A Comparative Study of Deep Neural Network and Meta-Model Techniques in Behavior Learning of Microgrids
A Comparative Study of Deep Neural Network and Meta-Model Techniques in Behavior Learning of Microgrids
复制标题
深度神经网络与元模型技术在微电网行为学习中的比较研究
DOI：
10.1109/access.2020.2972569
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发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Chenghong Tang
中科院分区：
文献类型：
--
作者：
Hao Xiao;Wei Pei;wei deng;Li Kong;Hongjian Sun;Chenghong Tang
微电网行为学习是多微电网合作和分布式能源市场定价的必要和具有挑战性的任务。随着用户隐私需求的增加，这个问题变得更加严重，因为公共耦合点（PCC）后面的设备参数和模型的访问限制要少得多，这阻碍了传统的基于模型的电源管理方法。为了解决这个问题，本文介绍了一些新的无模型数据驱动方法，包括深度神经网络（DNN）和元模型技术，如径向基函数（RBF），响应面方法（RSM）和克里格方法。这些方法可以通过仅访问PCC处的历史有功功率测量以及PCC后面的公共电价和天气信息，通过连续迭代学习来预测MG的行为，而无需完整的系统识别并且没有关于系统的先验知识。通过与传统的基于模型的模型进行比较，充分地进行了比较研究，以更好地了解它们的优点、缺点和局限性。数值实验验证了所提方法的有效性和适用性。本文可为未来不完全信息下MG的交互操作提供一定的参考。
Behavior learning of microgrids (MGs) is a necessary and challenging task for multi-MGs cooperation and energy pricing of distribution energy market. With the increasing demand for user privacy, this problem becomes more severe because of much less limited access to device parameters and models behind the Point of Common Coupling (PCC), which hinders conventional model-based power management methods. In this paper, to address this problem, some novel model-free data-driven methods including Deep Neural Network (DNN) and Meta-model techniques, such as Radial Basis Function (RBF), Response Surface Methods (RSM), and Kriging methods are introduced. These methods can predict the behavior of MGs through continuous iterative learning by accessing merely the historical active power measurements at the PCCs as well as public electricity price and weather information behind the PCCs, without full system identification and no prior knowledge on the system. A comparative study has been fully carried out by comparing with the conventional model-based model to better understand their advantages, drawbacks and limitations. The validity and applicability of the proposed methods is verified by numerical experiments. This paper can provide some references for future MGs interactive operation under incomplete information.