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An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory

Author

Listed:
  • Aqib Jamil

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Turki Ali Alghamdi

    (Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 11692, Saudi Arabia)

  • Zahoor Ali Khan

    (Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE)

  • Sakeena Javaid

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

  • Abdul Haseeb

    (Department of Electrical Engineering, Institute of Space Technology (IST), Islamabad 44000, Pakistan)

  • Zahid Wadud

    (Department of Computer System Engineering, University of Engineering and Technology, Peshawar 25000, Pakistan)

  • Nadeem Javaid

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan)

Abstract

The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.

Suggested Citation

  • Aqib Jamil & Turki Ali Alghamdi & Zahoor Ali Khan & Sakeena Javaid & Abdul Haseeb & Zahid Wadud & Nadeem Javaid, 2019. "An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory," Sustainability, MDPI, vol. 11(22), pages 1-23, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6287-:d:285040
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    References listed on IDEAS

    as
    1. Alam, Muhammad Raisul & St-Hilaire, Marc & Kunz, Thomas, 2019. "Peer-to-peer energy trading among smart homes," Applied Energy, Elsevier, vol. 238(C), pages 1434-1443.
    2. Sheraz Aslam & Zafar Iqbal & Nadeem Javaid & Zahoor Ali Khan & Khursheed Aurangzeb & Syed Irtaza Haider, 2017. "Towards Efficient Energy Management of Smart Buildings Exploiting Heuristic Optimization with Real Time and Critical Peak Pricing Schemes," Energies, MDPI, vol. 10(12), pages 1-25, December.
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    Cited by:

    1. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    2. Mahmoud H. Elkholy & Tomonobu Senjyu & Mohammed Elsayed Lotfy & Abdelrahman Elgarhy & Nehad S. Ali & Tamer S. Gaafar, 2022. "Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving," Sustainability, MDPI, vol. 14(21), pages 1-22, October.
    3. Simona-Vasilica Oprea & Adela Bâra & George Adrian Ifrim, 2021. "Optimizing the Electricity Consumption with a High Degree of Flexibility Using a Dynamic Tariff and Stackelberg Game," Journal of Optimization Theory and Applications, Springer, vol. 190(1), pages 151-182, July.
    4. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    5. Tostado-Véliz, Marcos & Kamel, Salah & Aymen, Flah & Jurado, Francisco, 2022. "A novel hybrid lexicographic-IGDT methodology for robust multi-objective solution of home energy management systems," Energy, Elsevier, vol. 253(C).

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