制，防止随机规划，以实现实时最优控制问题。本文提出了一种基于深度学习的快速场景约简方法来解决这一问题。受基于深度学习的图像处理、识别和生成方法的启发，场景数据被转换为2D图像数据，然后被馈送到深度卷积神经网络（DCNN）中。DCNN的输出将是简化场景集的“图像”。由于神经网络可以以非常高的速度处理图像，因此神经网络的场景缩减也可以非常快。仿真结果表明，所提出的DCNN方法可以在非常高的速度完成场景约简。
英文摘要：
Scenario reduction is an important topic in stochastic programming problems. Due to the random behavior of load and renewable energy, stochastic programming becomes a useful technique to optimize power systems. Thus, scenario reduction gets more attentions in recent years. Many scenario reduction methods have been proposed to reduce the scenario set in a fast speed. However, the speed of scenario reduction is still very slow, in which it takes at least several seconds to several minutes to finish the reduction. This limitation of speed prevents stochastic programming to be implemented in real-time optimal control problems. In this paper, a fast scenario reduction method based on deep learning is proposed to solve this problem. Inspired by the deep learning based image process, recognition and generation methods, the scenario data are transformed into a 2D image-like data and then to be fed into a deep convolutional neural network (DCNN). The output of the DCNN will be an "image" of the reduced scenario set. Since images can be processed in a very high speed by neural networks, the scenario reduction by neural network can also be very fast. The results of the simulation show that the scenario reduction with the proposed DCNN method can be completed in very high speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
