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Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices

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  • Haddadian, Hossein
  • Noroozian, Reza

Abstract

Clustering the large scale active distribution networks (ADNs) into a set of smaller microgrids (MGs) or multi-microgrids (MMGs)-based operation of ADNs can have several benefits for the utility, electric power consumers, and distributed generation (DG) owners such as easier control strategy, distributed control among MGs, load routing and transfer among MGs and reliability enhancement. This research proposes a novel program for optimal operation of ADNs based on the MMGs approach. Initially, the operation of ADNs was carried out by integrated management of all distributed energy resources (DERs) using probabilistic forward-backward load flow using Monte Carlo simulation (MCS) algorithm. In this stage, energy storage devices (ESDs) as one of the significant components of MG are sized and sited in ADN. In the second stage, the operation quality is scrutinized accurately by testing the possibility of MG construction in the modified ADN based on various technical criteria such as adequacy, efficiency, voltage profile, and reliability. This issue has not been addressed in the previous researches. Finally, the results of the proposed model are compared with conventional operation method in different scenarios by implementation on IEEE 33-bus ADN and an actual Portuguese distribution network using a powerful multi-objective optimization tool known as non-dominated genetic algorithm-II (NSGA-II).

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  • Haddadian, Hossein & Noroozian, Reza, 2017. "Multi-microgrids approach for design and operation of future distribution networks based on novel technical indices," Applied Energy, Elsevier, vol. 185(P1), pages 650-663.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p1:p:650-663
    DOI: 10.1016/j.apenergy.2016.10.120
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    10. Meena, Nand K. & Yang, Jin & Zacharis, Evan, 2019. "Optimisation framework for the design and operation of open-market urban and remote community microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Ren, Lingyu & Qin, Yanyuan & Li, Yan & Zhang, Peng & Wang, Bing & Luh, Peter B. & Han, Song & Orekan, Taofeek & Gong, Tao, 2018. "Enabling resilient distributed power sharing in networked microgrids through software defined networking," Applied Energy, Elsevier, vol. 210(C), pages 1251-1265.
    12. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    13. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
    14. Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
    15. Xiao, Zhao-xia & Guerrero, Josep M. & Shuang, Jia & Sera, Dezso & Schaltz, Erik & Vásquez, Juan C., 2018. "Flat tie-line power scheduling control of grid-connected hybrid microgrids," Applied Energy, Elsevier, vol. 210(C), pages 786-799.
    16. Zhou, Yutian & Panteli, Mathaios & Moreno, Rodrigo & Mancarella, Pierluigi, 2018. "System-level assessment of reliability and resilience provision from microgrids," Applied Energy, Elsevier, vol. 230(C), pages 374-392.
    17. Wu, Pan & Huang, Wentao & Tai, Nengling & Liang, Shuo, 2018. "A novel design of architecture and control for multiple microgrids with hybrid AC/DC connection," Applied Energy, Elsevier, vol. 210(C), pages 1002-1016.
    18. Thebelt, Alexander & Tsay, Calvin & Lee, Robert M. & Sudermann-Merx, Nathan & Walz, David & Tranter, Tom & Misener, Ruth, 2022. "Multi-objective constrained optimization for energy applications via tree ensembles," Applied Energy, Elsevier, vol. 306(PB).
    19. 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.
    20. Li, Yan & Zhang, Peng & Yue, Meng, 2018. "Networked microgrid stability through distributed formal analysis," Applied Energy, Elsevier, vol. 228(C), pages 279-288.
    21. Lin, Yanling & Bie, Zhaohong, 2018. "Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding," Applied Energy, Elsevier, vol. 210(C), pages 1266-1279.
    22. Salem Alkhalaf & Tomonobu Senjyu & Ayat Ali Saleh & Ashraf M. Hemeida & Al-Attar Ali Mohamed, 2019. "A MODA and MODE Comparison for Optimal Allocation of Distributed Generations with Different Load Levels," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
    23. Mousavizadeh, Saeed & Haghifam, Mahmoud-Reza & Shariatkhah, Mohammad-Hossein, 2018. "A linear two-stage method for resiliency analysis in distribution systems considering renewable energy and demand response resources," Applied Energy, Elsevier, vol. 211(C), pages 443-460.
    24. Villanueva-Rosario, Junior Alexis & Santos-García, Félix & Aybar-Mejía, Miguel Euclides & Mendoza-Araya, Patricio & Molina-García, Angel, 2022. "Coordinated ancillary services, market participation and communication of multi-microgrids: A review," Applied Energy, Elsevier, vol. 308(C).
    25. Lazo, Joaquín & Watts, David, 2024. "Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).

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