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

Author

Listed:
  • 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)

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).

Suggested Citation

  • Muhammad Babar Rasheed & Nadeem Javaid & Muhammad Awais & Zahoor Ali Khan & Umar Qasim & Nabil Alrajeh & Zafar Iqbal & Qaisar Javaid, 2016. "Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes," Energies, MDPI, vol. 9(7), pages 1-30, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:542-:d:73928
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    References listed on IDEAS

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    1. Danish Mahmood & Nadeem Javaid & Nabil Alrajeh & Zahoor Ali Khan & Umar Qasim & Imran Ahmed & Manzoor Ilahi, 2016. "Realistic Scheduling Mechanism for Smart Homes," Energies, MDPI, vol. 9(3), pages 1-28, March.
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    Cited by:

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    2. Zafar Iqbal & Nadeem Javaid & Syed Muhammad Mohsin & Syed Muhammad Abrar Akber & Muhammad Khalil Afzal & Farruh Ishmanov, 2018. "Performance Analysis of Hybridization of Heuristic Techniques for Residential Load Scheduling," Energies, MDPI, vol. 11(10), pages 1-31, October.
    3. Ghulam Hafeez & Nadeem Javaid & Sohail Iqbal & Farman Ali Khan, 2018. "Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units," Energies, MDPI, vol. 11(3), pages 1-27, March.
    4. Dan Dobrotă & Ionela Rotaru & Florin Adrian Nicolescu & Mădălina Marin, 2019. "Improving the Sustainability of the Manufacturing Process by Constructively Optimizing the Parts “Transition Type Fitting”," Sustainability, MDPI, vol. 11(19), pages 1-18, October.
    5. Shaterabadi, Mohammad & Jirdehi, Mehdi Ahmadi & Amiri, Nima & Omidi, Sina, 2020. "Enhancement the economical and environmental aspects of plus-zero energy buildings integrated with INVELOX turbines," Renewable Energy, Elsevier, vol. 153(C), pages 1355-1367.
    6. Bishnu P. Bhattarai & Kurt S. Myers & Birgitte Bak-Jensen & Sumit Paudyal, 2017. "Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks," Energies, MDPI, vol. 10(1), pages 1-18, January.
    7. Ch Anwar ul Hassan & Jawaid Iqbal & Nasir Ayub & Saddam Hussain & Roobaea Alroobaea & Syed Sajid Ullah, 2022. "Smart Grid Energy Optimization and Scheduling Appliances Priority for Residential Buildings through Meta-Heuristic Hybrid Approaches," Energies, MDPI, vol. 15(5), pages 1-19, February.
    8. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    9. Nadeem Javaid & Sardar Mehboob Hussain & Ibrar Ullah & Muhammad Asim Noor & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations," Energies, MDPI, vol. 10(8), pages 1-29, August.
    10. Andrzej Ożadowicz, 2017. "A New Concept of Active Demand Side Management for Energy Efficient Prosumer Microgrids with Smart Building Technologies," Energies, MDPI, vol. 10(11), pages 1-22, November.
    11. Sébastien Bissey & Sébastien Jacques & Jean-Charles Le Bunetel, 2017. "The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing," Energies, MDPI, vol. 10(11), pages 1-24, October.
    12. Iulia Stamatescu & Nicoleta Arghira & Ioana Făgărăşan & Grigore Stamatescu & Sergiu Stelian Iliescu & Vasile Calofir, 2017. "Decision Support System for a Low Voltage Renewable Energy System," Energies, MDPI, vol. 10(1), pages 1-15, January.
    13. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

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