Metaheuristic Optimization Review

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Volume 4 , Issue 1 , PP: 01-11, 2025 | Cite this article as | XML | Html | PDF | Full Length Article

AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks

M. El-Said 1 * , Marwa M. Eid 2 *

  • 1 Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt - (Melsaid@mans.edu.eg)
  • 2 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt - (mmm@ieee.org)
  • Doi: https://doi.org/10.54216/MOR.040101

    Received: January 01, 2025 Revised: February 25, 2025 Accepted: May 02, 2025
    Abstract

    This work examines the transformational potential of AI-based decentralized energy systems: P2P renewable energy networks interconnect AI, blockchain technology, and multi-agent systems, thus circumventing the barriers of traditional centralized grids. This paper will trace how their latest trends in real-time energy optimization, secure smart contracts, and autonomous coordination of distributed resources can enhance grid resilience, minimize transmission losses, and democratize energy markets. However, it becomes evident that to enable mass adoption; significant challenges must be addressed regarding renewable energy intermittency, scalability limitations, regulatory loopholes, and cybersecurity threats. Through synthesizing current research and the analytical case of Brooklyn Microgrid, this paper discusses some of the barriers and potential future directions that must be emphasized, such as hybrid optimization models, standardized frameworks, and inclusive design for accelerating transitions towards sustainable and equitable energy systems.

    Keywords :

    Decentralized energy systems , Peer-to-peer energy trading , Artificial Intelligence , Blockchain , Multi-agent systems , Renewable energy , Smart grids

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    [22]    A. Esmat, M. de Vos, Y. Ghiassi-Farrokhfal, P. Palensky, and D. Epema, “A novel decentralized platform for peer-to-peer energy trading market with blockchain technology,” Appl Energy, vol. 282, p. 116123, Jan. 2021, doi: 10.1016/J.APENERGY.2020.116123.

    [23]    R. Darshi, S. Shamaghdari, A. Jalali, and H. Arasteh, “Decentralized energy management system for smart microgrids using reinforcement learning,” IET Gener. Transm. Distrib., vol. 17, no. 9, pp. 2142–2155, May 2023, doi: 10.1049/GTD2.12796.

    [24]    N. Saeed, F. Wen, and M. Z. Afzal, “Decentralized peer-to-peer energy trading in microgrids: Leveraging blockchain technology and smart contracts,” Energy Reports, vol. 12, pp. 1753–1764, Dec. 2024, doi: 10.1016/J.EGYR.2024.07.053.

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    Cite This Article As :
    El-Said, M.. , M., Marwa. AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks. Metaheuristic Optimization Review, vol. , no. , 2025, pp. 01-11. DOI: https://doi.org/10.54216/MOR.040101
    El-Said, M. M., M. (2025). AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks. Metaheuristic Optimization Review, (), 01-11. DOI: https://doi.org/10.54216/MOR.040101
    El-Said, M.. M., Marwa. AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks. Metaheuristic Optimization Review , no. (2025): 01-11. DOI: https://doi.org/10.54216/MOR.040101
    El-Said, M. , M., M. (2025) . AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks. Metaheuristic Optimization Review , () , 01-11 . DOI: https://doi.org/10.54216/MOR.040101
    El-Said M. , M. M. [2025]. AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks. Metaheuristic Optimization Review. (): 01-11. DOI: https://doi.org/10.54216/MOR.040101
    El-Said, M. M., M. "AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks," Metaheuristic Optimization Review, vol. , no. , pp. 01-11, 2025. DOI: https://doi.org/10.54216/MOR.040101