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An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster

Author

Listed:
  • Mansouri, Seyed Amir
  • Rezaee Jordehi, Ahmad
  • Marzband, Mousa
  • Tostado-Véliz, Marcos
  • Jurado, Francisco
  • Aguado, José A.

Abstract

The integrated exploitation of different energy infrastructures in the form of multi-energy systems (MESs) and the transformation of traditional prosumers into smart prosumers are two effective pathways to achieve net-zero emission energy systems in the near future. Managing different energy markets is one of the biggest challenges for the operators of MESs, since different carriers are traded in them simultaneously. Hence, this paper presents a hierarchical decentralized framework for the simultaneous management of electricity, heat and hydrogen markets among multi-energy microgrids (MEMGs) integrated with smart prosumers. The market strategy of MEMGs is deployed using a hierarchical framework and considering the programs requested by smart prosumers. A deep learning-based forecaster is utilized to predict uncertain parameters while a risk-averse information gap decision theory (IGDT)-based strategy controls the scheduling risk. A new prediction-based mechanism for designing dynamic demand response (DR) schemes compatible with smart prosumers’ behavior is introduced, and the results illustrate that this mechanism reduces the electricity and heat clearing prices in peak hours by 17.5% and 8.78%, respectively. Moreover, the results reveal that the introduced structure for hydrogen exchange through the transportation system has the ability to be implemented in competitive markets. Overall, the simulation results confirm that the proposed hierarchical model is able to optimally manage the competitive markets of electricity, heat and hydrogen by taking advantage of the potential of smart prosumers.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018177
    DOI: 10.1016/j.apenergy.2022.120560
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    as
    1. Klemm, Christian & Vennemann, Peter, 2021. "Modeling and optimization of multi-energy systems in mixed-use districts: A review of existing methods and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Gu, Chenjia & Zhang, Yao & Wang, Jianxue & Li, Qingtao, 2021. "Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle," Applied Energy, Elsevier, vol. 301(C).
    3. Coelho, António & Iria, José & Soares, Filipe, 2021. "Network-secure bidding optimization of aggregators of multi-energy systems in electricity, gas, and carbon markets," Applied Energy, Elsevier, vol. 301(C).
    4. Mansouri, Seyed Amir & Ahmarinejad, Amir & Javadi, Mohammad Sadegh & Catalão, João P.S., 2020. "Two-stage stochastic framework for energy hubs planning considering demand response programs," Energy, Elsevier, vol. 206(C).
    5. Lai, Ching-Ming & Teh, Jiashen, 2022. "Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability," Applied Energy, Elsevier, vol. 305(C).
    6. Mansouri, Seyed Amir & Nematbakhsh, Emad & Ahmarinejad, Amir & Jordehi, Ahmad Rezaee & Javadi, Mohammad Sadegh & Marzband, Mousa, 2022. "A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Kostelac, Matija & Pavić, Ivan & Zhang, Ning & Capuder, Tomislav, 2022. "Uncertainty modelling of an industry facility as a multi-energy demand response provider," Applied Energy, Elsevier, vol. 307(C).
    8. Zhu, Xu & Sun, Yuanzhang & Yang, Jun & Dou, Zhenlan & Li, Gaojunjie & Xu, Chengying & Wen, Yuxin, 2022. "Day-ahead energy pricing and management method for regional integrated energy systems considering multi-energy demand responses," Energy, Elsevier, vol. 251(C).
    9. Bao, Shiyuan & Yang, Zhifang & Yu, Juan, 2021. "Decomposition and analysis of marginal prices in multi-energy systems," Energy, Elsevier, vol. 221(C).
    10. Mansouri, S.A. & Ahmarinejad, A. & Nematbakhsh, E. & Javadi, M.S. & Esmaeel Nezhad, A. & Catalão, J.P.S., 2022. "A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources," Energy, Elsevier, vol. 245(C).
    11. Alabi, Tobi Michael & Aghimien, Emmanuel I. & Agbajor, Favour D. & Yang, Zaiyue & Lu, Lin & Adeoye, Adebusola R. & Gopaluni, Bhushan, 2022. "A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems," Renewable Energy, Elsevier, vol. 194(C), pages 822-849.
    12. Wu, Xiong & Qi, Shixiong & Wang, Zhao & Duan, Chao & Wang, Xiuli & Li, Furong, 2019. "Optimal scheduling for microgrids with hydrogen fueling stations considering uncertainty using data-driven approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    13. Zhang, Honghui & Chen, Yuanyuan & Liu, Kuili & Dehan, Sim, 2022. "A novel power system scheduling based on hydrogen-based micro energy hub," Energy, Elsevier, vol. 251(C).
    14. Zhang, Xiaojin & Bauer, Christian & Mutel, Christopher L. & Volkart, Kathrin, 2017. "Life Cycle Assessment of Power-to-Gas: Approaches, system variations and their environmental implications," Applied Energy, Elsevier, vol. 190(C), pages 326-338.
    15. Li, Xue & Li, Wenming & Zhang, Rufeng & Jiang, Tao & Chen, Houhe & Li, Guoqing, 2020. "Collaborative scheduling and flexibility assessment of integrated electricity and district heating systems utilizing thermal inertia of district heating network and aggregated buildings," Applied Energy, Elsevier, vol. 258(C).
    16. Good, Nicholas & Martínez Ceseña, Eduardo A. & Zhang, Lingxi & Mancarella, Pierluigi, 2016. "Techno-economic and business case assessment of low carbon technologies in distributed multi-energy systems," Applied Energy, Elsevier, vol. 167(C), pages 158-172.
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