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Power Loss-Aware Transactive Microgrid Coalitions under Uncertainty

Author

Listed:
  • Mohammad Sadeghi

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Shahram Mollahasani

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

  • Melike Erol-Kantarci

    (School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada)

Abstract

Peer-to-peer energy trading within microgrid (MG) communities emerges as a key enabler of the future transactive distribution system and the transactive electricity market. Energy trading within MGs refers to the idea that the surplus energy of one MG can be used to satisfy the demand of another MG or a group of MGs that form an MG community. These communities can be dynamically established through time, based on the variations of demand and supply of the interconnected MGs. In many modern MGs, Electric Vehicles (EVs) have been considered as a viable storage option due to their ease of use (plug-and-play) and their growing adoption rates by drivers. On the other hand, the dynamic nature of EVs escalates the uncertainty in the transactive distribution system. In this paper, we study the problem of energy trading among MGs and EVs with the aim of power loss minimization where there is uncertainty. We propose a novel Bayesian Coalition Game (BCG) based algorithm, which allows the MGs and EVs to reduce the overall power loss by allowing them to form coalitions intelligently. The proposed scheme is compared with a conventional coalitional game theory-based approach and a Q-learning based approach. Our results show significant improvement over other compared techniques.

Suggested Citation

  • Mohammad Sadeghi & Shahram Mollahasani & Melike Erol-Kantarci, 2020. "Power Loss-Aware Transactive Microgrid Coalitions under Uncertainty," Energies, MDPI, vol. 13(21), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5782-:d:440077
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    Citations

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    Cited by:

    1. Mohammad Sadeghi & Shahram Mollahasani & Melike Erol-Kantarci, 2021. "Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning," Energies, MDPI, vol. 14(22), pages 1-20, November.
    2. Omar Makram Kamel & Ahmed A. Zaki Diab & Mohamed Metwally Mahmoud & Ameena Saad Al-Sumaiti & Hamdy M. Sultan, 2023. "Performance Enhancement of an Islanded Microgrid with the Support of Electrical Vehicle and STATCOM Systems," Energies, MDPI, vol. 16(4), pages 1-19, February.

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