Location optimization of electric vehicle chargers, photovoltaic generation, and vehicle to grid point of connection for minimizing voltage harmonic levels in low voltage power distribution networks

The aim of this paper is to investigate the optimal point of connection for electric vehicle chargers, photovoltaic generation, and vehicle to grid to minimize the steady-state voltage total harmonic distortion of all points of supply across the low voltage electricity distribution network. Connecting electric vehicle chargers, photovoltaic generation, and vehicle to grid can lead to increased levels of harmonic distortion. Harmonics increase the risk of transformer, motor, and cable overheating, metering and computer errors, failure of electronic equipment, circuit breaker malfunctions, and communication interferences. Therefore, minimizing harmonics on electricity distribution networks is essential. To identify the optimum point of connection, under a range of scenarios, a radial low voltage electricity distribution network is simulated under normal and steady-state fault conditions. Elephant Herding Optimization and Monarch Butterfly Optimization are then integrated into the simulation to converge on the optimum solution. In conclusion, for all but one scenario, the optimal electric vehicle charger point of connection is the 1st bus fed from the 11kV:400V transformer. For photovoltaic generation, the 1st bus or first few busses are optimal for three specific scenarios. For vehicle to grid, the optimum points of connection are presented as a guide rather than a definitive conclusion.

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Publication Details

Journal
International Journal of Green Energy
Published
2024-11-25
DOI
https://doi.org/10.1080/15435075.2024.2414231
Citations
1
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
0.21
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article

Location optimization of electric vehicle chargers, photovoltaic generation, and vehicle to grid point of connection for minimizing voltage harmonic levels in low voltage power distribution networks

Jin Feng Yang, Nand K. Meena, Graham Gissing
1 citations
International Journal of Green Energy
Electric Vehicles and Infrastructure
0.21
article

Location optimization of electric vehicle chargers, photovoltaic generation, and vehicle to grid point of connection for minimizing voltage harmonic levels in low voltage power distribution networks

Jin Feng Yang, Nand K. Meena, Graham Gissing
article en
1 citations

Abstract

The aim of this paper is to investigate the optimal point of connection for electric vehicle chargers, photovoltaic generation, and vehicle to grid to minimize the steady-state voltage total harmonic distortion of all points of supply across the low voltage electricity distribution network. Connecting electric vehicle chargers, photovoltaic generation, and vehicle to grid can lead to increased levels of harmonic distortion. Harmonics increase the risk of transformer, motor, and cable overheating, metering and computer errors, failure of electronic equipment, circuit breaker malfunctions, and communication interferences. Therefore, minimizing harmonics on electricity distribution networks is essential. To identify the optimum point of connection, under a range of scenarios, a radial low voltage electricity distribution network is simulated under normal and steady-state fault conditions. Elephant Herding Optimization and Monarch Butterfly Optimization are then integrated into the simulation to converge on the optimum solution. In conclusion, for all but one scenario, the optimal electric vehicle charger point of connection is the 1st bus fed from the 11kV:400V transformer. For photovoltaic generation, the 1st bus or first few busses are optimal for three specific scenarios. For vehicle to grid, the optimum points of connection are presented as a guide rather than a definitive conclusion.

International Journal of Green EnergyVol. 22(7)
Health Data Research UK (GB), University of Glasgow (GB)
Affordable and clean energy
Openalex Percentile: Top 46%
Electric Vehicles and Infrastructure
0.21
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