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A Day-Ahead Energy Management for Multi MicroGrid System to Optimize the Energy Storage Charge and Grid Dependency—A Comparative Analysis

Author

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  • Saqib Iqbal

    (Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK)

  • Kamyar Mehran

    (Department of Electronic Engineering, Queen Mary University of London, London E1 4NS, UK)

Abstract

Microgrid (MG) is a combination of distributed generators (DGs), energy storage systems (ESSs), and loads connected to distribution network that can either be in islanded mode or grid-tied mode. Similarly, a multi-microgrid (MMG) system is a number of interconnected MGs connected with a larger and complex distribution network. Recently, the MMG energy management has created new challenges due to the inherent intermittency, uncertainty, and probabilistic nature of renewable based DGs output and varying load demands. To ensure the efficient operation and optimal energy management in the MMGs, this work proposes a two-stage, a day-ahead, simultaneous energy management strategy (EMS) of the MMG system as well as the MG system. At the first stage, each MG assumes a day-ahead predicted load demand and DGs output. At the second stage, through EMS, the energy scheduling, minimization of the main grid dependency, and maximization of the stored energy in the ESS are managed simultaneously. Four case studies are considered with four interconnected MGs with different DGs output and different initial state of charge (SOC) of ESS along with varying load demand. The proposed optimization model is formulated in the standard form using MATLAB OptimProblem, and compared with heuristic state flow-based EMS. Results show that the total grid dependency will be reduced to zero and ESS depth of discharge (DoD) will be increased up to 50% with the proposed optimization model.

Suggested Citation

  • Saqib Iqbal & Kamyar Mehran, 2022. "A Day-Ahead Energy Management for Multi MicroGrid System to Optimize the Energy Storage Charge and Grid Dependency—A Comparative Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4062-:d:829634
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    References listed on IDEAS

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    1. Seyed Reza Seyednouri & Amin Safari & Meisam Farrokhifar & Sajad Najafi Ravadanegh & Anas Quteishat & Mahmoud Younis, 2023. "Day-Ahead Scheduling of Multi-Energy Microgrids Based on a Stochastic Multi-Objective Optimization Model," Energies, MDPI, vol. 16(4), pages 1-17, February.

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