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Presenting a model for decentralized operation based on the internet of things in a system multiple microgrids

Author

Listed:
  • Lei, Yu
  • Ali, Mazhar
  • Khan, Imran Ali
  • Yinling, Wang
  • Mostafa, Aziz

Abstract

This paper addresses the escalating energy demand by proposing a market-based model for decentralized operation in a multi-microgrid system, aiming to enhance power distribution efficiency and reliability. Microgrids, small-scale electrical networks, exchange energy among themselves, storage systems, load collectors, and the upstream distribution network. The system encompasses uncontrollable and controllable generation units and loads. Leveraging Internet of Things (IoT) technology and cloud infrastructure, the communication structure facilitates data measurement, processing, and exchange of technical and financial information. Each microgrid, storage system, and load collector operates independently to maximize benefits by participating in the future market. The proposed model is simulated under various scenarios, and the results are systematically analyzed. To validate the model's performance, alterations in fuel cost and solar radiation intensity values are implemented, and simulation results are scrutinized. The model acknowledges interdependence and connectivity between microgrids and employs a chimpanzee optimization algorithm inspired by chimpanzee social behavior. This algorithm effectively tackles complex optimization problems by simulating the cooperative behavior of a group of chimpanzees, where each individual represents a potential solution. The numerical results show that Microgrid 1 achieves a 100% success rate in maintaining uninterrupted loads with cost-effective power. Microgrid 2 strategically preserves a 100% success rate for priority 1 load but faces occasional challenges, leading to limited load shedding. Microgrid 3 achieves an 80% success rate in keeping priority 1 load uninterrupted, adapting to market conditions.

Suggested Citation

  • Lei, Yu & Ali, Mazhar & Khan, Imran Ali & Yinling, Wang & Mostafa, Aziz, 2024. "Presenting a model for decentralized operation based on the internet of things in a system multiple microgrids," Energy, Elsevier, vol. 293(C).
  • Handle: RePEc:eee:energy:v:293:y:2024:i:c:s0360544224004092
    DOI: 10.1016/j.energy.2024.130637
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    References listed on IDEAS

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    1. Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Marzband, Mousa & Tostado-Véliz, Marcos & Jurado, Francisco & Aguado, José A., 2023. "An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster," Applied Energy, Elsevier, vol. 333(C).
    2. Huang, Shoujun & Abedinia, Oveis, 2021. "Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market," Energy, Elsevier, vol. 225(C).
    3. Yang, Qing & Wang, Hao & Wang, Taotao & Zhang, Shengli & Wu, Xiaoxiao & Wang, Hui, 2021. "Blockchain-based decentralized energy management platform for residential distributed energy resources in a virtual power plant," Applied Energy, Elsevier, vol. 294(C).
    4. Alizadeh, Ali & Esfahani, Moein & Dinar, Farid & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud & Busvelle, Eric, 2024. "A cooperative transactive multi-carrier energy control mechanism with P2P energy + reserve trading using Nash bargaining game theory under renewables uncertainty," Applied Energy, Elsevier, vol. 353(PB).
    5. Goraj, Rafał & Kiciński, Marcin & Ślefarski, Rafał & Duczkowska, Anna, 2023. "Validity of decision criteria for selecting power-to-gas projects in Poland," Utilities Policy, Elsevier, vol. 83(C).
    6. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    7. Andrew Whitmore & Anurag Agarwal & Li Xu, 2015. "The Internet of Things—A survey of topics and trends," Information Systems Frontiers, Springer, vol. 17(2), pages 261-274, April.
    8. Kong, Xiangyu & Sun, Fangyuan & Huo, Xianxu & Li, Xue & Shen, Yu, 2020. "Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things," Energy, Elsevier, vol. 210(C).
    9. Wang, Zhuo & Hou, Hui & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Xie, Changjun, 2024. "Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach," Applied Energy, Elsevier, vol. 357(C).
    10. Shancang Li & Li Da Xu & Shanshan Zhao, 2015. "The internet of things: a survey," Information Systems Frontiers, Springer, vol. 17(2), pages 243-259, April.
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