A novel time-of-use tariff design based on Gaussian Mixture Model

被引:88
作者
Li, Ran [1 ]
Wang, Zhimin [2 ]
Gu, Chenghong [1 ]
Li, Furong [1 ]
Wu, Hao [3 ]
机构
[1] Univ Bath, Bath BA2 7AY, Avon, England
[2] China State Grid Jibei Elect Econ Res Inst, Beijing 100045, Peoples R China
[3] Zhejiang Univ, Hangzhou 310027, Zhejiang, Peoples R China
基金
英国工程与自然科学研究理事会;
关键词
Time-of-use tariff; Clustering; Demand response; Benefit quantification; Energy storage; ELECTRICITY; RETAILERS;
D O I
10.1016/j.apenergy.2015.02.063
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper proposes a novel method to design feasible Time-of-Use (ToU) tariffs for domestic customers from flat rate tariffs by clustering techniques. The method is dedicated to designing the fundamental window patterns of ToU tariffs rather than optimising exact prices for each settlement period. It makes use of Gaussian Mixture Model clustering technique to group half-hour interval flat rate tariffs within a day into clusters to determine ToU tariffs. Two groups of ToU are designed following the variations in energy prices and system loading demand respectively. With a number of price-oriented and load-oriented ToU tariffs, the investigation is further carried out to explore the effects of these ToU tariffs on domestic demand response (DR), especially in terms of energy cost reduction and peak shaving. The DR in this paper is assumed to be enabled by household storage battery and the objective of the DR in response to each ToU tariff is to ininimise the electricity bills for end customers and/or mitigate network pressures. An example study in the UK case is also carried out to demonstrate the effectiveness of the proposed methods. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1530 / 1536
页数:7
相关论文
共 17 条
[1]  
[Anonymous], 2004, FINITE MIXTURE MODEL
[2]   A model for efficient consumer pricing schemes in electricity markets [J].
Celebi, Emre ;
Fuller, J. David .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (01) :60-67
[3]   Time-of-Use Pricing in Electricity Markets Under Different Market Structures [J].
Celebi, Emre ;
Fuller, J. David .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (03) :1170-1181
[4]  
Chengqi Ou, 2010, Proceedings 2010 3rd International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII 2010), P176, DOI 10.1109/ICIII.2010.520
[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]   A MAXIMUM-LIKELIHOOD METHODOLOGY FOR CLUSTERWISE LINEAR-REGRESSION [J].
DESARBO, WS ;
CRON, WL .
JOURNAL OF CLASSIFICATION, 1988, 5 (02) :249-282
[7]  
Elexon, 2013, LOAD PROF THEIR US E
[8]   Designing incentive compatible contracts for effective demand management [J].
Fahrioglu, M ;
Alvarado, FL .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2000, 15 (04) :1255-1260
[9]   Short- and long-run time-of-use price elasticities in Swiss residential electricity demand [J].
Filippini, Massimo .
ENERGY POLICY, 2011, 39 (10) :5811-5817
[10]   THE CONCEPT OF DEMAND-SIDE MANAGEMENT FOR ELECTRIC UTILITIES [J].
GELLINGS, CW .
PROCEEDINGS OF THE IEEE, 1985, 73 (10) :1468-1470