Potential of Active Demand Reduction With Residential Wet Appliances: A Case Study for Belgium

被引:51
作者
Labeeuw, Wouter [1 ,2 ]
Stragier, Jeroen [3 ]
Deconinck, Geert [1 ,2 ]
机构
[1] Katholieke Univ Leuven, ELECTA, Dept Elect Engn ESAT, B-3001 Louvain, Belgium
[2] EnergyVille, B-3600 Genk, Belgium
[3] Univ Ghent, Dept Commun Sci, iMinds MICT, B-9000 Ghent, Belgium
关键词
Clustering; demand response; residential; wet appliances; INFORMATION-TECHNOLOGY; ELECTRICITY CUSTOMERS; USER ACCEPTANCE; CLASSIFICATION; PERCEPTION; MARKETS; IMPACTS; SMART; VIEW;
D O I
10.1109/TSG.2014.2357343
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Two problems are tackled in this paper: determining the active demand reduction potential of wet appliances and making time series estimates from project data. The former is an application of the latter. Household groups representative to the average population are defined by applying expectation maximization clustering to a representative measurement set (n = 1363). Attitudes toward active demand are found by conducting a survey (n = 418). Project data (n = 58) containing wet appliance measurements are scaled up by adapting the clustering algorithm, spreading the electricity demand of the wet appliances over the clusters. The potential for active demand reduction with wet appliances is 4% of the total residential power demand, assuming that 29% of the households take part. The potential is in the order of magnitude of the power reserves, but does not fulfill availability and response time requirements.
引用
收藏
页码:315 / 323
页数:9
相关论文
共 38 条
[1]  
[Anonymous], 2005, DATA MINING
[2]  
[Anonymous], 2008, Tech. rep.
[3]  
[Anonymous], IBM SPSS MOD 15 ALG
[4]   Introducing a demand-based electricity distribution tariff in the residential sector: Demand response and customer perception [J].
Bartusch, Cajsa ;
Wallin, Fredrik ;
Odlare, Monica ;
Vassileva, Iana ;
Wester, Lars .
ENERGY POLICY, 2011, 39 (09) :5008-5025
[5]   Comparisons among clustering techniques for electricity customer classification [J].
Chicco, G ;
Napoli, R ;
Piglione, F .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2006, 21 (02) :933-940
[6]   Overview and performance assessment of the clustering methods for electrical load pattern grouping [J].
Chicco, Gianfranco .
ENERGY, 2012, 42 (01) :68-80
[7]   Random effects mixture models for clustering electrical load series [J].
Coke, Geoffrey ;
Tsao, Min .
JOURNAL OF TIME SERIES ANALYSIS, 2010, 31 (06) :451-464
[9]  
Directorate General for Statistics and Economic Information Belgium, 2011, STRUCT BEV
[10]   Impact of residential demand response on power system operation: A Belgian case study [J].
Dupont, B. ;
Dietrich, K. ;
De Jonghe, C. ;
Ramos, A. ;
Belmans, R. .
APPLIED ENERGY, 2014, 122 :1-10