Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique
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 dai...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/10/12/2144 |
| _version_ | 1798040274984763392 |
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| author | Yajing Gao Yanping Sun Xiaodan Wang Feifan Chen Ali Ehsan Hongmei Li Hong Li |
| author_facet | Yajing Gao Yanping Sun Xiaodan Wang Feifan Chen Ali Ehsan Hongmei Li Hong Li |
| author_sort | Yajing Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:05:13Z |
| format | Article |
| id | doaj.art-7b0e22ec3ded4ee4b54e3e51b3bce2c8 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:05:13Z |
| publishDate | 2017-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7b0e22ec3ded4ee4b54e3e51b3bce2c82022-12-22T04:00:44ZengMDPI AGEnergies1996-10732017-12-011012214410.3390/en10122144en10122144Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario TechniqueYajing Gao0Yanping Sun1Xiaodan Wang2Feifan Chen3Ali Ehsan4Hongmei Li5Hong Li6School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, ChinaTo 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.https://www.mdpi.com/1996-1073/10/12/2144demand side resource (DSR)self-organizing map (SOM)scenario partitionresource aggregation (RA)multi-objective optimization |
| spellingShingle | Yajing Gao Yanping Sun Xiaodan Wang Feifan Chen Ali Ehsan Hongmei Li Hong Li Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique Energies demand side resource (DSR) self-organizing map (SOM) scenario partition resource aggregation (RA) multi-objective optimization |
| title | Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique |
| title_full | Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique |
| title_fullStr | Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique |
| title_full_unstemmed | Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique |
| title_short | Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique |
| title_sort | multi objective optimized aggregation of demand side resources based on a self organizing map clustering algorithm considering a multi scenario technique |
| topic | demand side resource (DSR) self-organizing map (SOM) scenario partition resource aggregation (RA) multi-objective optimization |
| url | https://www.mdpi.com/1996-1073/10/12/2144 |
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