Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator
In recent years, the integration of electric vehicles (EV) into urban fleets has seen a significant rise, leading to a considerable increase in the number of EV chargers and fast charging stations (FCS) connected to distribution networks. Depending on the characteristics of the electrical power syst...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10720019/ |
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| Summary: | In recent years, the integration of electric vehicles (EV) into urban fleets has seen a significant rise, leading to a considerable increase in the number of EV chargers and fast charging stations (FCS) connected to distribution networks. Depending on the characteristics of the electrical power system, such as short-circuit power and voltage harmonic distortion, due to the dynamic operation during charging sessions, EV charging stations may impacts the quality of power in the connection point. As a result, it is crucial for utility companies and facility managers to perform preliminary assessments to identify potential exceedances of quality limits.In this context, this work describes the development of an electrical grid impact indicator that evaluates the parameters that influence the electrical network during charging of electric vehicles. A case study and a simulation model were used to identify and incorporate into the indice the main relevant factors, such as power demand, short-circuit power, harmonic distortion, and power factor. The simulation models were employed to evaluate critical operational points, and measurement data further validated the model’s performance. The results highlighted the importance of considering these parameters to ensure effective and safe recharging of electric vehicles. The proposed electrical impact indicator offers an electrical network management tool, allowing a predictive assessment of the impact of EV charging and enabling the adoption of appropriate measures to ensure the quality of power of the distribution networks accessed by charging stations. |
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| ISSN: | 2169-3536 |