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Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations

Author

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  • Ali, E.S.
  • Abd Elazim, S.M.
  • Abdelaziz, A.Y.

Abstract

Renewable sources can supply a clean and smart solution to the increased demands. Thus, Photovoltaic (PV) and Wind Turbine (WT) are taken here as resources of Distributed Generation (DG). Location and sizing of DG have affected largely on the system losses. In this paper, Ant Lion Optimization Algorithm (ALOA) is proposed for optimal location and sizing of DG based renewable sources for various distribution systems. First the most candidate buses for installing DG are introduced using Loss Sensitivity Factors (LSFs). Then the proposed ALOA is used to deduce the locations and sizing of DG from the elected buses. The proposed algorithm is tested on two IEEE radial distribution systems. The obtained results via the proposed algorithm are compared with other algorithms to highlight its benefits in decreasing total power losses and consequently increasing the net saving. Moreover, the results are presented to confirm the effectiveness of ALOA in enhancing the voltage profiles for different distribution systems and loading conditions. Also, the Wilcoxon test is performed to verify the superiority of ALOA.

Suggested Citation

  • Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2017. "Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations," Renewable Energy, Elsevier, vol. 101(C), pages 1311-1324.
  • Handle: RePEc:eee:renene:v:101:y:2017:i:c:p:1311-1324
    DOI: 10.1016/j.renene.2016.09.023
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    Citations

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    Cited by:

    1. Guido C. Guerrero-Liquet & Santiago Oviedo-Casado & J. M. Sánchez-Lozano & M. Socorro García-Cascales & Javier Prior & Antonio Urbina, 2018. "Determination of the Optimal Size of Photovoltaic Systems by Using Multi-Criteria Decision-Making Methods," Sustainability, MDPI, Open Access Journal, vol. 10(12), pages 1-18, December.
    2. Ben Hamida, Imen & Salah, Saoussen Brini & Msahli, Faouzi & Mimouni, Mohamed Faouzi, 2018. "Optimal network reconfiguration and renewable DG integration considering time sequence variation in load and DGs," Renewable Energy, Elsevier, vol. 121(C), pages 66-80.
    3. Ehsan, Ali & Yang, Qiang, 2018. "Optimal integration and planning of renewable distributed generation in the power distribution networks: A review of analytical techniques," Applied Energy, Elsevier, vol. 210(C), pages 44-59.
    4. Jingmin Wang & Wenhai Yang & Huaxin Cheng & Lingyu Huang & Yajing Gao, 2017. "The Optimal Configuration Scheme of the Virtual Power Plant Considering Benefits and Risks of Investors," Energies, MDPI, Open Access Journal, vol. 10(7), pages 1-12, July.
    5. Amirreza Naderipour & Zulkurnain Abdul-Malek & Saber Arabi Nowdeh & Foad H. Gandoman & Mohammad Jafar Hadidian Moghaddam, 2019. "A Multi-Objective Optimization Problem for Optimal Site Selection of Wind Turbines for Reduce Losses and Improve Voltage Profile of Distribution Grids," Energies, MDPI, Open Access Journal, vol. 12(13), pages 1-15, July.
    6. Luis Fernando Grisales-Noreña & Daniel Gonzalez Montoya & Carlos Andres Ramos-Paja, 2018. "Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques," Energies, MDPI, Open Access Journal, vol. 11(4), pages 1-27, April.
    7. Kazak, Jan & van Hoof, Joost & Szewranski, Szymon, 2017. "Challenges in the wind turbines location process in Central Europe – The use of spatial decision support systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 425-433.
    8. Dincer, Hasan & Yuksel, Serhat, 2019. "Balanced scorecard-based analysis of investment decisions for the renewable energy alternatives: A comparative analysis based on the hybrid fuzzy decision-making approach," Energy, Elsevier, vol. 175(C), pages 1259-1270.

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