A new clustering algorithm for load profiling based on billing data

被引:27
作者
Fidalgo, Jose Nuno [1 ]
Matos, Manuel Antonio [1 ]
Ribeiro, Luis
机构
[1] Univ Porto, Fac Engn, INESC Porto, P-4200465 Oporto, Portugal
关键词
Load modeling; Electricity Markets; Clustering; Optimization; Simulated annealing;
D O I
10.1016/j.epsr.2011.08.016
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
In open energy markets, the settlement process between distribution operators and traders is made on an hourly (or 15 min) basis, while LV consumers' billing data continues to result from monthly energy bills. In order to reconcile these two different realities, load profiling is used as a means to redistribute the consumed energy of each trader's portfolio by hourly intervals, according to recorded consumption patterns. This paper presents a new clustering approach to derive typical load diagrams that can be used in the process. The algorithm uses real load diagrams obtained in measurement campaigns to define classes (in the billing information space) that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP Distribution (the Portuguese distribution system operator) and the result was approved by the Regulatory Authority that adopted the proposed profiles for market use. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:27 / 33
页数:7
相关论文
共 23 条
[1]
ALLERA SV, 1998, P DISTRIBUTECH EUR D
[2]
[Anonymous], SCIENCE
[3]
BOMPARD E, 2000, P RIMAPS 2000 FUNCH
[4]
Electricity customer classification using frequency-domain load pattern data [J].
Carpaneto, E ;
Chicco, G ;
Napoli, R ;
Scutariu, M .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2006, 28 (01) :13-20
[5]
Determination of customer load characteristics by load survey system at taipower [J].
Chen, CS ;
Hwang, JC ;
Tzeng, YM ;
Huang, CW ;
Cho, MY .
IEEE TRANSACTIONS ON POWER DELIVERY, 1996, 11 (03) :1430-1436
[6]
*EUR MARK RES CUST, 1999, 19993100001 EUR MARK
[7]
*EUR SYST TAR ISS, 2000, 20002200004 EUR SYST
[8]
FALCAO DM, 2001, 2001 IEEE PES SUMM M, V2
[9]
An electric energy consumer characterization framework based on data mining techniques [J].
Figueiredo, V ;
Rodrigues, F ;
Vale, Z ;
Gouveia, JB .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (02) :596-602
[10]
GILLMAN R, 1999, TR11278 EPRI