Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers
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 ar...
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | http://eprints.utm.my/107353/1/HasmatMalik2023_ElectricVehicleLoadEstimationatHome.pdf |
| _version_ | 1811132521740500992 |
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| author | Almutairi, Abdulaziz Albagami, Naif Sultanh Almesned, Sultanh Almesned Alrumayh, Omar Malik, Hasmat |
| author_facet | Almutairi, Abdulaziz Albagami, Naif Sultanh Almesned, Sultanh Almesned Alrumayh, Omar Malik, Hasmat |
| author_sort | Almutairi, Abdulaziz |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-09-24T00:05:37Z |
| format | Article |
| id | utm.eprints-107353 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-09-24T00:05:37Z |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-1073532024-09-03T06:22:52Z http://eprints.utm.my/107353/ Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers Almutairi, Abdulaziz Albagami, Naif Sultanh Almesned, Sultanh Almesned Alrumayh, Omar Malik, Hasmat TK Electrical engineering. Electronics Nuclear engineering 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. MDPI 2023-11 Article PeerReviewed application/pdf en http://eprints.utm.my/107353/1/HasmatMalik2023_ElectricVehicleLoadEstimationatHome.pdf Almutairi, Abdulaziz and Albagami, Naif and Sultanh Almesned, Sultanh Almesned and Alrumayh, Omar and Malik, Hasmat (2023) Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers. Sustainability (Switzerland), 15 (22). pp. 1-16. ISSN 2071-1050 http://dx.doi.org/10.3390/su152215878 DOI:10.3390/su152215878 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Almutairi, Abdulaziz Albagami, Naif Sultanh Almesned, Sultanh Almesned Alrumayh, Omar Malik, Hasmat Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers |
| title | Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers |
| title_full | Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers |
| title_fullStr | Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers |
| title_full_unstemmed | Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers |
| title_short | Electric vehicle load estimation at home and workplace in Saudi Arabia for grid planners and policy makers |
| title_sort | electric vehicle load estimation at home and workplace in saudi arabia for grid planners and policy makers |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/107353/1/HasmatMalik2023_ElectricVehicleLoadEstimationatHome.pdf |
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