Optimal planning of PEV fast charging stations using an auction-based method

Electric vehicles will play a dominant role in future transportation due to their friendliness towards the present day environment. The battery which drives the vehicle can be refilled using battery charging and battery swapping techniques. Fast charging stations provide faster service to the customers. Though battery swapping method outperforms battery charging in many ways, the heavy infrastructure requirement of the former requires time in integrating with the real world. Queuing models are used to depict the real-time behavior of service stations. The aspiration level model provides the optimal value of charging piles for the given system capacity in a fast-charging station. The parameters in the aspiration level model can be formulated to an optimization problem. In the present work, the optimal planning for an fast charging station in Beijing is carried out using genetic algorithm. The simulation work is carried out in MATLAB/Simulink.

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Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2019-0270 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Mohamad Nassereddine

Keyword(s):

Machine Learning ◽

Renewable Energy ◽

Electric Vehicles ◽

Renewable Energy Sources ◽

Energy Sources ◽

Power Network ◽

Network Stability ◽

Fast Charging ◽

Electrical Loads ◽

Charging Stations

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.

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Partial Power Processing Based Converter for Electric Vehicle Fast Charging Stations

Electronics ◽

10.3390/electronics10030260 ◽

2021 ◽

Vol 10 (3) ◽

pp. 260

Author(s):

Jon Anzola ◽

Iosu Aizpuru ◽

Asier Arruti

Keyword(s):

Electric Vehicle ◽

Power Converters ◽

Power Ratio ◽

Charging Station ◽

Fast Charging ◽

Full Power ◽

Processing Architectures ◽

Charging Stations ◽

Fast Charging Station ◽

Partial Power Processing

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.

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