Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique

• Main Authors: Yajing Gao, Yanping Sun, Xiaodan Wang, Feifan Chen, Ali Ehsan, Hongmei Li, Hong Li
• Format: Article
• Language: English
• Published: MDPI AG 2017-12-01
• Series: Energies
• Subjects: demand side resource (DSR); self-organizing map (SOM); scenario partition; resource aggregation (RA); multi-objective optimization
• Online Access: https://www.mdpi.com/1996-1073/10/12/2144

To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model based on quarter division and self-organizing map (SOM) neural network algorithm is proposed. In the model, the clustering feature vector is selected as the input vector of the SOM algorithm to perform DSR clustering analysis to get the different scenarios. In addition, to obtain the resource aggregation (RA) with good load characteristics, response characteristics and distributed generation (DG) consumption, a multi-scenario objective optimization aggregation model of DSR based on scenario partition is established, and an the model is solved by an improved niche evolutionary multi-objective immune algorithm. Finally, the case studies are given to verify the validity of the model.