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Network Reconfiguration Management in Intelligent Distribution System Taking into Account PV Production Variation Using Grey Wolf Optimizer

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Artificial Intelligence and Renewables Towards an Energy Transition (ICAIRES 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 174))

Abstract

The management of modern distribution networks or smart grids needs the integration of new technologies and advanced software. This paper proposes the Grey Wolf Optimizer or GWO technique to determine the optimal configuration of the network in real-time in the presence of photovoltaic sources. This technique is able to establish the state of the looping switches during each hour and this according to the variation of the power load and the photovoltaic power produced. The goal function chosen is to minimize the active losses under the imposed constraints. This study was applied on a practical network, namely the Algerian distribution network under MATLAB code. The combined results showed the efficiency and robustness of the proposed technique.

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Author information

Authors and Affiliations

  1. LACoSERE, Electrical Engineering Department, University of Amar Telidji, Laghouat, Algeria

    Mustafa Mosbah & Salem Arif

  2. School of Science and Engineering, Al Akhawayn University in Ifrane, Ifrane, Morocco

    Rabie Zine

  3. Unité-de Développement des Equipements Solaires, Bou Ismaïl, Tipaza, Algeria

    Mustapha Hatti

  4. Unité de Recherche Appliquée en Energies Renouvelables, Ghardaia, Algeria

    Samir Hamid-Oudjana

Authors
  1. Mustafa Mosbah
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  2. Rabie Zine
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  3. Mustapha Hatti
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  4. Samir Hamid-Oudjana
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  5. Salem Arif
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Corresponding author

Correspondence to Mustafa Mosbah .

Editor information

Editors and Affiliations

  1. EPST-CDER, Unité de Développement des Equipements Solaires, Bou Ismaïl, Algeria

    Mustapha Hatti

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Mosbah, M., Zine, R., Hatti, M., Hamid-Oudjana, S., Arif, S. (2021). Network Reconfiguration Management in Intelligent Distribution System Taking into Account PV Production Variation Using Grey Wolf Optimizer. In: Hatti, M. (eds) Artificial Intelligence and Renewables Towards an Energy Transition. ICAIRES 2020. Lecture Notes in Networks and Systems, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-63846-7_11

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