Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks
Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5905 |
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| _version_ | 1850060298238558208 |
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| author | Mohamed El-Hendawi Zhanle Wang Raman Paranjape James Fick Shea Pederson Darcy Kozoriz |
| author_facet | Mohamed El-Hendawi Zhanle Wang Raman Paranjape James Fick Shea Pederson Darcy Kozoriz |
| author_sort | Mohamed El-Hendawi |
| collection | DOAJ |
| description | Achieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development. |
| format | Article |
| id | doaj-art-693c244b64b24fe78b18763ccc9841f2 |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-693c244b64b24fe78b18763ccc9841f22025-08-20T02:50:37ZengMDPI AGEnergies1996-10732024-11-011723590510.3390/en17235905Impact of Electric Vehicles Charging on Urban Residential Power Distribution NetworksMohamed El-Hendawi0Zhanle Wang1Raman Paranjape2James Fick3Shea Pederson4Darcy Kozoriz5Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, CanadaFaculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, CanadaFaculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, CanadaSaskatchewan Power Corporation, Regina, SK S4P 0S1, CanadaSaskatchewan Power Corporation, Regina, SK S4P 0S1, CanadaSaskatchewan Power Corporation, Regina, SK S4P 0S1, CanadaAchieving transportation decarbonization and reducing carbon emissions are global initiatives that have attracted a lot of effort. The use of electric vehicles (EVs) has experienced a significant increase lately, which will have a considerable impact on current power systems. This study develops a framework to evaluate/mitigate the negative impact of increasing EV charging on urban power distribution systems. This framework includes data analytics of actual residential electrical load and EV charging profiles, and the development of optimal EV charging management and AC load flow models using an actual residential power distribution system in Saskatchewan, Canada. We use statistical methods to identify a statistically-extreme situation for a power system, which a power utility needs to prepare for. The philosophy is that if the power system can accommodate this situation, the power system will be stable 97.7% of the time. Simulation results show the house voltage and transformer loading at various EV penetration levels under this statistically-extreme situation. We also identify that the particular 22-house power distribution system can accommodate a maximum number of 11 EVs (representing 50% EV penetration) under this statistically-extreme situation. The results also show that the proposed optimal EV charging management model can reduce the peak demand by 43%. Since we use actual data for this study, it reflects the current real-world situation, which presents a useful reference for power utilities. The framework can also be used to evaluate/mitigate the impact of EV charging on power systems and optimize EV infrastructure development.https://www.mdpi.com/1996-1073/17/23/5905electric vehiclesload flowsmart chargingoptimal EV charging |
| spellingShingle | Mohamed El-Hendawi Zhanle Wang Raman Paranjape James Fick Shea Pederson Darcy Kozoriz Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks Energies electric vehicles load flow smart charging optimal EV charging |
| title | Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks |
| title_full | Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks |
| title_fullStr | Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks |
| title_full_unstemmed | Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks |
| title_short | Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks |
| title_sort | impact of electric vehicles charging on urban residential power distribution networks |
| topic | electric vehicles load flow smart charging optimal EV charging |
| url | https://www.mdpi.com/1996-1073/17/23/5905 |
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