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Open AccessArticle

Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes

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COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
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Department of Technology, The University of Lahore, Lahore 54000, Pakistan
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Internetworking Program, Faculty of Engineering, Dalhousie University, Halifax, NS B3J 4R2, Canada
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Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada
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Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi Arabia
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University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan
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Department of Computer Science & Software Engineering, International Islamic University, Islamabad 44000, Pakistan
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Author to whom correspondence should be addressed.
Academic Editor: G.J.M. (Gerard) Smit
Energies 2016, 9(7), 542; https://doi.org/10.3390/en9070542
Received: 10 March 2016 / Revised: 26 June 2016 / Accepted: 5 July 2016 / Published: 14 July 2016
(This article belongs to the Special Issue Decentralized Management of Energy Streams in Smart Grids)
This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc appliances under comfort constraints. The optimization models for ua, el, and iel appliances are solved subject to electricity cost minimization and PAR reduction. Considering user comfort, el appliances are considered where users can adjust appliance waiting time to increase or decrease their comfort level. Furthermore, energy demand of r appliances is fulfilled via local supply where the major objective is to reduce the fuel cost of various generators by proper scheduling. Simulation results show that the proposed algorithms efficiently schedule the energy demand of all types of appliances by considering identified constraints (i.e., PAR, variable prices, temperature, capacity limit and waiting time). View Full-Text
Keywords: demand side management; optimization; energy management; real time pricing; genetic algorithm (GA); knapsack; smart grid (SG); programmable communication thermostat; microgird demand side management; optimization; energy management; real time pricing; genetic algorithm (GA); knapsack; smart grid (SG); programmable communication thermostat; microgird
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MDPI and ACS Style

Rasheed, M.B.; Javaid, N.; Awais, M.; Khan, Z.A.; Qasim, U.; Alrajeh, N.; Iqbal, Z.; Javaid, Q. Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes. Energies 2016, 9, 542.

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