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Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids

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  • El-Sharafy, M. Zaki
  • Farag, Hany E.Z.

Abstract

In this paper, an optimization problem is formulated for the automatic back-feed service restoration in smart distribution grids. The formulated problem relies on the structure of smart distribution grids, clustered into multi-microgrids, capable of operating in both grid-connected and islanded modes of operation. To that end, three types of power transfer between the neighboring microgrids, during the restoration processes are introduced: load transfer, distributed generation (DG) transfer, and combined load–DG transfer. The formulated optimization problem takes into account the ability of forming new, not predefined islanded microgrids, in the post-restoration configuration, to maximize service restoration. To obviate the need for a central unit, the optimization problem is reformulated, in this work, asa distributed constraint optimization problem, in which the variables and constraints are distributed among automated agents. To reduce the problem complexity, the restoration problem is decomposed into two sequential and interdependent distributed sub-problems: supply adequacy, and optimal reconfiguration. The proposed algorithm adopts the Optimal Asynchronous Partial Overlay (OPTAPO) technique, which is based on the distributed constraint agent search to solve distributed sub-problems in a multi-agent environment. Several case studies have been carried out to evaluate the effectiveness and robustness of the proposed algorithm.

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  • El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:1102-1117
    DOI: 10.1016/j.apenergy.2017.08.106
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    Cited by:

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    4. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    5. Shobole, Abdulfetah Abdela & Wadi, Mohammed, 2021. "Multiagent systems application for the smart grid protection," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. Cagnano, A. & De Tuglie, E. & Mancarella, P., 2020. "Microgrids: Overview and guidelines for practical implementations and operation," Applied Energy, Elsevier, vol. 258(C).
    7. Pascual, Julio & Arcos-Aviles, Diego & Ursúa, Alfredo & Sanchis, Pablo & Marroyo, Luis, 2021. "Energy management for an electro-thermal renewable–based residential microgrid with energy balance forecasting and demand side management," Applied Energy, Elsevier, vol. 295(C).
    8. 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.

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