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Electric Vehicle Load Estimation at Home and Workplace in Saudi Arabia for Grid Planners and Policy Makers

Author

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  • Abdulaziz Almutairi

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi Arabia)

  • Naif Albagami

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi Arabia)

  • Sultanh Almesned

    (Department of Educational Sciences, College of Education, Majmaah University, Majmaah 11952, Saudi Arabia)

  • Omar Alrumayh

    (Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56453, Saudi Arabia)

  • Hasmat Malik

    (Department of Electrical Power Engineering, Faculty of Electrical Engineering, University Technology Malaysia (UTM), Skudai 81310, Malaysia
    Department of Electrical Engineering, Graphic Era Deemed to be University, Dehradun 248002, India)

Abstract

Electric vehicles (Evs) offer promising benefits in reducing emissions and enhancing energy security; however, accurately estimating their load presents a challenge in optimizing grid management and sustainable integration. Moreover, EV load estimation is context-specific, and generalized methods are inadequate. To address this, our study introduces a tailored three-step solution, focusing on the Middle East, specifically Saudi Arabia. Firstly, real survey data are employed to estimate driving patterns and commuting behaviors such as daily mileage, arrival/departure time at home and workplace, and trip mileage. Subsequently, per-unit profiles for homes and workplaces are formulated using these data and commercially available EV data, as these locations are preferred for charging by most EV owners. Finally, the developed profiles facilitate EV load estimations under various scenarios with differing charger ratios (L1 and L2) and building types (residential, commercial, mixed). Simulation outcomes reveal that while purely residential or commercial buildings lead to higher peak loads, mixed buildings prove advantageous in reducing the peak load of Evs. Especially, the ratio of commercial to residential usage of around 50% generates the lowest peak load, indicating an optimal balance. Such analysis aids grid operators and policymakers in load estimation and incentivizing EV-related infrastructure. This study, encompassing data from five Saudi Arabian cities, provides valuable insights into EV usage, but it is essential to interpret findings within the context of these specific cities and be cautious of potential limitations and biases.

Suggested Citation

  • Abdulaziz Almutairi & Naif Albagami & Sultanh Almesned & Omar Alrumayh & Hasmat Malik, 2023. "Electric Vehicle Load Estimation at Home and Workplace in Saudi Arabia for Grid Planners and Policy Makers," Sustainability, MDPI, vol. 15(22), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15878-:d:1278902
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    References listed on IDEAS

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    2. Abdulgader Alsharif & Chee Wei Tan & Razman Ayop & Ahmed Al Smin & Abdussalam Ali Ahmed & Farag Hamed Kuwil & Mohamed Mohamed Khaleel, 2023. "Impact of Electric Vehicle on Residential Power Distribution Considering Energy Management Strategy and Stochastic Monte Carlo Algorithm," Energies, MDPI, vol. 16(3), pages 1-22, January.
    3. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    4. Cristian Cataldo-Díaz & Rodrigo Linfati & John Willmer Escobar, 2022. "Mathematical Model for the Electric Vehicle Routing Problem Considering the State of Charge of the Batteries," Sustainability, MDPI, vol. 14(3), pages 1-26, January.
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