Abstract
The use of electric vehicles in addition to reducing environmental concerns can play a significant role in reducing the peak and filling the characteristic valleys of the daily network load. In other words, in the context of smart grids, it is possible to improve the battery of electric vehicles by scheduling charging and discharging processes. In this research, the issue of controlling the charge and discharge of electric vehicles was evaluated using a variety of neural models, until the by examining the effect of the growth rate of the penetration level of electric vehicles of the hybrid type that can be connected to the distribution network, the results of the charge management and discharge model of the proposed response are examined. The results indicate that due to increased penetration of these cars is increased the amount of responses to charge and discharge management. In this research, a variety of neural network methods, a) neural network method using Multilayer Perceptron Training (MLP), b) neural network method using Jordan Education (RNN), c) neural network method using training (RBF ) Was evaluated based on parameters such as reduction of training error, reduction of network testing error, duration of run and number of replications for each one. The final results indicate that electric vehicles can be used as scattered power plants, and can be useful for regulating the frequency and regulation of network voltages and the supply of peak traffic. This also reduces peak charges and incidental costs, which ultimately helps to further network stability. Finally, the charge and discharge management response reflects the fact that intelligent network-based models have the ability to manage the charge and discharge of electric vehicles, and among the models the amount of error reduction training and testing is very favourable for both RNN, MLP.
Highlights
In recent years, the global demand for electricity has grown more than other energy sources
The results show that electric vehicles can be used as scattered power plants
2-3- Neural Network Jordan (RNN) Jordan's recurring neural network has a structure very similar to Allman's neural network, but with the difference that the feeder operation is performed from the output layer to the input layer, which is shown in Figure 3 of the neural network (RNN) used in this research and display
Summary
The global demand for electricity has grown more than other energy sources. The main goal is to provide reliable electricity and meet the growing needs of customers with the least damage to the environment Another feature of the smart grid, in addition to monitoring the transmission and distribution network, is the ability of the smart. The effect of electric vehicles' penetration on the distribution network is different These effects have affected the charging pattern, charging speed, and charge characteristics, power losses in the distribution network, vehicle driving patterns, response and demand, strategy for reducing load, driving distance, battery size and tariffs. Researchers follow these issues and plans to reduce these effects [3, 4]. This reduces peak charges and incidental costs, which helps to further network stability
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