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A novel communication efficient peer-to-peer energy trading scheme for enhanced privacy in microgrids

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  • Umer, Khalid
  • Huang, Qi
  • Khorasany, Mohsen
  • Afzal, Muhammad
  • Amin, Waqas

Abstract

Peer-to-peer energy trading is an emerging concept for implementing power markets with a large number of distributed energy resources. The success of such trading depends on the participation of lots of prosumers. Therefore, the trading scheme should motivate peers to participate in the market through fair pricing while guaranteeing their privacy. A novel communication efficient algorithm, called Energy Trading Distributed Alternating Direction Method of Multipliers (ETD-ADMM), is proposed to solve the social welfare maximization problem in a decentralized manner without requiring any central authority. The algorithm is formulated using the concept of node coloring, where each node only needs to negotiate with its neighbors to reach the global optimal solution. The decrease in communications makes the trading scheme more privacy-preserving and scalable than other contemporary approaches. The proposed algorithm is tested on a wide range of scenarios that prove its optimality and fast convergence. The comparison of the algorithm with other state-of-the-art distributed approaches shows that it requires much fewer communications and iterations to get similar optimal outcomes.

Suggested Citation

  • Umer, Khalid & Huang, Qi & Khorasany, Mohsen & Afzal, Muhammad & Amin, Waqas, 2021. "A novel communication efficient peer-to-peer energy trading scheme for enhanced privacy in microgrids," Applied Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:appene:v:296:y:2021:i:c:s0306261921005274
    DOI: 10.1016/j.apenergy.2021.117075
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    References listed on IDEAS

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    Cited by:

    1. Hannie Zang & JongWon Kim, 2021. "Reinforcement Learning Based Peer-to-Peer Energy Trade Management Using Community Energy Storage in Local Energy Market," Energies, MDPI, vol. 14(14), pages 1-18, July.
    2. Li, Wenzhuo & Wang, Shengwei, 2022. "A fully distributed optimal control approach for multi-zone dedicated outdoor air systems to be implemented in IoT-enabled building automation networks," Applied Energy, Elsevier, vol. 308(C).
    3. Liu, Junhong & Long, Qinfei & Liu, Rong-Peng & Liu, Wenjie & Hou, Yunhe, 2023. "Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading," Applied Energy, Elsevier, vol. 331(C).
    4. Azim, M. Imran & Tushar, Wayes & Saha, Tapan K. & Yuen, Chau & Smith, David, 2022. "Peer-to-peer kilowatt and negawatt trading: A review of challenges and recent advances in distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    5. Ruan, Hebin & Gao, Hongjun & Gooi, Hoay Beng & Liu, Junyong, 2022. "Active distribution network operation management integrated with P2P trading," Applied Energy, Elsevier, vol. 323(C).
    6. Babagheibi, Mahsa & Jadid, Shahram & Kazemi, Ahad, 2023. "An Incentive-based robust flexibility market for congestion management of an active distribution system to use the free capacity of Microgrids," Applied Energy, Elsevier, vol. 336(C).

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