IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i7p1741-d1116527.html
   My bibliography  Save this article

Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems

Author

Listed:
  • Nahar F. Alshammari

    (Department of Electrical Engineering, Faculty of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Mohamed Mahmoud Samy

    (Department of Electrical Engineering, Faculty of Engineering, Beni-Suef University, Beni-Suef 2722165, Egypt)

  • Shimaa Barakat

    (Department of Electrical Engineering, Faculty of Engineering, Beni-Suef University, Beni-Suef 2722165, Egypt)

Abstract

This study presents a multi-objective optimization approach for designing hybrid renewable energy systems for electric vehicle (EV) charging stations that considers both economic and reliability factors as well as seasonal variations in energy production and consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, and MOEA/D, were evaluated in terms of their convergence, diversity, efficiency, and robustness. Unlike previous studies that focused on single-objective optimization or ignored seasonal variations, our approach results in a more comprehensive and sustainable design for EV charging systems. The proposed system includes a 223-kW photovoltaic system, an 80-kW wind turbine, and seven Lithium-Ion battery banks, achieving a total net present cost of USD 564,846, a levelized cost of electricity of 0.2521 USD/kWh, and a loss of power supply probability of 1.21%. NSGA-II outperforms the other algorithms in terms of convergence and diversity, while NSGA-III is the most efficient, and MOEA/D has the highest robustness. The findings contribute to the development of efficient and reliable renewable energy systems for urban areas, emphasizing the importance of considering both economic and reliability factors in the design process. Our study represents a significant advance in the field of hybrid renewable energy systems for EV charging stations.

Suggested Citation

  • Nahar F. Alshammari & Mohamed Mahmoud Samy & Shimaa Barakat, 2023. "Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems," Mathematics, MDPI, vol. 11(7), pages 1-31, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1741-:d:1116527
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/7/1741/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/7/1741/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Florentina Magda Enescu & Fernando Georgel Birleanu & Maria Simona Raboaca & Mircea Raceanu & Nicu Bizon & Phatiphat Thounthong, 2023. "Electric Vehicle Charging Station Based on Photovoltaic Energy with or without the Support of a Fuel Cell–Electrolyzer Unit," Energies, MDPI, vol. 16(2), pages 1-19, January.
    2. Claude Ziad El-Bayeh & Mohamed Zellagui & Navid Shirzadi & Ursula Eicker, 2021. "A Novel Optimization Algorithm for Solar Panels Selection towards a Self-Powered EV Parking Lot and Its Impact on the Distribution System," Energies, MDPI, vol. 14(15), pages 1-28, July.
    3. Ehrgott, Matthias & Ide, Jonas & Schöbel, Anita, 2014. "Minmax robustness for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 239(1), pages 17-31.
    4. Schöbel, Anita & Zhou-Kangas, Yue, 2021. "The price of multiobjective robustness: Analyzing solution sets to uncertain multiobjective problems," European Journal of Operational Research, Elsevier, vol. 291(2), pages 782-793.
    5. Al Wahedi, Abdulla & Bicer, Yusuf, 2022. "Techno-economic optimization of novel stand-alone renewables-based electric vehicle charging stations in Qatar," Energy, Elsevier, vol. 243(C).
    6. Shubham Mishra & Shrey Verma & Subhankar Chowdhury & Ambar Gaur & Subhashree Mohapatra & Gaurav Dwivedi & Puneet Verma, 2021. "A Comprehensive Review on Developments in Electric Vehicle Charging Station Infrastructure and Present Scenario of India," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    7. Shaukat, N. & Khan, B. & Ali, S.M. & Mehmood, C.A. & Khan, J. & Farid, U. & Majid, M. & Anwar, S.M. & Jawad, M. & Ullah, Z., 2018. "A survey on electric vehicle transportation within smart grid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1329-1349.
    8. Vitaliy Feoktistov, 2006. "Differential Evolution," Springer Optimization and Its Applications, Springer, number 978-0-387-36896-2, September.
    9. Bastida-Molina, Paula & Hurtado-Pérez, Elías & Moros Gómez, María Cristina & Vargas-Salgado, Carlos, 2021. "Multicriteria power generation planning and experimental verification of hybrid renewable energy systems for fast electric vehicle charging stations," Renewable Energy, Elsevier, vol. 179(C), pages 737-755.
    10. Mohd Bilal & Ibrahim Alsaidan & Muhannad Alaraj & Fahad M. Almasoudi & Mohammad Rizwan, 2022. "Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-40, March.
    11. Taboada, Heidi A. & Baheranwala, Fatema & Coit, David W. & Wattanapongsakorn, Naruemon, 2007. "Practical solutions for multi-objective optimization: An application to system reliability design problems," Reliability Engineering and System Safety, Elsevier, vol. 92(3), pages 314-322.
    12. Kabli, Mohannad & Quddus, Md Abdul & Nurre, Sarah G. & Marufuzzaman, Mohammad & Usher, John M., 2020. "A stochastic programming approach for electric vehicle charging station expansion plans," International Journal of Production Economics, Elsevier, vol. 220(C).
    13. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    14. Suresh Chavhan & Subhi R. M. Zeebaree & Ahmed Alkhayyat & Sachin Kumar, 2022. "Design of Space Efficient Electric Vehicle Charging Infrastructure Integration Impact on Power Grid Network," Mathematics, MDPI, vol. 10(19), pages 1-20, September.
    15. Malheiro, André & Castro, Pedro M. & Lima, Ricardo M. & Estanqueiro, Ana, 2015. "Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems," Renewable Energy, Elsevier, vol. 83(C), pages 646-657.
    16. Ali Q. Al-Shetwi & Walid K. Issa & Raed F. Aqeil & Taha Selim Ustun & Hussein M. K. Al-Masri & Khaled Alzaareer & Maher G. M. Abdolrasol & Majid A. Abdullah, 2022. "Active Power Control to Mitigate Frequency Deviations in Large-Scale Grid-Connected PV System Using Grid-Forming Single-Stage Inverters," Energies, MDPI, vol. 15(6), pages 1-21, March.
    17. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
    18. Diaf, S. & Diaf, D. & Belhamel, M. & Haddadi, M. & Louche, A., 2007. "A methodology for optimal sizing of autonomous hybrid PV/wind system," Energy Policy, Elsevier, vol. 35(11), pages 5708-5718, November.
    19. Javed, Muhammad Shahzad & Song, Aotian & Ma, Tao, 2019. "Techno-economic assessment of a stand-alone hybrid solar-wind-battery system for a remote island using genetic algorithm," Energy, Elsevier, vol. 176(C), pages 704-717.
    20. Tamiz, Mehrdad & Jones, Dylan & Romero, Carlos, 1998. "Goal programming for decision making: An overview of the current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 111(3), pages 569-581, December.
    21. Fabio Cazzato & Marco Di Clerico & Maria Carmen Falvo & Simone Ferrero & Marco Vivian, 2020. "New Dispatching Paradigm in Power Systems Including EV Charging Stations and Dispersed Generation: A Real Test Case," Energies, MDPI, vol. 13(4), pages 1-13, February.
    22. Goli, P. & Shireen, W., 2014. "PV powered smart charging station for PHEVs," Renewable Energy, Elsevier, vol. 66(C), pages 280-287.
    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. Hang Xu & Chaohui Huang & Hui Wen & Tao Yan & Yuanmo Lin & Ying Xie, 2024. "A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification," Mathematics, MDPI, vol. 12(4), pages 1-24, 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. Selçuklu, Saltuk Buğra & Coit, David W. & Felder, Frank A., 2020. "Pareto uncertainty index for evaluating and comparing solutions for stochastic multiple objective problems," European Journal of Operational Research, Elsevier, vol. 284(2), pages 644-659.
    2. Qi, Yue & Liao, Kezhi & Liu, Tongyang & Zhang, Yu, 2022. "Originating multiple-objective portfolio selection by counter-COVID measures and analytically instigating robust optimization by mean-parameterized nondominated paths," Operations Research Perspectives, Elsevier, vol. 9(C).
    3. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Mikulik, Jerzy, 2021. "A hybrid method for scenario-based techno-economic-environmental analysis of off-grid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    4. Goberna, M.A. & Jeyakumar, V. & Li, G. & Vicente-Pérez, J., 2015. "Robust solutions to multi-objective linear programs with uncertain data," European Journal of Operational Research, Elsevier, vol. 242(3), pages 730-743.
    5. Kuhn, K. & Raith, A. & Schmidt, M. & Schöbel, A., 2016. "Bi-objective robust optimisation," European Journal of Operational Research, Elsevier, vol. 252(2), pages 418-431.
    6. Mavrotas, George & Figueira, José Rui & Siskos, Eleftherios, 2015. "Robustness analysis methodology for multi-objective combinatorial optimization problems and application to project selection," Omega, Elsevier, vol. 52(C), pages 142-155.
    7. Jonas Ide & Elisabeth Köbis, 2014. "Concepts of efficiency for uncertain multi-objective optimization problems based on set order relations," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 80(1), pages 99-127, August.
    8. Jonas Ide & Anita Schöbel, 2016. "Robustness for uncertain multi-objective optimization: a survey and analysis of different concepts," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(1), pages 235-271, January.
    9. Klamroth, Kathrin & Köbis, Elisabeth & Schöbel, Anita & Tammer, Christiane, 2017. "A unified approach to uncertain optimization," European Journal of Operational Research, Elsevier, vol. 260(2), pages 403-420.
    10. 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.
    11. Jornada, Daniel & Leon, V. Jorge, 2016. "Biobjective robust optimization over the efficient set for Pareto set reduction," European Journal of Operational Research, Elsevier, vol. 252(2), pages 573-586.
    12. Erin K. Doolittle & Hervé L. M. Kerivin & Margaret M. Wiecek, 2018. "Robust multiobjective optimization with application to Internet routing," Annals of Operations Research, Springer, vol. 271(2), pages 487-525, December.
    13. Hemant Kumar & Shiv Prasad Yadav, 2019. "Fuzzy rule-based reliability analysis using NSGA-II," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 953-972, October.
    14. Zhang, X. & Liu, X., 2022. "A two-stage robust model for express service network design with surging demand," European Journal of Operational Research, Elsevier, vol. 299(1), pages 154-167.
    15. Schöbel, Anita & Zhou-Kangas, Yue, 2021. "The price of multiobjective robustness: Analyzing solution sets to uncertain multiobjective problems," European Journal of Operational Research, Elsevier, vol. 291(2), pages 782-793.
    16. Chedid, Riad & Sawwas, Ahmad & Fares, Dima, 2020. "Optimal design of a university campus micro-grid operating under unreliable grid considering PV and battery storage," Energy, Elsevier, vol. 200(C).
    17. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid," Applied Energy, Elsevier, vol. 190(C), pages 232-248.
    18. Javed, Muhammad Shahzad & Ma, Tao & Jurasz, Jakub & Ahmed, Salman & Mikulik, Jerzy, 2020. "Performance comparison of heuristic algorithms for optimization of hybrid off-grid renewable energy systems," Energy, Elsevier, vol. 210(C).
    19. Knoke, Thomas & Paul, Carola & Härtl, Fabian & Castro, Luz Maria & Calvas, Baltazar & Hildebrandt, Patrick, 2015. "Optimizing agricultural land-use portfolios with scarce data—A non-stochastic model," Ecological Economics, Elsevier, vol. 120(C), pages 250-259.
    20. Raith, Andrea & Schmidt, Marie & Schöbel, Anita & Thom, Lisa, 2018. "Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty," European Journal of Operational Research, Elsevier, vol. 267(2), pages 628-642.

    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:jmathe:v:11:y:2023:i:7:p:1741-:d:1116527. 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.