IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i10p3983-d1142667.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/10/3983/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/10/3983/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Senthil Kumar, J. & Charles Raja, S. & Jeslin Drusila Nesamalar, J. & Venkatesh, P., 2018. "Optimizing renewable based generations in AC/DC microgrid system using hybrid Nelder-Mead – Cuckoo Search algorithm," Energy, Elsevier, vol. 158(C), pages 204-215.
    2. Mohammadali Kiehbadroudinezhad & Adel Merabet & Ahmed G. Abo-Khalil & Tareq Salameh & Chaouki Ghenai, 2022. "Intelligent and Optimized Microgrids for Future Supply Power from Renewable Energy Resources: A Review," Energies, MDPI, vol. 15(9), pages 1-21, May.
    3. Longda Wang & Xingcheng Wang & Zhao Sheng & Senkui Lu, 2020. "Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation," Energies, MDPI, vol. 13(3), pages 1-25, February.
    4. Mahmoud A. Ali & Salah Kamel & Mohamed H. Hassan & Emad M. Ahmed & Mohana Alanazi, 2022. "Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    5. 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.
    6. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2016. "Ant Lion Optimization Algorithm for Renewable Distributed Generations," Energy, Elsevier, vol. 116(P1), pages 445-458.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    2. Mohamed A. Elseify & Salah Kamel & Hussein Abdel-Mawgoud & Ehab E. Elattar, 2022. "A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity," Mathematics, MDPI, vol. 10(12), pages 1-26, June.
    3. 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, vol. 10(12), pages 1-18, December.
    4. Chandrasekaran Venkatesan & Raju Kannadasan & Mohammed H. Alsharif & Mun-Kyeom Kim & Jamel Nebhen, 2021. "A Novel Multiobjective Hybrid Technique for Siting and Sizing of Distributed Generation and Capacitor Banks in Radial Distribution Systems," Sustainability, MDPI, vol. 13(6), pages 1-34, March.
    5. Andrzej Ożadowicz & Gabriela Walczyk, 2023. "Energy Performance and Control Strategy for Dynamic Façade with Perovskite PV Panels—Technical Analysis and Case Study," Energies, MDPI, vol. 16(9), pages 1-23, April.
    6. Ghiasi, Mohammad, 2019. "Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources," Energy, Elsevier, vol. 169(C), pages 496-507.
    7. Andrzej Ożadowicz, 2023. "Technical, Qualitative and Energy Analysis of Wireless Control Modules for Distributed Smart Home Systems," Future Internet, MDPI, vol. 15(9), pages 1-21, September.
    8. Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    9. Ahmed G. Abo-Khalil & Mohammad Alobaid, 2023. "A Guide to the Integration and Utilization of Energy Storage Systems with a Focus on Demand Resource Management and Power Quality Enhancement," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    10. Ganesh Sampatrao Patil & Anwar Mulla & Taha Selim Ustun, 2022. "Impact of Wind Farm Integration on LMP in Deregulated Energy Markets," Sustainability, MDPI, vol. 14(7), pages 1-20, April.
    11. Kiehbadroudinezhad, Mohammadali & Hosseinzadeh-Bandbafha, Homa & Varjani, Sunita & Wang, Yajing & Peng, Wanxi & Pan, Junting & Aghbashlo, Mortaza & Tabatabaei, Meisam, 2023. "Marine shell-based biorefinery: A sustainable solution for aquaculture waste valorization," Renewable Energy, Elsevier, vol. 206(C), pages 623-634.
    12. Feng, Li & Liu, Jiajun & Lu, Haitao & Liu, Bingzhi & Chen, Yuning & Wu, Shenyu, 2022. "Robust operation of distribution network based on photovoltaic/wind energy resources in condition of COVID-19 pandemic considering deterministic and probabilistic approaches," Energy, Elsevier, vol. 261(PB).
    13. Ayman Awad & Hussein Abdel-Mawgoud & Salah Kamel & Abdalla A. Ibrahim & Francisco Jurado, 2021. "Developing a Hybrid Optimization Algorithm for Optimal Allocation of Renewable DGs in Distribution Network," Clean Technol., MDPI, vol. 3(2), pages 1-15, May.
    14. Ossai, Chinedu I., 2017. "Optimal renewable energy generation – Approaches for managing ageing assets mechanisms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 269-280.
    15. Farhad Zishan & Saeedeh Mansouri & Farzad Abdollahpour & Luis Fernando Grisales-Noreña & Oscar Danilo Montoya, 2023. "Allocation of Renewable Energy Resources in Distribution Systems While considering the Uncertainty of Wind and Solar Resources via the Multi-Objective Salp Swarm Algorithm," Energies, MDPI, vol. 16(1), pages 1-17, January.
    16. Lu, Peng & Ye, Lin & Zhao, Yongning & Dai, Binhua & Pei, Ming & Tang, Yong, 2021. "Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges," Applied Energy, Elsevier, vol. 301(C).
    17. Saman Shahrokhi & Adel El-Shahat & Fatemeh Masoudinia & Foad H. Gandoman & Shady H. E. Abdel Aleem, 2021. "Sizing and Energy Management of Parking Lots of Electric Vehicles Based on Battery Storage with Wind Resources in Distribution Network," Energies, MDPI, vol. 14(20), pages 1-21, October.
    18. Junting Wu & Yingjin Zhang & Kanglong Sun & Qicheng Chen, 2022. "Heat Transfer Enhancement of Phase Change Material in Triple-Tube Latent Heat Thermal Energy Storage Units: Operating Modes and Fin Configurations," Energies, MDPI, vol. 15(15), pages 1-26, August.
    19. Hale Bakır & Adel Merabet & Mohammadali Kiehbadroudinezhad, 2023. "Optimized Control of a Hybrid Water Pumping System Integrated with Solar Photovoltaic and Battery Storage: Towards Sustainable and Green Water-Power Supply," Energies, MDPI, vol. 16(13), pages 1-16, July.
    20. Mohamed Tolba & Hegazy Rezk & Ahmed A. Zaki Diab & Mujahed Al-Dhaifallah, 2018. "A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids," Energies, MDPI, vol. 11(10), pages 1-34, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:3983-:d:1142667. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.