A Comparative Study of Deep Neural Network and Meta-model techniques in Behavior Learning of Microgrids

Xiao, Hao; Pei, Wei; Deng, Wei; Kong, Li; Sun, Hongjian; Tang, Chenghong

Abstract

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.

Citation

Xiao, H., Pei, W., Deng, W., Kong, L., Sun, H., & Tang, C. (2020). A Comparative Study of Deep Neural Network and Meta-model techniques in Behavior Learning of Microgrids. IEEE Access, 8, 30104-30118. https://doi.org/10.1109/access.2020.2972569

• Journal Article Type: Article
• Acceptance Date: Feb 4, 2020
• Online Publication Date: Feb 10, 2020
• Publication Date: Feb 10, 2020
• Deposit Date: Feb 14, 2020
• Publicly Available Date: Feb 14, 2020
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 30104-30118
• DOI: https://doi.org/10.1109/access.2020.2972569
• Public URL: https://durham-repository.worktribe.com/output/1270550