Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints - Yibo Lai; Libo Fan; Weiyan Zheng; Rongjie Han; Kai Liu (Energy Informatics)
Abstract In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.