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A survey on behind the meter energy management systems in smart grid

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  • Bayram, Islam Safak
  • Ustun, Taha Selim

Abstract

Over the last few years, the fast-growing energy needs across the world have intensified a central challenge: how to reduce the generation and operation costs in power systems and, in parallel, to minimize the hydrocarbon emissions. Moreover, one-quarter of world's population still lacks access to electricity, as the cost of building conventional power grids is not affordable by third world countries. On the other hand, behind-the-meter (BTM) energy systems offer cost-effective solutions to aforementioned challenges, as they enable end-users to satisfy their energy needs with distributed energy generation and storage technologies. To that end, this paper presents a detailed survey of BTM energy management systems. The paper starts with the classification of the electrical loads with respect to their physical properties, priority ranking, and sizes. Next, the literature on BTM energy management systems is systematically classified into three main categories: technology layer, economic layer, and social layer. The technology layer spans the studies related to power systems including distributed generation and storage technologies, whereas the economic layer shows how economic incentives along with optimization and scheduling techniques are employed to shape the energy consumption. The social layer, on the other hand, presents the recent studies on how to employ social sciences to reduce the energy consumption without requiring any technological upgrades. This paper also provides an overview of the enabling technologies and standards for communication, sensing, and monitoring purposes. In the final part, a case study is provided to illustrate an implementation of the system.

Suggested Citation

  • Bayram, Islam Safak & Ustun, Taha Selim, 2017. "A survey on behind the meter energy management systems in smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1208-1232.
  • Handle: RePEc:eee:rensus:v:72:y:2017:i:c:p:1208-1232
    DOI: 10.1016/j.rser.2016.10.034
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    6. S. M. Mahfuz Alam & Mohd. Hasan Ali, 2020. "Equation Based New Methods for Residential Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-22, December.
    7. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    8. Touzani, Samir & Prakash, Anand Krishnan & Wang, Zhe & Agarwal, Shreya & Pritoni, Marco & Kiran, Mariam & Brown, Richard & Granderson, Jessica, 2021. "Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency," Applied Energy, Elsevier, vol. 304(C).
    9. van de Kaa, G. & Fens, T. & Rezaei, J. & Kaynak, D. & Hatun, Z. & Tsilimeni-Archangelidi, A., 2019. "Realizing smart meter connectivity: Analyzing the competing technologies Power line communication, mobile telephony, and radio frequency using the best worst method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 320-327.
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    13. Gordon Rausser & Wadim Strielkowski & Dalia Å treimikienÄ—, 2018. "Smart meters and household electricity consumption: A case study in Ireland," Energy & Environment, , vol. 29(1), pages 131-146, February.

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