V2G Potential Estimation and Optimal Discharge Scheduling for MMC-based Charging Stations

Author(s):  
Erdem Gumrukcu ◽  
Harshvardhan Samsukha ◽  
Ferdinanda Ponci ◽  
Antonello Monti ◽  
Giuseppe Guidi ◽  
...  
Keyword(s):  
Author(s):  
Hossein Parastvand ◽  
Octavian Bass ◽  
Mohammad A. S. Masoum ◽  
Zeinab Moghaddam ◽  
Stefan Lachowicz ◽  
...  

Author(s):  
Mohamad Nassereddine

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.


Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 260
Author(s):  
Jon Anzola ◽  
Iosu Aizpuru ◽  
Asier Arruti

This paper focuses on the design of a charging unit for an electric vehicle fast charging station. With this purpose, in first place, different solutions that exist for fast charging stations are described through a brief introduction. Then, partial power processing architectures are introduced and proposed as attractive strategies to improve the performance of this type of applications. Furthermore, through a series of simulations, it is observed that partial power processing based converters obtain reduced processed power ratio and efficiency results compared to conventional full power converters. So, with the aim of verifying the conclusions obtained through the simulations, two downscaled prototypes are assembled and tested. Finally, it is concluded that, in case galvanic isolation is not required for the charging unit converter, partial power converters are smaller and more efficient alternatives than conventional full power converters.


Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1215
Author(s):  
Alvaro Carreno ◽  
Marcelo Perez ◽  
Carlos Baier ◽  
Alex Huang ◽  
Sanjay Rajendran ◽  
...  

Distribution systems are under constant stress due to their highly variable operating conditions, which jeopardize distribution transformers and lines, degrading the end-user service. Due to transformer regulation, variable loads can generate voltage profiles out of the acceptable bands recommended by grid codes, affecting the quality of service. At the same time, nonlinear loads, such as diode bridge rectifiers without power factor correction systems, generate nonlinear currents that affect the distribution transformer operation, reducing its lifetime. Variable loads can be commonly found at domiciliary levels due to the random operation of home appliances, but recently also due to electric vehicle charging stations, where the distribution transformer can cyclically vary between no-load, rated and overrated load. Thus, the distribution transformer can not safely operate under highly-dynamic and stressful conditions, requiring the support of alternative systems. Among the existing solutions, hybrid transformers, which are composed of a conventional transformer and a power converter, are an interesting alternative to cope with several power quality problems. This article is a review of the available literature about hybrid distribution transformers.


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