IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i11p3188-d565183.html
   My bibliography  Save this article

Optimal Planning of Integrated Nuclear-Renewable Energy System for Marine Ships Using Artificial Intelligence Algorithm

Author

Listed:
  • Hossam A. Gabbar

    (Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada
    Faculty of Engineering and Applied Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada)

  • Md. Ibrahim Adham

    (Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada)

  • Muhammad R. Abdussami

    (Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1G 0C5, Canada)

Abstract

Ocean-going ships are one of the primary sources of Greenhouse Gas (GHG) emissions. Several actions are being taken to reduce the GHG emissions from maritime vessels, and integration of Renewable Energy Sources (RESs) is one of them. Ocean-going marine ships need a large amount of reliable energy to support the propulsive load. Intermittency is one of the drawbacks of RESs, and penetration of RESs in maritime vessels is limited by the cargo carrying capacity and usable area of that ship. Other types of reliable energy sources need to be incorporated in ships to overcome these shortcomings of RESs. Some researchers proposed to integrate fossil fuel-based generators like diesel generators and renewable energy in marine vessels to reduce GHG emissions. As the penetration of RESs in marine ships is limited, fossil fuel-based generators provide most of the energy. Therefore, renewable and fossil fuel-based hybrid energy systems in maritime vessels can not reduce GHG emissions to the desired level. Fossil fuel-based generators need to be replaced by emissions-free energy sources to make marine ships free from emissions. Nuclear energy is emissions-free energy, and small-scale nuclear reactors like Microreactors (MRs) are competent to replace fossil fuel-based generators. In this paper, the technical, environmental, and economic competitiveness of Nuclear-Renewable Hybrid Energy Systems (N-R HES) in marine ships are assessed. The lifecycle cost of MR, reliability of the proposed system, and limitations of integrating renewable energy in maritime vessels are considered in this study. The proposed N-R HES is compared with three different energy systems, namely ‘Standalone Fossil Fuel-based Energy Systems’, ‘Renewable and Fossil Fuel-based Hybrid Energy Systems’, and ‘Standalone Nuclear Energy System’. The cost modeling of each energy system is carried out in MATLAB simulator. Each energy system is optimized by using the Differential Evolution Algorithm (DEA), an artificial intelligence algorithm, to find out the optimal configuration of the system components in terms of Net Present Cost (NPC). The results determine that N-R HES has the lowest NPC compared to the other three energy systems. The performance of the DE algorithm is compared with another widely accepted artificial intelligence optimization technique called ‘Particle Swarm Optimization (PSO)’ to validate the findings of the DE algorithm. The impact of control parameters in the DE algorithm is assessed by employing the Adaptive Differential Evolution (ADE) algorithm. A sensitivity analysis is carried out to assess the impact of different system parameters on this study’s findings.

Suggested Citation

  • Hossam A. Gabbar & Md. Ibrahim Adham & Muhammad R. Abdussami, 2021. "Optimal Planning of Integrated Nuclear-Renewable Energy System for Marine Ships Using Artificial Intelligence Algorithm," Energies, MDPI, vol. 14(11), pages 1-39, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3188-:d:565183
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/11/3188/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/11/3188/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heydari, Ali & Askarzadeh, Alireza, 2016. "Optimization of a biomass-based photovoltaic power plant for an off-grid application subject to loss of power supply probability concept," Applied Energy, Elsevier, vol. 165(C), pages 601-611.
    2. Yang, H.X. & Lu, L. & Burnett, J., 2003. "Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong," Renewable Energy, Elsevier, vol. 28(11), pages 1813-1824.
    3. Diaf, S. & Notton, G. & Belhamel, M. & Haddadi, M. & Louche, A., 2008. "Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions," Applied Energy, Elsevier, vol. 85(10), pages 968-987, October.
    4. Hossam A. Gabbar & Muhammad R. Abdussami & Md. Ibrahim Adham, 2020. "Techno-Economic Evaluation of Interconnected Nuclear-Renewable Micro Hybrid Energy Systems with Combined Heat and Power," Energies, MDPI, vol. 13(7), pages 1-29, April.
    5. Lu, Lin & Yang, Hongxing & Burnett, John, 2002. "Investigation on wind power potential on Hong Kong islands—an analysis of wind power and wind turbine characteristics," Renewable Energy, Elsevier, vol. 27(1), pages 1-12.
    6. Diab, Fahd & Lan, Hai & Ali, Salwa, 2016. "Novel comparison study between the hybrid renewable energy systems on land and on ship," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 452-463.
    7. Piotr F. Borowski, 2020. "Zonal and Nodal Models of Energy Market in European Union," Energies, MDPI, vol. 13(16), pages 1-21, August.
    8. Hossam A. Gabbar & Muhammad R. Abdussami & Md. Ibrahim Adham, 2020. "Micro Nuclear Reactors: Potential Replacements for Diesel Gensets within Micro Energy Grids," Energies, MDPI, vol. 13(19), pages 1-38, October.
    9. Luo, Xing & Wang, Jihong & Dooner, Mark & Clarke, Jonathan, 2015. "Overview of current development in electrical energy storage technologies and the application potential in power system operation," Applied Energy, Elsevier, vol. 137(C), pages 511-536.
    10. Borhanazad, Hanieh & Mekhilef, Saad & Gounder Ganapathy, Velappa & Modiri-Delshad, Mostafa & Mirtaheri, Ali, 2014. "Optimization of micro-grid system using MOPSO," Renewable Energy, Elsevier, vol. 71(C), pages 295-306.
    11. Yuan, Jun & Ng, Szu Hui & Sou, Weng Sut, 2016. "Uncertainty quantification of CO2 emission reduction for maritime shipping," Energy Policy, Elsevier, vol. 88(C), pages 113-130.
    12. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    13. Lan, Hai & Wen, Shuli & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun, 2015. "Optimal sizing of hybrid PV/diesel/battery in ship power system," Applied Energy, Elsevier, vol. 158(C), pages 26-34.
    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. Pablo Fernández-Arias & Diego Vergara & Álvaro Antón-Sancho, 2023. "Bibliometric Review and Technical Summary of PWR Small Modular Reactors," Energies, MDPI, vol. 16(13), pages 1-15, July.

    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. Hossam A. Gabbar & Muhammad R. Abdussami & Md. Ibrahim Adham, 2020. "Micro Nuclear Reactors: Potential Replacements for Diesel Gensets within Micro Energy Grids," Energies, MDPI, vol. 13(19), pages 1-38, October.
    2. Kaabeche, A. & Belhamel, M. & Ibtiouen, R., 2011. "Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system," Energy, Elsevier, vol. 36(2), pages 1214-1222.
    3. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    4. Sawle, Yashwant & Gupta, S.C. & Bohre, Aashish Kumar, 2018. "Review of hybrid renewable energy systems with comparative analysis of off-grid hybrid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2217-2235.
    5. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    6. Anoune, Kamal & Bouya, Mohsine & Astito, Abdelali & Abdellah, Abdellatif Ben, 2018. "Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 652-673.
    7. Zhou, Wei & Lou, Chengzhi & Li, Zhongshi & Lu, Lin & Yang, Hongxing, 2010. "Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems," Applied Energy, Elsevier, vol. 87(2), pages 380-389, February.
    8. Paliwal, Priyanka & Patidar, N.P. & Nema, R.K., 2014. "Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization," Renewable Energy, Elsevier, vol. 63(C), pages 194-204.
    9. Abu Bakar Siddique & Hossam A. Gabbar, 2023. "Adaptive Mixed-Integer Linear Programming-Based Energy Management System of Fast Charging Station with Nuclear–Renewable Hybrid Energy System," Energies, MDPI, vol. 16(2), pages 1-22, January.
    10. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2013. "A review of photovoltaic systems size optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 454-465.
    11. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Cheng, Shih-Ping & Sun, Li-Chung & Kuo, Shye-Chorng, 2014. "Comparative analysis on power curve models of wind turbine generator in estimating capacity factor," Energy, Elsevier, vol. 73(C), pages 88-95.
    12. Yuan, Yupeng & Wang, Jixiang & Yan, Xinping & Shen, Boyang & Long, Teng, 2020. "A review of multi-energy hybrid power system for ships," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    13. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    14. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    15. Han, Xiaojuan & Zhang, Hua & Yu, Xiaoling & Wang, Lina, 2016. "Economic evaluation of grid-connected micro-grid system with photovoltaic and energy storage under different investment and financing models," Applied Energy, Elsevier, vol. 184(C), pages 103-118.
    16. Tezer, Tuba & Yaman, Ramazan & Yaman, Gülşen, 2017. "Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 840-853.
    17. Yuan, Yupeng & Wang, Jixiang & Yan, Xinping & Li, Qing & Long, Teng, 2018. "A design and experimental investigation of a large-scale solar energy/diesel generator powered hybrid ship," Energy, Elsevier, vol. 165(PA), pages 965-978.
    18. Diaf, S. & Notton, G. & Belhamel, M. & Haddadi, M. & Louche, A., 2008. "Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions," Applied Energy, Elsevier, vol. 85(10), pages 968-987, October.
    19. Zhu, Jianyun & Chen, Li & Wang, Xuefeng & Yu, Long, 2020. "Bi-level optimal sizing and energy management of hybrid electric propulsion systems," Applied Energy, Elsevier, vol. 260(C).
    20. Hong, Ying-Yi & Beltran, Angelo A. & Paglinawan, Arnold C., 2018. "A robust design of maximum power point tracking using Taguchi method for stand-alone PV system," Applied Energy, Elsevier, vol. 211(C), pages 50-63.

    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:14:y:2021:i:11:p:3188-:d:565183. 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.