NDLI: Customer recognition-based demand response implementation by an electricity retailer

Content Provider	IEEE Xplore Digital Library
Author	Mahmoudi-Kohan, N. Eghbal, M. Moghaddam, M.P.
Copyright Year	2011
Description	Author affiliation: Information Technology and Electrical Engineering, The University of Queensland, Brisbane, 4072, Australia (Mahmoudi-Kohan, N.) \|\| Queensland Geothermal Energy Centre of Excellence, The University of Queensland, Brisbane, 4072, Australia (Eghbal, M.) \|\| Department of Electrical & Computer Engineering, Tarbiat Modares University, Tehran, Iran (Moghaddam, M.P.)
Abstract	This paper introduces an innovative methodology based on clustering techniques to provide the retailer with a strategy to select the most suitable customers for implementing demand response programs (DRPs). The main aim is to minimize the cost of DRPs for supplying the demand during power shortage periods. For this purpose, customers with similar load profile are clustered in same cluster by using clustering techniques. Then, clusters following similar load pattern as the load curve of the retailer, especially during peak hours, are determined. In addition, a new concept is proposed to enable the customers to submit their demand reduction function based on the award offered by the retailer. A nonlinear optimisation approach is developed to minimize the cost function of the DRP and is solved using GAMS software. The proposed methodology is implemented on 114 customers of an electricity retailer in Tehran.
Starting Page	1
Ending Page	6
File Size	1067100
Page Count	6
File Format	PDF
ISBN	9781457717932
e-ISBN	9781921897078
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-25
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Queensland Univ of Tech
Subject Keyword	demand response program clustering techniques retailer Vectors Societies weighted fuzzy average k-means Awards activities Electricity Load management customer recognition Mathematical model Load modeling load pattern
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Electricity customer characterization based on different representative load curves

Evaluating different clustering techniques for electricity customer classification

Load profiling in the deregulated electricity markets: A review of the applications

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Customer behavior based demand response model

Fuzzy clustering applied to a demand response model in a smart grid contingency scenario

A review of Electricity Load Profile Classification methods

Developing a scenario-based demand response for short-term decisions of electricity retailers

Improving WFA k-means technique for demand response programs applications

Customer recognition-based demand response implementation by an electricity retailer

Similar Documents

Electricity customer characterization based on different representative load curves

Evaluating different clustering techniques for electricity customer classification

Load profiling in the deregulated electricity markets: A review of the applications

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Customer behavior based demand response model

Fuzzy clustering applied to a demand response model in a smart grid contingency scenario

A review of Electricity Load Profile Classification methods

Developing a scenario-based demand response for short-term decisions of electricity retailers

Improving WFA k-means technique for demand response programs applications

Customer recognition-based demand response implementation by an electricity retailer