scholarly journals Electric vehicle charging to support renewable energy integration in a capacity constrained electricity grid

2016 ◽  
Vol 109 ◽  
pp. 130-139 ◽  
Author(s):  
Nathaniel S. Pearre ◽  
Lukas G. Swan
Keyword(s):  
Renewable Energy ◽  
Electric Vehicle ◽  
Energy Integration ◽  
Electricity Grid ◽  
Renewable Energy Integration ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Capacity Constrained

Optimization of Smart Grid Solar Energy Application

2014 ◽  
Author(s):  
Olumide Bello ◽  
Landon Onyebueke
Keyword(s):  
Renewable Energy ◽  
Smart Grid ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Peak Load ◽  
Energy Integration ◽  
Electric Vehicle Charging ◽  
Battery Bank ◽  
Vehicle Charging

This paper presents an approach to modeling of renewable energy integration into Smart Grid for Electric Vehicle charging applications. Integration of renewable energy sources to smart grid is not only the key to smart Electric Vehicle charging but also the most efficient way to manage the distributed energy resources. It enables the ability to control, ease the peak load impacts, and protect distribution network components from being overloaded by Electric Vehicles. Thus, the electricity generation and consumption is managed in more cost effective way. The developed model is a grid connected solar-assisted Electric Vehicle charging station, with battery bank. It generates electricity using solar photovoltaic (PV) arrays to augment the electricity used to charge the electric vehicles. The battery bank stores electricity from the grid and discharges the stored energy during periods of peak charging demand. Optimization of the model was done by developing a program written in Visual Basic 2012. The computational results show the economic advantages of this model as well as the anticipated benefits of the smart grid for reduced peak loads, and increased efficiency.

scholarly journals Retraction: Codani, P., et al. Coupling Local Renewable Energy Production with Electric Vehicle Charging: A Survey of the French Case. World Electr. Veh. J. 2015, 7, 489–499

2021 ◽  
Vol 12 (1) ◽  
pp. 49
Author(s):  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicle ◽  
Energy Production ◽  
Electric Vehicle Charging ◽  
Renewable Energy Production ◽  
French Case ◽  
Vehicle Charging

The journal retracts the article, ”Coupling Local Renewable Energy Production with Electric Vehicle Charging: A Survey of the French Case” [...]

Robust model of electric vehicle charging station location considering renewable energy and storage equipment

Energy ◽  
2021 ◽  
pp. 121713
Author(s):  
Li Chengzhe ◽  
Libo Zhang ◽  
Ou Zihan ◽  
Qunwei Wang ◽  
Dequn Zhou ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicle ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Robust Model ◽  
Vehicle Charging ◽  
Station Location ◽  
And Storage

Hybrid Renewable Energy Fed Battery Electric Vehicle Charging Station

2021 ◽  
Author(s):  
R. Kannan ◽  
S. Karthikkumar ◽  
P. Suseendhar ◽  
S. Pragaspathy ◽  
B. N. Ch.V. Chakravarthi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Electric Vehicle ◽  
Battery Electric Vehicle ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Vehicle Charging ◽  
Hybrid Renewable Energy

scholarly journals Service Time Analysis For Electric Vehicle Charging Infrastructure

2018 ◽  
Vol 8 (2) ◽  
pp. 818
Author(s):  
Junghoon Lee ◽  
Gyung-Leen Park
Keyword(s):  
Electric Vehicle ◽  
Service Time ◽  
Open Data ◽  
Energy Integration ◽  
Charging Infrastructure ◽  
Tourist Attractions ◽  
Electric Vehicle Charging ◽  
Charging Time ◽  
Main Observation ◽  
Vehicle Charging

This paper analyzes electric vehicle charging patterns in Jeju City, taking advantage of open software such as MySQL, Hadoop, and R, as well as open data obtained from the real-time charger monitoring system currently in operation. Main observation points lie in average service time, maximum service time, and the number of transactions, while we measure the effect of both temporal and spatial factors to them. According to the analysis result, the average service time is almost constant for all parameters. The charging time of 88.7 % transactions ranges from 10 to 40 minutes, while abnormally long transactions occupy just 3.4 % for fast chargers. The day-by-day difference in the number of charging transactions is 28.6 % at maximum, while Wednesday shows the largest number of transactions. Additionally, geographic information-based analysis tells that the charging demand is concentrated in those regions having many tourist attractions and administrative offices. With this analysis, it is possible to predict when a charger will be idle and allocate it to another service such as V2G or renewable energy integration.

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