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Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm

Author

Listed:
  • Mohd Bilal

    (Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India)

  • Ibrahim Alsaidan

    (Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia)

  • Muhannad Alaraj

    (Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia)

  • Fahad M. Almasoudi

    (Department of Electrical Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47913, Saudi Arabia)

  • Mohammad Rizwan

    (Department of Electrical Engineering, Delhi Technological University, Delhi 110042, India)

Abstract

The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO 2 , CO 2 . The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:924-:d:770656
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    as
    1. 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.
    2. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    3. Xiaoxue Zheng & Haiyan Lin & Zhi Liu & Dengfeng Li & Carlos Llopis-Albert & Shouzhen Zeng, 2018. "Manufacturing Decisions and Government Subsidies for Electric Vehicles in China: A Maximal Social Welfare Perspective," Sustainability, MDPI, vol. 10(3), pages 1-28, March.
    4. Amit Kumer Podder & Sayma Afroza Supti & Sayemul Islam & Maria Malvoni & Arunkumar Jayakumar & Sanchari Deb & Nallapaneni Manoj Kumar, 2021. "Feasibility Assessment of Hybrid Solar Photovoltaic-Biogas Generator Based Charging Station: A Case of Easy Bike and Auto Rickshaw Scenario in a Developing Nation," Sustainability, MDPI, vol. 14(1), pages 1-27, December.
    5. Sen, Souvik & Ganguly, Sourav, 2017. "Opportunities, barriers and issues with renewable energy development – A discussion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1170-1181.
    6. Qiongjie Dai & Jicheng Liu & Qiushuang Wei, 2019. "Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    7. Dominković, D.F. & Bačeković, I. & Pedersen, A.S. & Krajačić, G., 2018. "The future of transportation in sustainable energy systems: Opportunities and barriers in a clean energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P2), pages 1823-1838.
    8. Abdelkader, Abbassi & Rabeh, Abbassi & Mohamed Ali, Dami & Mohamed, Jemli, 2018. "Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage," Energy, Elsevier, vol. 163(C), pages 351-363.
    9. Bin Ye & Jingjing Jiang & Lixin Miao & Peng Yang & Ji Li & Bo Shen, 2015. "Feasibility Study of a Solar-Powered Electric Vehicle Charging Station Model," Energies, MDPI, vol. 8(11), pages 1-19, November.
    10. Hafez, Omar & Bhattacharya, Kankar, 2017. "Optimal design of electric vehicle charging stations considering various energy resources," Renewable Energy, Elsevier, vol. 107(C), pages 576-589.
    11. Chandra Mouli, G.R. & Bauer, P. & Zeman, M., 2016. "System design for a solar powered electric vehicle charging station for workplaces," Applied Energy, Elsevier, vol. 168(C), pages 434-443.
    12. Fares, Dalila & Fathi, Mohamed & Mekhilef, Saad, 2022. "Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system," Applied Energy, Elsevier, vol. 305(C).
    13. Al Busaidi, Ahmed Said & Kazem, Hussein A & Al-Badi, Abdullah H & Farooq Khan, Mohammad, 2016. "A review of optimum sizing of hybrid PV–Wind renewable energy systems in oman," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 185-193.
    14. Yue Zhang & Qi Zhang & Arash Farnoosh & Siyuan Chen & Yan Li, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Post-Print hal-02009151, HAL.
    15. Milad Akbari & Morris Brenna & Michela Longo, 2018. "Optimal Locating of Electric Vehicle Charging Stations by Application of Genetic Algorithm," Sustainability, MDPI, vol. 10(4), pages 1-14, April.
    16. Xu, Min & Meng, Qiang, 2020. "Optimal deployment of charging stations considering path deviation and nonlinear elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 135(C), pages 120-142.
    17. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
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    Cited by:

    1. 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.
    2. Mohd Bilal & Pitshou N. Bokoro & Gulshan Sharma, 2024. "Design and Development of Grid Connected Renewable Energy System for Electric Vehicle Loads in Taif, Kingdom of Saudi Arabia," Energies, MDPI, vol. 17(16), pages 1-36, August.
    3. Ibrahim Alsaidan & Mohd Bilal & Muhannad Alaraj & Mohammad Rizwan & Fahad M. Almasoudi, 2023. "A Novel EA-Based Techno–Economic Analysis of Charging System for Electric Vehicles: A Case Study of Qassim Region, Saudi Arabia," Mathematics, MDPI, vol. 11(9), pages 1-31, April.

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