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

Yajing Gao, Yanping Sun, Xiaodan Wang, Feifan Chen, Ali Ehsan, Hongmei Li and Hong Li
Additional contact information
Yajing Gao: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Yanping Sun: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Xiaodan Wang: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Feifan Chen: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Ali Ehsan: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Hongmei Li: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Hong Li: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2017, vol. 10, issue 12, 1-20

Abstract: 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.

Keywords: demand side resource (DSR); self-organizing map (SOM); scenario partition; resource aggregation (RA); multi-objective optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/12/2144/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/12/2144/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:12:p:2144-:d:123101

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().