Volume 4 • Issue 1 • PP: 01-11 • 2025
AI-Driven Decentralized Energy Systems: A Review of Peer-to-Peer Renewable Energy Networks
View PDF 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
References
[1] Y. Lu, N. Hao, X. Li, and M. Y. Alshahrani, “AI-enabled sports-system peer-to-peer energy exchange network for remote areas in the digital economy,” Heliyon, vol. 10, no. 16, p. e35890, Aug. 2024, doi: 10.1016/J.HELIYON.2024.E35890.
[2] 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.
[3] K. Heo, J. Kong, S. Oh, and J. Jung, “Development of operator-oriented peer-to-peer energy trading model for integration into the existing distribution system,” Int. J. Electr. Power Energy Syst., vol. 125, p. 106488, Feb. 2021, doi: 10.1016/J.IJEPES.2020.106488.
[4] U. your Rehman, P. Faria, L. Gomes, and Z. Vale, “Future of Energy Management Models in Smart Homes: A Systematic Literature Review of Research Trends, Gaps, and Future Directions,” Process Integration Optim. Sustain., pp. 1–30, Apr. 2025, doi: 10.1007/S41660-025-00506-X.
[5] M. Zedan, M. Nour, G. Shabib, L. Nasrat, and A. A. Ali, “Review of peer-to-peer energy trading: Advances and challenges,” e-Prime - Adv. Electr. Eng., vol. 10, Dec. 2024, doi: 10.1016/J.PRIME.2024.100778.
[6] A. Abraheem Almahdi, M. A. Jbril, and A. M. Azzin, “Role of DG in Enhancement of Voltage Stability and Technological Innovations for DG Integration,” J. Multidisciplinary Curr. Res., vol. 12, p. 3, doi: 10.14741/ijmcr/v.12.6.2.
[7] “AI-Powered Smart Grids: Optimizing Energy Consumption in Enterprises,” Accessed: Apr. 07, 2025. [Online]. Available: https://www.researchgate.net/publication/389992721_AI-Powered_Smart_Grids_Optimizing_Energy_Consumption_in_Enterprises.
[8] 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.
[9] 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.
[10] D. C. S. Santos, L. M. S. Gonçalves, and R. L. de Oliveira, “A framework for decentralized energy trading based on blockchain technology,” Int. J. Energy Res., vol. 45, no. 3, pp. 2223–2235, Mar. 2021, doi: 10.1002/er.5857.
[11] M. Solahudeen Tando, S. Gawusu, S. Yakubu, O. S. Kwabena, D. Donatus, and S. Raqib Abu, “Decentralized energy trading with blockchain technology,” The Intersection of Blockchain and Energy Trading: Exploring Decentralized Solutions for Next-Generation Energy Markets, pp. 75–101, Jan. 2025, doi: 10.1016/B978-0-443-23627-3.00004-1.
[12] C. Zhao, Q. Liu, D. Han, P. Niu, and S. Wu, “Decentralized energy trading framework with personalized pricing for energy community embedded with shared energy storage,” Electr. Power Syst. Res., vol. 235, p. 110562, Oct. 2024, doi: 10.1016/J.EPSR.2024.110562.
[13] M. Shoaei, Y. Noorollahi, A. Hajinezhad, and S. F. Moosavian, “A review of the applications of artificial intelligence in renewable energy systems: An approach-based study,” Energy Convers. Manag., vol. 306, p. 118207, Apr. 2024, doi: 10.1016/J.ENCONMAN.2024.118207.
[14] P. Arévalo, F. Jurado, P. Arévalo, and F. Jurado, “Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids,” Energies, vol. 17, no. 17, p. 4501, Sep. 2024, doi: 10.3390/EN17174501.
[15] N. A. Alghanmi and H. Alkhudhayr, “EnergyShare AI: Transforming P2P energy trading through advanced deep learning,” Heliyon, vol. 10, no. 17, p. e36948, Sep. 2024, doi: 10.1016/J.HELIYON.2024.E36948.
[16] B. Biswal, S. Deb, S. Datta, T. S. Ustun, and U. Cali, “Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques,” Energy Reports, vol. 12, pp. 3654–3670, Dec. 2024, doi: 10.1016/J.EGYR.2024.09.056.
[17] N. Heris, M. Mohammadpourfard, Q. Cui, A. Martínez, and P. Arévalo, “Distributed Peer-to-Peer Optimization Based on Robust Reinforcement Learning with Demand Response: A Review,” Computers, vol. 14, no. 2, p. 65, Feb. 2025, doi: 10.3390/COMPUTERS14020065.
[18] T. M. Masaud, J. Warner, and E. F. El-Saadany, “A Blockchain-Enabled Decentralized Energy Trading Mechanism for Islanded Networked Microgrids,” IEEE Access, vol. 8, pp. 211291–211302, 2020, doi: 10.1109/ACCESS.2020.3038824.
[19] M. I. A. Shah, A. Wahid, E. Barrett, and K. Mason, “Multi-agent systems in Peer-to-Peer energy trading: A comprehensive survey,” Eng. Appl. Artif. Intell., vol. 132, p. 107847, Jun. 2024, doi: 10.1016/J.ENGAPPAI.2024.107847.
[20] M. A. Judge, V. Franzitta, D. Curto, A. Guercio, G. Cirrincione, and H. A. Khattak, “A comprehensive review of artificial intelligence approaches for smart grid integration and optimization,” Energy Convers. Manag.: X, vol. 24, p. 100724, Oct. 2024, doi: 10.1016/J.ECMX.2024.100724.
[21] Y. Lu, N. Hao, X. Li, and M. Y. Alshahrani, “AI-enabled sports-system peer-to-peer energy exchange network for remote areas in the digital economy,” Heliyon, vol. 10, no. 16, p. e35890, Aug. 2024, doi: 10.1016/j.heliyon.2024.e35890.
[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.
[25] N. A. Alghanmi and H. Alkhudhayr, “EnergyShare AI: Transforming P2P energy trading through advanced deep learning,” Heliyon, vol. 10, no. 17, Sep. 2024, doi: 10.1016/j.heliyon.2024.e36948.
Cite This Article
Choose your preferred format