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A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

Author

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  • Sana Iqbal

    (Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Mohammad Sarfraz

    (Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Mohammad Ayyub

    (Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Mohd Tariq

    (Department of Electrical Engineering, ZHCET, Aligarh Muslim University, Aligarh 202002, India)

  • Ripon K. Chakrabortty

    (Capability Systems Centre, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia)

  • Michael J. Ryan

    (Capability Systems Centre, School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia)

  • Basem Alamri

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

Abstract

The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

Suggested Citation

  • Sana Iqbal & Mohammad Sarfraz & Mohammad Ayyub & Mohd Tariq & Ripon K. Chakrabortty & Michael J. Ryan & Basem Alamri, 2021. "A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment," Sustainability, MDPI, vol. 13(13), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7170-:d:582605
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    Cited by:

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    2. Kanakadhurga, Dharmaraj & Prabaharan, Natarajan, 2022. "Demand side management in microgrid: A critical review of key issues and recent trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    4. Sriraj Gokarakonda & Christoph van Treeck & Rajan Rawal, 2022. "Investigating Optimum Cooling Set Point Temperature and Air Velocity for Thermal Comfort and Energy Conservation in Mixed-Mode Buildings in India," Energies, MDPI, vol. 15(6), pages 1-27, March.
    5. Abdulrashid Muhammad Kabir & Mohsin Kamal & Fiaz Ahmad & Zahid Ullah & Fahad R. Albogamy & Ghulam Hafeez & Faizan Mehmood, 2021. "Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    6. 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.
    7. Dániel István Németh & Kálmán Tornai, 2023. "Electrical Load Classification with Open-Set Recognition," Energies, MDPI, vol. 16(2), pages 1-14, January.

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