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Demand Side Management for Smart Houses: A Survey

Author

Listed:
  • Khouloud Salameh

    (Department of Computer Science and Engineering, School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 10021, United Arab Emirates)

  • Mohammed Awad

    (Department of Computer Science and Engineering, School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 10021, United Arab Emirates)

  • Aisha Makarfi

    (Department of Computer Science and Engineering, School of Engineering, American University of Ras Al Khaimah, Ras Al Khaimah P.O. Box 10021, United Arab Emirates)

  • Abdul-Halim Jallad

    (Electrical Engineering Department, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
    National Space Science and Technology Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Richard Chbeir

    (Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France)

Abstract

Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.

Suggested Citation

  • Khouloud Salameh & Mohammed Awad & Aisha Makarfi & Abdul-Halim Jallad & Richard Chbeir, 2021. "Demand Side Management for Smart Houses: A Survey," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6768-:d:575358
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    References listed on IDEAS

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    1. Axel Gautier & Julien Jacqmin & Jean-Christophe Poudou, 2018. "The prosumers and the grid," Journal of Regulatory Economics, Springer, vol. 53(1), pages 100-126, February.
    2. Zhou, Bin & Li, Wentao & Chan, Ka Wing & Cao, Yijia & Kuang, Yonghong & Liu, Xi & Wang, Xiong, 2016. "Smart home energy management systems: Concept, configurations, and scheduling strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 30-40.
    3. Jean-Christophe Poudou & Axel Gautier & Julien Jacqmin, 2018. "The prosumers and the grid," Post-Print hal-01810028, HAL.
    4. Makhadmeh, Sharif Naser & Khader, Ahamad Tajudin & Al-Betar, Mohammed Azmi & Naim, Syibrah & Abasi, Ammar Kamal & Alyasseri, Zaid Abdi Alkareem, 2019. "Optimization methods for power scheduling problems in smart home: Survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    5. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
    6. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    7. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    8. Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Tan, Kang Miao & Mithulananthan, N., 2015. "A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 365-385.
    9. Vardakas, John S. & Zorba, Nizar & Verikoukis, Christos V., 2015. "Performance evaluation of power demand scheduling scenarios in a smart grid environment," Applied Energy, Elsevier, vol. 142(C), pages 164-178.
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    Cited by:

    1. Emad M. Ahmed & Rajarajeswari Rathinam & Suchitra Dayalan & George S. Fernandez & Ziad M. Ali & Shady H. E. Abdel Aleem & Ahmed I. Omar, 2021. "A Comprehensive Analysis of Demand Response Pricing Strategies in a Smart Grid Environment Using Particle Swarm Optimization and the Strawberry Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    2. 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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