Smart energy storage dispatching of peak-valley load characteristics based-convolutional neural network. (January 2022)

• Record Type: Journal Article
• Authors: Lu, Huayong; Yang, Xiao; Guo, Hongzuo
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Distribution network energy storage -- Peak-valley load -- Convolutional neural network -- Peak dispatching; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107543
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The distribution network can realize the load management strategy through demand side management, so that it has greater flexibility. Based on elastic load research, a coordinated dispatch method of adjustable active distribution network with intelligent load based on convolutional neural network (CNN) is proposed. First, in order to stabilize the load fluctuation of the distribution network and reduce the network loss, the intelligent coordinated load dispatching of the distribution network is implemented. Then, the distribution network can suppress power fluctuations caused by new energy access, and achieve intelligent load distribution and power constraints. The experimental results show that the convolution neural network algorithm based on peak-valley load characteristics has a good peak valley load control effect compared with the test data analysis. The adjustment of peak value can reduce the frequent activities caused by valley difference, which has a good utilization rate of the impact on energy utilization.