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Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links

Author

Listed:
  • Olamide Jogunola

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

  • Bamidele Adebisi

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

  • Kelvin Anoh

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

  • Augustine Ikpehai

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

  • Mohammad Hammoudeh

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

  • Georgina Harris

    (Faculty of Mathematics, Computing and Technology, The Open University, Milton Keynes MK7 6AA, UK)

  • Haris Gacanin

    (Nokia-Bell Labs, Copernicuslaan, 50, 2018 Antwerp, Belgium)

Abstract

Algorithms for distributed coordination and control are increasingly being used in smart grid applications including peer-to-peer energy trading and sharing to improve reliability and efficiency of the power system. However, for realistic deployment of these algorithms, their designs should take into account the suboptimal conditions of the communication network, in particular the communication links that connect the energy trading entities in the energy network. This study proposes a distributed adaptive primal (DAP) routing algorithm to facilitate communication and coordination among proactive prosumers in an energy network over imperfect communication links. The proposed technique employs a multi-commodity flow optimization scheme in its formulation with the objective to minimize both the communication delay and loss of energy transactional messages due to suboptimal network conditions. Taking into account realistic constraints relating to network delay and communication link capacity between the peers, the DAP routing algorithm is used to evaluate network performance using various figures of merit such as probability of signal loss, message delay, congestion and different network topologies. Further, we address the link communication delay problem by redirecting traffic from congested links to less utilized ones. The results show that the proposed routing algorithm is robust to packet loss on the communication links with a 20% reduction in delay compared with hop-by-hop adaptive link state routing algorithm.

Suggested Citation

  • Olamide Jogunola & Bamidele Adebisi & Kelvin Anoh & Augustine Ikpehai & Mohammad Hammoudeh & Georgina Harris & Haris Gacanin, 2018. "Distributed Adaptive Primal Algorithm for P2P-ETS over Unreliable Communication Links," Energies, MDPI, vol. 11(9), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2331-:d:167694
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    References listed on IDEAS

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    1. Hamada Almasalma & Sander Claeys & Konstantin Mikhaylov & Jussi Haapola & Ari Pouttu & Geert Deconinck, 2018. "Experimental Validation of Peer-to-Peer Distributed Voltage Control System," Energies, MDPI, vol. 11(5), pages 1-22, May.
    2. 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.
    3. 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.
    4. Olamide Jogunola & Augustine Ikpehai & Kelvin Anoh & Bamidele Adebisi & Mohammad Hammoudeh & Haris Gacanin & Georgina Harris, 2017. "Comparative Analysis of P2P Architectures for Energy Trading and Sharing," Energies, MDPI, vol. 11(1), pages 1-20, December.
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    Cited by:

    1. Christie Etukudor & Benoit Couraud & Valentin Robu & Wolf-Gerrit Früh & David Flynn & Chinonso Okereke, 2020. "Automated Negotiation for Peer-to-Peer Electricity Trading in Local Energy Markets," Energies, MDPI, vol. 13(4), pages 1-19, February.

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