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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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10720019/ |
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| author | Samuel Dias Vasconcelos Jose Filho da Costa Castro Felipe Gouveia Antonio Venancio de Moura Lacerda Filho Ricardo Fonseca Buzo Luiz Henrique Alves de Medeiros Leonardo Rodrigues Limongi Davidson da Costa Marques Amanda Lopes Fernandes Jiyong Chai Nicolau Kellyano Leite Dantas Chenxin Zhang Pedro Rosas Nestor Medina |
| author_facet | Samuel Dias Vasconcelos Jose Filho da Costa Castro Felipe Gouveia Antonio Venancio de Moura Lacerda Filho Ricardo Fonseca Buzo Luiz Henrique Alves de Medeiros Leonardo Rodrigues Limongi Davidson da Costa Marques Amanda Lopes Fernandes Jiyong Chai Nicolau Kellyano Leite Dantas Chenxin Zhang Pedro Rosas Nestor Medina |
| author_sort | Samuel Dias Vasconcelos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-584f8e4bbfc6456fb7287c50e7b82b56 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-584f8e4bbfc6456fb7287c50e7b82b562024-11-12T00:01:07ZengIEEEIEEE Access2169-35362024-01-011216330716332310.1109/ACCESS.2024.348209510720019Assessment of Electric Vehicles Charging Grid Impact via Predictive IndicatorSamuel Dias Vasconcelos0Jose Filho da Costa Castro1https://orcid.org/0000-0002-2989-9406Felipe Gouveia2Antonio Venancio de Moura Lacerda Filho3Ricardo Fonseca Buzo4https://orcid.org/0000-0002-6451-8197Luiz Henrique Alves de Medeiros5Leonardo Rodrigues Limongi6https://orcid.org/0000-0003-1728-8031Davidson da Costa Marques7Amanda Lopes Fernandes8https://orcid.org/0000-0002-5593-0681Jiyong Chai9Nicolau Kellyano Leite Dantas10Chenxin Zhang11Pedro Rosas12https://orcid.org/0000-0001-9680-7228Nestor Medina13https://orcid.org/0009-0005-6453-3978Department of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilInstitute of Technology Edson Mororó Moura (ITEMM), Belo Jardim, BrazilCPFL Energia, São Paulo, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilCPFL Energia, São Paulo, BrazilCPFL Energia, São Paulo, BrazilInstitute of Technology Edson Mororó Moura (ITEMM), Belo Jardim, BrazilCPFL Energia, São Paulo, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilDepartment of Electrical Engineering (DEE), Storage and Mobility Laboratory (LAM), Federal University of Pernambuco (UFPE), Recife, BrazilIn 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.https://ieeexplore.ieee.org/document/10720019/Electric vehicle charging stationselectrical impact indicatorelectric mobilityelectric vehicles |
| spellingShingle | Samuel Dias Vasconcelos Jose Filho da Costa Castro Felipe Gouveia Antonio Venancio de Moura Lacerda Filho Ricardo Fonseca Buzo Luiz Henrique Alves de Medeiros Leonardo Rodrigues Limongi Davidson da Costa Marques Amanda Lopes Fernandes Jiyong Chai Nicolau Kellyano Leite Dantas Chenxin Zhang Pedro Rosas Nestor Medina Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator IEEE Access Electric vehicle charging stations electrical impact indicator electric mobility electric vehicles |
| title | Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator |
| title_full | Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator |
| title_fullStr | Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator |
| title_full_unstemmed | Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator |
| title_short | Assessment of Electric Vehicles Charging Grid Impact via Predictive Indicator |
| title_sort | assessment of electric vehicles charging grid impact via predictive indicator |
| topic | Electric vehicle charging stations electrical impact indicator electric mobility electric vehicles |
| url | https://ieeexplore.ieee.org/document/10720019/ |
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