Fast Reconfiguration of Distribution Network Based on Deep Reinforcement Learning Algorithm. Issue 1 (November 2020)

• Record Type: Journal Article
• Authors: Zhao, Bincheng; Han, Xueshan; Ma, Yiran; Li, Zhiqi
• Is Part Of: IOP conference series. Volume 571:Issue 1(2020)
• Publication Date: 2020-11
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/571/1/012023
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: In the context of large-scale grid connection of distributed energy, during the reconfiguration of the distribution network, the availability of distributed energy and the load of the distribution system may be inconsistent with the prediction due to the influence of environmental factors and human factors. If the distribution network reconfiguration is still carried out according to the expected offline optimization scheme, there may be reliability problems of voltage over-limits and economic problems of increased network loss in the actual reconfiguration process. Therefore, the reconfiguration plan formulated in advance can give some guidance to the dispatch operator, but it may not be directly used in the actual reconfiguration process. This paper proposes a deep reinforcement learning approach to solving the electric distribution network reconfiguration. Based on the uncertainty of distributed energy output and network load in the distribution network, the online algorithm of distribution network reconfiguration realizes the second-level solution of distribution network reconfiguration, through day-ahead training of the neural network.