Impact of Electric Vehicles Charging on Urban Residential Power Distribution Networks

Mohamed El-Hendawi, Zhanle Wang (), Raman Paranjape, James Fick, Shea Pederson and Darcy Kozoriz
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Mohamed El-Hendawi: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Zhanle Wang: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
Raman Paranjape: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada
James Fick: Saskatchewan Power Corporation, Regina, SK S4P 0S1, Canada
Shea Pederson: Saskatchewan Power Corporation, Regina, SK S4P 0S1, Canada
Darcy Kozoriz: Saskatchewan Power Corporation, Regina, SK S4P 0S1, Canada

Energies, 2024, vol. 17, issue 23, 1-22

Abstract: 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.

Keywords: electric vehicles; load flow; smart charging; optimal EV charging (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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