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Distributed robust operational optimization of networked microgrids embedded interconnected energy hubs

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  • Nikmehr, Nima

Abstract

An interconnected energy hubs (EHs) framework aims to enhance the efficiency of the multicarrier energy system through realizing optimal coordination among existing players. In this paper, EH concept is studied in networked microgrids (MGs) structure to exploit the potential capabilities of microgrids in satisfying various types of energy demands. In the proposed model, MGs and distribution network are independent entities which have their local scheduling problem. To coordinate the operation of entities, a distributed algorithm based on the alternating direction method of multipliers (ADMM) is exploited to resolve the conflict of exchanged power between multi-MGs and distribution network. Considering the uncertainties, a distributed robust model is employed to precisely analyze the performance of multi-carrier energy networked MGs in different robustness levels. The efficiency of the ADMM model on integrated energy systems is tested on a networked MGs. The achieved results can ensure the light computational burden and convergence of proposed distributed algorithm. The proposed EH optimization problem is solved via Gurobi optimizer packages. According to the results, the ADMM converges to the final solution after 5 iterations, and with increasing the robustness level, the operation costs of EHs increases. The obtained results by Gurobi is more optimal than heuristic algorithms.

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  • Nikmehr, Nima, 2020. "Distributed robust operational optimization of networked microgrids embedded interconnected energy hubs," Energy, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:energy:v:199:y:2020:i:c:s0360544220305478
    DOI: 10.1016/j.energy.2020.117440
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    1. Shams, Mohammad H. & Shahabi, Majid & Khodayar, Mohammad E., 2018. "Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response," Energy, Elsevier, vol. 155(C), pages 326-338.
    2. Chen, Yue & Wei, Wei & Liu, Feng & Wu, Qiuwei & Mei, Shengwei, 2018. "Analyzing and validating the economic efficiency of managing a cluster of energy hubs in multi-carrier energy systems," Applied Energy, Elsevier, vol. 230(C), pages 403-416.
    3. Chang, Chenglin & Wang, Yufei & Ma, Jiaze & Chen, Xiaolu & Feng, Xiao, 2018. "An energy hub approach for direct interplant heat integration," Energy, Elsevier, vol. 159(C), pages 878-890.
    4. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    5. Lorestani, Alireza & Gharehpetian, G.B. & Nazari, Mohammad Hassan, 2019. "Optimal sizing and techno-economic analysis of energy- and cost-efficient standalone multi-carrier microgrid," Energy, Elsevier, vol. 178(C), pages 751-764.
    6. Hu, Mian & Wang, Yan-Wu & Xiao, Jiang-Wen & Lin, Xiangning, 2019. "Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters," Energy, Elsevier, vol. 185(C), pages 910-921.
    7. Rakipour, Davood & Barati, Hassan, 2019. "Probabilistic optimization in operation of energy hub with participation of renewable energy resources and demand response," Energy, Elsevier, vol. 173(C), pages 384-399.
    8. Hak-Man Kim & Yujin Lim & Tetsuo Kinoshita, 2012. "An Intelligent Multiagent System for Autonomous Microgrid Operation," Energies, MDPI, vol. 5(9), pages 1-16, September.
    9. Hussain, Akhtar & Bui, Van-Hai & Kim, Hak-Man, 2019. "Microgrids as a resilience resource and strategies used by microgrids for enhancing resilience," Applied Energy, Elsevier, vol. 240(C), pages 56-72.
    10. Davatgaran, Vahid & Saniei, Mohsen & Mortazavi, Seyed Saeidollah, 2018. "Optimal bidding strategy for an energy hub in energy market," Energy, Elsevier, vol. 148(C), pages 482-493.
    11. Jadidbonab, Mohammad & Babaei, Ebrahim & Mohammadi-ivatloo, Behnam, 2019. "CVaR-constrained scheduling strategy for smart multi carrier energy hub considering demand response and compressed air energy storage," Energy, Elsevier, vol. 174(C), pages 1238-1250.
    12. Hemmati, S. & Ghaderi, S.F. & Ghazizadeh, M.S., 2018. "Sustainable energy hub design under uncertainty using Benders decomposition method," Energy, Elsevier, vol. 143(C), pages 1029-1047.
    13. Yu, Mengmeng & Lu, Renzhi & Hong, Seung Ho, 2016. "A real-time decision model for industrial load management in a smart grid," Applied Energy, Elsevier, vol. 183(C), pages 1488-1497.
    14. Moretti, Luca & Martelli, Emanuele & Manzolini, Giampaolo, 2020. "An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids," Applied Energy, Elsevier, vol. 261(C).
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