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Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation
ABSTRACT: The transportation sector is characterized by high emissions of greenhouse gases (GHG) into the atmosphere. Consequently, electric vehicles (EVs) have been proposed as a revolutionary solution to mitigate GHG emissions and the dependence on petroleum products, which are fast depleting. EVs are proliferating in many countries worldwide and the fast adoption of this technology is significantly dependent on the expansion of charging stations. This study proposes the use of the hybrid genetic algorithm and particle swarm optimization (GA-PSO) for the optimal allocation of plug-in EV charging stations (PEVCS) into the distribution network with distributed generation (DG) in high volumes and at selected buses. Photovoltaic (PV) systems with a power factor of 0.95 are used as DGs. The PVs are penetrated into the distribution network at 60% and six penetration cases are considered for the optimal placement of the PEVCSs. The optimization problem is formulated as a multi-objective problem minimizing the active and reactive power losses as well as the voltage deviation index. The IEEE 33 and 69 bus distribution networks are used as test networks. The simulation was performed using MATLAB and the results obtained validate the effectiveness of the hybrid GA-PSO. For example, the integration of PEVCSs results in the minimum bus voltage still within accepted margins. For the IEEE 69 bus network, the resulting minimum voltage is 0.973 p.u in case 1, 0.982 p.u in case 2, 0.96 p.u in case 3, 0.961 p.u in case 4, 0.954 p.u in case 5, and 0.965 p.u in case 6. EVs are a sustainable means of significantly mitigating emissions from the transportation sector and their utilization is essential as the worldwide concern of climate change and a carbon-free society intensifies.
Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation
ABSTRACT: The transportation sector is characterized by high emissions of greenhouse gases (GHG) into the atmosphere. Consequently, electric vehicles (EVs) have been proposed as a revolutionary solution to mitigate GHG emissions and the dependence on petroleum products, which are fast depleting. EVs are proliferating in many countries worldwide and the fast adoption of this technology is significantly dependent on the expansion of charging stations. This study proposes the use of the hybrid genetic algorithm and particle swarm optimization (GA-PSO) for the optimal allocation of plug-in EV charging stations (PEVCS) into the distribution network with distributed generation (DG) in high volumes and at selected buses. Photovoltaic (PV) systems with a power factor of 0.95 are used as DGs. The PVs are penetrated into the distribution network at 60% and six penetration cases are considered for the optimal placement of the PEVCSs. The optimization problem is formulated as a multi-objective problem minimizing the active and reactive power losses as well as the voltage deviation index. The IEEE 33 and 69 bus distribution networks are used as test networks. The simulation was performed using MATLAB and the results obtained validate the effectiveness of the hybrid GA-PSO. For example, the integration of PEVCSs results in the minimum bus voltage still within accepted margins. For the IEEE 69 bus network, the resulting minimum voltage is 0.973 p.u in case 1, 0.982 p.u in case 2, 0.96 p.u in case 3, 0.961 p.u in case 4, 0.954 p.u in case 5, and 0.965 p.u in case 6. EVs are a sustainable means of significantly mitigating emissions from the transportation sector and their utilization is essential as the worldwide concern of climate change and a carbon-free society intensifies.
Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation
Ebunle Akupan Rene (author) / Willy Stephen Tounsi Fokui (author) / Paule Kevin Nembou Kouonchie (author)
2023
Article (Journal)
Electronic Resource
Unknown
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