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An Optimized Framework for Energy Management of Multi-Microgrid Systems

Author

Listed:
  • Komal Naz

    (U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), Department of Electrical Engineering (Power), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Fasiha Zainab

    (U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), Department of Electrical Engineering (Power), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Khawaja Khalid Mehmood

    (Department of Electrical Engineering, The University of Azad Jammu & Kashmir, Muzaffarabad 13100, Pakistan)

  • Syed Basit Ali Bukhari

    (Department of Electrical Engineering, The University of Azad Jammu & Kashmir, Muzaffarabad 13100, Pakistan)

  • Hassan Abdullah Khalid

    (U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), Department of Electrical Engineering (Power), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan)

  • Chul-Hwan Kim

    (College of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 440-746, Korea)

Abstract

Regarding different challenges, such as integration of green energy and autonomy of microgrid (MG) in the multi-microgrid (MMG) system, this paper presents an optimized and coordinated strategy for energy management of MMG systems that consider multiple scenarios of MGs. The proposed strategy operates at two optimization levels: local and global. At an MG level, each energy management system satisfies its local demand by utilizing all available resources via local optimization, and only sends surplus/deficit energy data signals to MMG level, which enhances customer privacy. Thereafter, at an MMG level, a central energy management system performs global optimization and selects optimized options from the available resources, which include charging/discharging energy to/from the community battery energy storage system, selling/buying power to/from other MGs, and trading with the grid. Two types of loads are considered in this model: sensitive and non-sensitive. The algorithm tries to make the system reliable by avoiding utmost load curtailment and prefers to shed non-sensitive loads over sensitive loads in the case of load shedding. To verify the robustness of the proposed scheme, several test cases are generated by Monte Carlo Simulations and simulated on the IEEE 33-bus distribution system. The results show the effectiveness of the proposed model.

Suggested Citation

  • Komal Naz & Fasiha Zainab & Khawaja Khalid Mehmood & Syed Basit Ali Bukhari & Hassan Abdullah Khalid & Chul-Hwan Kim, 2021. "An Optimized Framework for Energy Management of Multi-Microgrid Systems," Energies, MDPI, vol. 14(19), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6012-:d:640368
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    References listed on IDEAS

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    1. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
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    Cited by:

    1. Matija Kostelac & Lin Herenčić & Tomislav Capuder, 2022. "Planning and Operational Aspects of Individual and Clustered Multi-Energy Microgrid Options," Energies, MDPI, vol. 15(4), pages 1-17, February.
    2. Tsoumpris, Charalampos & Theotokatos, Gerasimos, 2023. "A decision-making approach for the health-aware energy management of ship hybrid power plants," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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