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An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes

Author

Listed:
  • Awais Manzoor

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Nadeem Javaid

    (COMSATS Institute of Information Technology, Islamabad 44000, Pakistan)

  • Ibrar Ullah

    (University of Engineering and Technology Peshawar, Bannu 28100, Pakistan)

  • Wadood Abdul

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Ahmad Almogren

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

  • Atif Alamri

    (Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia)

Abstract

Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.

Suggested Citation

  • Awais Manzoor & Nadeem Javaid & Ibrar Ullah & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes," Energies, MDPI, vol. 10(9), pages 1-28, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1258-:d:109673
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    References listed on IDEAS

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    Cited by:

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    2. Hafiz Majid Hussain & Nadeem Javaid & Sohail Iqbal & Qadeer Ul Hasan & Khursheed Aurangzeb & Musaed Alhussein, 2018. "An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid," Energies, MDPI, vol. 11(1), pages 1-28, January.
    3. Haneef Ullah & Murad Khan & Irshad Hussain & Ibrar Ullah & Peerapong Uthansakul & Naeem Khan, 2021. "An Optimal Energy Management System for University Campus Using the Hybrid Firefly Lion Algorithm (FLA)," Energies, MDPI, vol. 14(19), pages 1-16, September.
    4. M. Bilal Nasir & Asif Hussain & Kamran Ali Khan Niazi & Mashood Nasir, 2022. "An Optimal Energy Management System (EMS) for Residential and Industrial Microgrids," Energies, MDPI, vol. 15(17), pages 1-18, August.
    5. Huiru Zhao & Hao Lu & Bingkang Li & Xuejie Wang & Shiying Zhang & Yuwei Wang, 2020. "Stochastic Optimization of Microgrid Participating Day-Ahead Market Operation Strategy with Consideration of Energy Storage System and Demand Response," Energies, MDPI, vol. 13(5), pages 1-16, March.
    6. 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.
    7. Amit Shewale & Anil Mokhade & Nitesh Funde & Neeraj Dhanraj Bokde, 2022. "A Survey of Efficient Demand-Side Management Techniques for the Residential Appliance Scheduling Problem in Smart Homes," Energies, MDPI, vol. 15(8), pages 1-34, April.

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