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Experimental Study of 6LoPLC for Home Energy Management Systems

Author

Listed:
  • Augustine Ikpehai

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

  • Bamidele Adebisi

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

  • Khaled M. Rabie

    (School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK)

  • Russell Haggar

    (Xsilon Ltd., Bowman House, Whitehill Lane, Royal Wootton Bassett, Wiltshire SN4 7DB, UK)

  • Mike Baker

    (Xsilon Ltd., Bowman House, Whitehill Lane, Royal Wootton Bassett, Wiltshire SN4 7DB, UK)

Abstract

Ubiquitous connectivity is already transforming residential dwellings into smart homes. As citizens continue to embrace the smart home paradigm, a new generation of low-rate and low-power communication systems is required to leverage the mass market presented by energy management in homes. Although Power Line Communication (PLC) technology has evolved in the last decade, the adaptation of PLC for constrained networks is not fully charted. By adapting some features of IEEE 802.15.4 and IPv6 over Low-power Wireless Personal Area Network (6LoWPAN) into power lines, this paper demonstrates a low-rate, low-power PLC system over the IPv6 network (referred to as 6LoPLC), for Home Energy Management System (HEMS) applications. The overall idea is to provide a framework for assessing various scenarios that cannot be easily investigated with the limited number of evaluation hardware available. In this respect, a network model is developed in NS-3 (Version 21) to measure several important characteristics of the designed system and then validated with experimental results obtained using the Hanadu evaluation kits. Following the good agreement between the two, the NS-3 model is utilised to investigate more complex scenarios and various use-cases, such as the effects of impulsive noise, the number of nodes and packet size on the latency and Bit Error Rate (BER) performances. We further demonstrate that for different network and application configurations, optimal data sizes exist. For instance, the results reveal that in order to guarantee 99% system reliability, the HEMS application data must not exceed 64 bytes. Finally, it is shown that with impulsive noise in a HEMS network comprising 50 appliances, provided the size of the payload does not exceed 64 bytes, monitoring and control applications incur a maximum latency of 238.117 ms and 248.959 ms, respectively; both of which are within acceptable limits.

Suggested Citation

  • Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie & Russell Haggar & Mike Baker, 2016. "Experimental Study of 6LoPLC for Home Energy Management Systems," Energies, MDPI, vol. 9(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1046-:d:85001
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    References listed on IDEAS

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    1. Beaudin, Marc & Zareipour, Hamidreza, 2015. "Home energy management systems: A review of modelling and complexity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 318-335.
    2. Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2016. "Broadband PLC for Clustered Advanced Metering Infrastructure (AMI) Architecture," Energies, MDPI, vol. 9(7), pages 1-19, July.
    3. Gabriele Lobaccaro & Salvatore Carlucci & Erica Löfström, 2016. "A Review of Systems and Technologies for Smart Homes and Smart Grids," Energies, MDPI, vol. 9(5), pages 1-33, May.
    4. Tiago D. P. Mendes & Radu Godina & Eduardo M. G. Rodrigues & João C. O. Matias & João P. S. Catalão, 2015. "Smart Home Communication Technologies and Applications: Wireless Protocol Assessment for Home Area Network Resources," Energies, MDPI, vol. 8(7), pages 1-33, July.
    5. Mario Collotta & Giovanni Pau, 2015. "A Solution Based on Bluetooth Low Energy for Smart Home Energy Management," Energies, MDPI, vol. 8(10), pages 1-23, October.
    6. Nikoleta Andreadou & Miguel Olariaga Guardiola & Gianluca Fulli, 2016. "Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications," Energies, MDPI, vol. 9(5), pages 1-35, May.
    7. Brian L. Thomas & Diane J. Cook, 2016. "Activity-Aware Energy-Efficient Automation of Smart Buildings," Energies, MDPI, vol. 9(8), pages 1-17, August.
    8. Rosario Miceli, 2013. "Energy Management and Smart Grids," Energies, MDPI, vol. 6(4), pages 1-29, April.
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    Cited by:

    1. Gregorio López & José Ignacio Moreno & Eutimio Sánchez & Cristina Martínez & Fernando Martín, 2017. "Noise Sources, Effects and Countermeasures in Narrowband Power-Line Communications Networks: A Practical Approach," Energies, MDPI, vol. 10(8), pages 1-42, August.
    2. Danish Mahmood & Nadeem Javaid & Sheraz Ahmed & Imran Ahmed & Iftikhar Azim Niaz & Wadood Abdul & Sanaa Ghouzali, 2017. "Orchestrating an Effective Formulation to Investigate the Impact of EMSs (Energy Management Systems) for Residential Units Prior to Installation," Energies, MDPI, vol. 10(3), pages 1-25, March.
    3. Olamide Jogunola & Augustine Ikpehai & Kelvin Anoh & Bamidele Adebisi & Mohammad Hammoudeh & Sung-Yong Son & Georgina Harris, 2017. "State-Of-The-Art and Prospects for Peer-To-Peer Transaction-Based Energy System," Energies, MDPI, vol. 10(12), pages 1-28, December.
    4. Yusuf A. Sha’aban & Augustine Ikpehai & Bamidele Adebisi & Khaled M. Rabie, 2017. "Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control," Energies, MDPI, vol. 10(10), pages 1-20, September.
    5. Giovanni Pau & Mario Collotta & Antonio Ruano & Jiahu Qin, 2017. "Smart Home Energy Management," Energies, MDPI, vol. 10(3), pages 1-5, March.

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