Customer baseline load models for residential sector in a smart-grid environment

被引:43
作者
Sharifi, R. [1 ]
Fathi, S. H. [1 ]
Vahidinasab, V. [2 ]
机构
[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran
[2] Shahid Beheshti Univ, Dept Elect Engn, Tehran, Iran
关键词
Demand response (DR); Demand side management (DSM); Customer baseline load (CBL); Building load coefficient (BLC); Building insulation; Flexible load (FL); Non-flexible load (NFL); DEMAND RESPONSE; ENERGY MANAGEMENT; BUILDINGS;
D O I
10.1016/j.egyr.2016.04.003
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
Demand response (DR) can expand the customer participation in the electricity market and lead by changing its pattern from a simple function to an interactive relation. There are various methods to evaluate the successful implementation of DR program, the most important of which is determination of customer baseline load (CBL). In fact, CBL is the expected pattern of customer consumption in the absence of DR programs. Few works have been done in the field of calculation of CBL in residential sector, while most of them have paid little attention to the impact of changes in weather conditions on these calculations. In this paper, a new method is presented for the calculation of CBL for customers in residential sector in the context of a smart grid, considering the impact of weather changes. The results clearly show the high impact of changes in weather conditions on the calculation of CBL, and also show the extent of effect of buildings' improved insulation on this parameter. It is also indicated that implementing DR programs can increase the willingness of customers in residential sector to improve the insulations of their buildings. (C) 2016 The Authors. Published by Elsevier Ltd.
引用
收藏
页码:74 / 81
页数:8
相关论文
共 16 条
[1]  
[Anonymous], 2011, FUTURE ELECTRICITY D
[2]  
[Anonymous], 2008, ESTIMATING DEMAND RE
[3]  
[Anonymous], P IEEE BUCH POWERTEC
[4]  
[Anonymous], 2008, GLOBAL SENSITIVITY A
[5]   Model predictive HVAC load control in buildings using real-time electricity pricing [J].
Avci, Mesut ;
Erkoc, Murat ;
Rahmani, Amir ;
Asfour, Shihab .
ENERGY AND BUILDINGS, 2013, 60 :199-209
[6]   Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system [J].
Brahman, Faeze ;
Honarmand, Masoud ;
Jadid, Shahram .
ENERGY AND BUILDINGS, 2015, 90 :65-75
[7]   Statistical analysis of baseline load models for non-residential buildings [J].
Coughlin, Katie ;
Piette, Mary Ann ;
Goldman, Charles ;
Kiliccote, Sila .
ENERGY AND BUILDINGS, 2009, 41 (04) :374-381
[8]  
Faria P, 2013, 2013 IEEE POW EN SOC, P1
[9]  
Cabrera NG, 2013, IEEE INT AUT MEET
[10]   BACnet-EnOcean Smart Grid Gateway and its application to demand response in buildings [J].
Li, Yi-Chang ;
Hong, Seung Ho .
ENERGY AND BUILDINGS, 2014, 78 :183-191