Next Article in Journal
Numerical Study on Hydrodynamic Performance of Bionic Caudal Fin
Next Article in Special Issue
Reallocating Charging Loads of Electric Vehicles in Distribution Networks
Previous Article in Journal
The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors
Previous Article in Special Issue
Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2016, 6(1), 16; doi:10.3390/app6010016

Residential Demand Response Scheduling with Consideration of Consumer Preferences

1
Qatar Environment and Energy Research Institute (QEERI), Hamad Bin Khalifa University, P.O. Box 5825, Doha 3263, Qatar
2
College of Science and Engineering, Hamad Bin Khalifa University, P.O. Box 5825, Doha 3263, Qatar
*
Author to whom correspondence should be addressed.
Academic Editor: Minho Shin
Received: 19 October 2015 / Revised: 31 December 2015 / Accepted: 31 December 2015 / Published: 12 January 2016
(This article belongs to the Special Issue Smart Grid: Convergence and Interoperability)
View Full-Text   |   Download PDF [724 KB, uploaded 12 January 2016]   |  

Abstract

This paper proposes a new demand response scheduling framework for an array of households, which are grouped into different categories based on socio-economic factors, such as the number of occupants, family decomposition and employment status. Each of the households is equipped with a variety of appliances. The model takes the preferences of participating households into account and aims to minimize the overall production cost and, in parallel, to lower the individual electricity bills. In the existing literature, customers submit binary values for each time period to indicate their operational preferences. However, turning the appliances “on” or “off” does not capture the associated discomfort levels, as each appliance provides a different service and leads to a different level of satisfaction. The proposed model employs integer values to indicate household preferences and models the scheduling problem as a multi-objective mixed integer programming. The main thrust of the framework is that the multi-level preference modeling of appliances increases their “flexibility”; hence, the job scheduling can be done at a lower cost. The model is evaluated by using the real data provided by the Department of Energy & Climate Change, UK. In the computational experiments, we examine the relation between the satisfaction of consumers based on the appliance usage preferences and the electricity costs by exploring the Pareto front of the related objective functions. The results show that the proposed model leads to significant savings in electricity cost, while maintaining a good level of customer satisfaction. View Full-Text
Keywords: demand response; mixed integer linear programming; scheduling; smart grids demand response; mixed integer linear programming; scheduling; smart grids
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jovanovic, R.; Bousselham, A.; Bayram, I.S. Residential Demand Response Scheduling with Consideration of Consumer Preferences. Appl. Sci. 2016, 6, 16.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top