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Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates

Author

Listed:
  • Alya AlHammadi

    (Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Nasser Al-Saif

    (Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Ameena Saad Al-Sumaiti

    (Advanced Power and Energy Center, Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates)

  • Mousa Marzband

    (Mathematics, Physics and Electrical Engineering Department, Northumbria New-Castle University, Newcastle Upon Tyne NE1 8ST, UK)

  • Tareefa Alsumaiti

    (Geography and Urban Sustainability Department, United Arab Emirates University, Alain P.O. Box 15551, United Arab Emirates)

  • Ehsan Heydarian-Forushani

    (Department of Electrical and Computer Engineering, Qom University of Technology, Qom 1519-37195, Iran)

Abstract

The United Arab Emirates is moving towards the use of renewable energy for many reasons, including the country’s high energy consumption, unstable oil prices, and increasing carbon dioxide emissions. The usage of electric vehicles can improve public health and reduce emissions that contribute to climate change. Thus, the usage of renewable energy resources to meet the demands of electric vehicles is the major challenge influencing the development of an optimal smart system that can satisfy energy requirements, enhance sustainability and reduce negative environmental impacts. The objective of this study was to examine different configurations of hybrid renewable energy systems for electric vehicle charging in Abu Dhabi city, UAE. A comprehensive study was conducted to investigate previous electric vehicle charging approaches and formulate the problem accordingly. Subsequently, methods for acquiring data with respect to the energy input and load profiles were determined, and a techno-economic analysis was performed using Hybrid Optimization of Multiple Energy Resources (HOMER) software. The results demonstrated that the optimal electric vehicle charging model comprising solar photovoltaics, wind turbines, batteries and a distribution grid was superior to the other studied configurations from the technical, economic and environmental perspectives. An optimal model could produce excess electricity of 22,006 kWh/year with an energy cost of 0.06743 USD/kWh. Furthermore, the proposed battery–grid–solar photovoltaics–wind turbine system had the highest renewable penetration and thus reduced carbon dioxide emissions by 384 tons/year. The results also indicated that the carbon credits associated with this system could result in savings of 8786.8 USD/year. This study provides new guidelines and identifies the best indicators for electric vehicle charging systems that will positively influence the trend in carbon dioxide emissions and achieve sustainable electricity generation. This study also provides a valid financial assessment for investors looking to encourage the use of renewable energy.

Suggested Citation

  • Alya AlHammadi & Nasser Al-Saif & Ameena Saad Al-Sumaiti & Mousa Marzband & Tareefa Alsumaiti & Ehsan Heydarian-Forushani, 2022. "Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates," Energies, MDPI, vol. 15(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6621-:d:911591
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    References listed on IDEAS

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    Cited by:

    1. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    2. Robert Michael Bridi & Marwa Ben Jabra & Naeema Al Hosani & Ahmed Hassan Almurshidi, 2024. "The Propensity to Adopt Electric Vehicles in the United Arab Emirates: An Analysis of Economic and Geographic Factors," Sustainability, MDPI, vol. 16(2), pages 1-19, January.
    3. José de Jesús Camacho & Bernabé Aguirre & Pedro Ponce & Brian Anthony & Arturo Molina, 2024. "Leveraging Artificial Intelligence to Bolster the Energy Sector in Smart Cities: A Literature Review," Energies, MDPI, vol. 17(2), pages 1-32, January.

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