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Energies 2017, 10(12), 2144; https://doi.org/10.3390/en10122144

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

1
School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
2
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
*
Authors to whom correspondence should be addressed.
Received: 9 November 2017 / Revised: 29 November 2017 / Accepted: 6 December 2017 / Published: 15 December 2017
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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. View Full-Text
Keywords: demand side resource (DSR); self-organizing map (SOM); scenario partition; resource aggregation (RA); multi-objective optimization demand side resource (DSR); self-organizing map (SOM); scenario partition; resource aggregation (RA); multi-objective optimization
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Gao, Y.; Sun, Y.; Wang, X.; Chen, F.; Ehsan, A.; Li, H.; Li, H. Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique. Energies 2017, 10, 2144.

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