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

Muhammad Babar Rasheed, Nadeem Javaid, Muhammad Awais, Zahoor Ali Khan, Umar Qasim, Nabil Alrajeh, Zafar Iqbal and Qaisar Javaid
Additional contact information
Muhammad Babar Rasheed: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Nadeem Javaid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Muhammad Awais: Department of Technology, The University of Lahore, Lahore 54000, Pakistan
Zahoor Ali Khan: Internetworking Program, Faculty of Engineering, Dalhousie University, Halifax, NS B3J 4R2, Canada
Umar Qasim: Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada
Nabil Alrajeh: Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Zafar Iqbal: University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan
Qaisar Javaid: Department of Computer Science & Software Engineering, International Islamic University, Islamabad 44000, Pakistan

Energies, 2016, vol. 9, issue 7, 1-30

Abstract: 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).

Keywords: demand side management; optimization; energy management; real time pricing; genetic algorithm (GA); knapsack; smart grid (SG); programmable communication thermostat; microgird (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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