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In this paper, the impact of Electric Vehicle (EV) uncontrolled charging with four levels of EV penetration in overall 21 real low voltage distribution grids in two seasons are analysed. The employed real grid data is provided by distribution system operators from three European countries: Austria, Germany and the Netherlands. At least six grids in each country were considered and they are categorised into three types, namely rural grids, suburban grids and urban grids. The EV charging data used in this study is based on real measurements or surveys. The seasonal and the weekday-weekend factors are also considered in the EV charging impact research. Three key congestion indicators, the transformer loading, line loading and node voltage as well as several other evaluation indexes are studied. The results reveal that the majority of the simulated grids had no or minor moments of mild overloading while the rest grids had critical issues. Among all the grids, suburban grids are most vulnerable to massive EV integration. Out of the evaluated grids, those who are located in Germany have the highest redundancy for high EV penetration accommodation. Overall, the impact of uncontrolled EV charging depends on the combination of EV charging demand as well as the grid inherent features.
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In this paper, the impact of Electric Vehicle (EV) uncontrolled charging with four levels of EV penetration in overall 21 real low voltage distribution grids in two seasons are analysed. The employed real grid data is provided by distribution system operators from three European countries: Austria, Germany and the Netherlands. At least six grids in each country were considered and they are categorised into three types, namely rural grids, suburban grids and urban grids. The EV charging data used in this study is based on real measurements or surveys. The seasonal and the weekday-weekend factors are also considered in the EV charging impact research. Three key congestion indicators, the transformer loading, line loading and node voltage as well as several other evaluation indexes are studied. The results reveal that the majority of the simulated grids had no or minor moments of mild overloading while the rest grids had critical issues. Among all the grids, suburban grids are most vulnerable to massive EV integration. Out of the evaluated grids, those who are located in Germany have the highest redundancy for high EV penetration accommodation. Overall, the impact of uncontrolled EV charging depends on the combination of EV charging demand as well as the grid inherent features.
Mass deployment of Electric Vehicles (EVs) can improve the loading characteristics of low voltage distribution grids with high Photovoltaic (PV) penetration. This impact is investigated in the paper from two point of views, namely, the EV charger type and the EV penetration level. Based on the measured usage data for home, public and semi-public EV chargers, it is highlighted that the ratio of the number of these charger types can influence the grid level impact of PV penetration. Using Monte-Carlo method with aggregated power balance model, it is suggested that the increase in percentage of public and semi-public chargers relative to home chargers can improve self-consumption of PV energy in the grid, thereby reducing the power mismatch due to excess local generation. A PowerFactory based simulation with real measurement based data on real German distribution grids reveals that the grids have no risk of congestion at all with 80% EV penetration, allowing for a possibility even higher EV penetration in the future. Furthermore, with the considered uncontrolled EV charging, it is observed that the grids experience reverse power flows due to excess PV generation. This excess PV energy reduces by about 5% with high EV penetration, indicating a future potential for targeted smart charging application for improving these benchmarked results.
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Mass deployment of Electric Vehicles (EVs) can improve the loading characteristics of low voltage distribution grids with high Photovoltaic (PV) penetration. This impact is investigated in the paper from two point of views, namely, the EV charger type and the EV penetration level. Based on the measured usage data for home, public and semi-public EV chargers, it is highlighted that the ratio of the number of these charger types can influence the grid level impact of PV penetration. Using Monte-Carlo method with aggregated power balance model, it is suggested that the increase in percentage of public and semi-public chargers relative to home chargers can improve self-consumption of PV energy in the grid, thereby reducing the power mismatch due to excess local generation. A PowerFactory based simulation with real measurement based data on real German distribution grids reveals that the grids have no risk of congestion at all with 80% EV penetration, allowing for a possibility even higher EV penetration in the future. Furthermore, with the considered uncontrolled EV charging, it is observed that the grids experience reverse power flows due to excess PV generation. This excess PV energy reduces by about 5% with high EV penetration, indicating a future potential for targeted smart charging application for improving these benchmarked results.
