Design and Analysis of a Power Transmission Network Model of Electric Vehicle

2016 ◽  
Vol 693 ◽  
pp. 45-52
Author(s):  
L.H. Liu
Keyword(s):  
Renewable Energy ◽  
Energy Distribution ◽  
Electric Vehicles ◽  
Power Transmission ◽  
Renewable Energy Sources ◽  
Operation Mode ◽  
Distribution Network ◽  
Linear Constraints ◽  
Energy Sources ◽  
Vehicle To Grid

Fossil energy is increasing depletion, renewable energy sources plays an important role in our life and Vehicle-to-Grid (V2G) is proved to be feasible. Electric Vehicles (EVs) can not only store energy, but also can be used as a medium between the battery energy stored in EVs and the power grid through Vehicle-to-Grid (V2G). And then, the energy in the batteries of electric vehicles can move with EVs. This paper introduces an energy distribution network, which is consisting of EVs, charge stations and renewable energy sources. After analyzing the characteristics of energy distribution network, we introduce a new commercial operation mode called mobile electrical grid, which is compared with the integrated grids. To calculate the life cycle energy loss for this novel operation mode, a mathematical model is developed, and then what we have deduced is demonstrated to be a lasso optimization problem with linear constraints, which is convex.

Application of Plug-In Electric Vehicles and Controllable Loads to Frequency Regulation

2012 ◽  
Vol 588-589 ◽  
pp. 1640-1643
Author(s):  
Shu Lei Deng ◽  
Bao Ping Liu ◽  
An Jun Li ◽  
Xiong Zhou ◽  
Yu Xiang Huang
Keyword(s):  
Renewable Energy ◽  
Feedback Control ◽  
Power Systems ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Frequency Regulation ◽  
Battery Charging ◽  
Vehicle To Grid ◽  
Simulation Results

High renewable energy penetration in power systems may bring a series of problems such as frequency fluctuations. Plug-in electric vehicles (PEVs) and controllable loads have been shifting into focus for this. A dynamic vehicle-to-grid (V2G) model with feedback control is proposed by considering the battery charging/discharging characteristics and the dynamic model of frequency regulation with PEVs and controllable loads for a single area is established. Simulation results demonstrate that the application of PEVs and controllable loads can relief the frequency refutation due to the randomness of renewable energy sources.

Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration

2014 ◽  
Vol 34 ◽  
pp. 501-516 ◽  
Author(s):  
Francis Mwasilu ◽  
Jackson John Justo ◽  
Eun-Kyung Kim ◽  
Ton Duc Do ◽  
Jin-Woo Jung
Keyword(s):  
Renewable Energy ◽  
Smart Grid ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Vehicle To Grid

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

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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