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Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems

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  • Ehab S. Ali

    (Electrical Engineering Department, Faculty of Engineering, Jazan University, Jazan 45142, Saudi Arabia
    Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Sahar. M. Abd Elazim

    (Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
    Computer Science Department, Faculty of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi Arabia)

  • Sultan H. Hakmi

    (Electrical Engineering Department, Faculty of Engineering, Jazan University, Jazan 45142, Saudi Arabia)

  • Mohamed I. Mosaad

    (Electrical & Electronics Engineering Technology Department, Royal Commission Yanbu Colleges & Institutes, Yanbu Industrial City 46452, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Damietta University, Damietta 34511, Egypt)

Abstract

The need for energy has significantly increased in the world in recent years. Various research works were presented to develop Renewable Energy Sources (RESs) as green energy Distributed Generations (DGs) to satisfy this demand. In addition, alleviating environmental problems caused by utilizing conventional power plants is diminished by these renewable sources. The optimal location and size of the DG-RESs significantly affect the performance of Radial Distribution Systems (RDSs) through the fine bus voltage profile, senior power quality, low power losses, and high efficiency. This paper investigates the use of PV (photovoltaic) and (Wind Turbine) WT systems as a DG source in RDSs. This investigation is presented via the optimal location and size of the PV and WT systems, which are the most used DG sources. This optimization problem aims to maximize system efficiency by minimizing power losses and improving both voltage profile and power quality using White Shark Optimization (WSO). This algorithm emulates the attitude of great white sharks when foraging using their senses of hearing and smell. It confirms the balance between exploration and exploitation to discover optimization that is considered as the main advantage of this approach in attaining the global minimum. To assess the suggested approach, three common RDSs are utilized, namely, IEEE 33, 69, and 85 node systems. The results prove that the applied WSO approach can find the best location and size of the RESs to reduce power loss, ameliorate the voltage profile, and outlast other recent strategies. Adding more units provides a high percentage of reducing losses by at least 93.52% in case of WTs, rather than 52.267% in the case of PVs. Additionally, the annual saving increased to USD 74,371.97, USD 82,127.257, and USD 86,731.16 with PV penetration, while it reached USD 104,872.96, USD 116,136.57, and USD 155,184.893 with WT penetration for the 33, 69, and 85 nodes, respectively. In addition, a considerable enhancement in the voltage profiles with the growth of PV and WT units was confirmed. The ability of the suggested WSO for feasible implementation was validated and inspected by preserving the restrictions and working constraints.

Suggested Citation

  • Ehab S. Ali & Sahar. M. Abd Elazim & Sultan H. Hakmi & Mohamed I. Mosaad, 2023. "Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems," Energies, MDPI, vol. 16(10), pages 1-27, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:3983-:d:1142667
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    References listed on IDEAS

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    1. Matheus Diniz Gonçalves-Leite & Edgar Manuel Carreño-Franco & Jesús M. López-Lezama, 2023. "Impact of Distributed Generation on the Effectiveness of Electric Distribution System Reconfiguration," Energies, MDPI, vol. 16(17), pages 1-20, August.
    2. Samson Oladayo Ayanlade & Funso Kehinde Ariyo & Abdulrasaq Jimoh & Kayode Timothy Akindeji & Adeleye Oluwaseye Adetunji & Emmanuel Idowu Ogunwole & Dolapo Eniola Owolabi, 2023. "Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks," Sustainability, MDPI, vol. 15(18), pages 1-26, September.
    3. Idris H. Smaili & Dhaifallah R. Almalawi & Abdullah M. Shaheen & Hany S. E. Mansour, 2024. "Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm," Mathematics, MDPI, vol. 12(5), pages 1-22, February.

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