Comparison and Evaluation of the Performance of Various Types of Neural Networks for Planning Issues Related to Optimal Management of Charging and Discharging Electric Cars in Intelligent Power Grids

Arash Moradzaeh, Kamran Khaffafi


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.


Smart Grid; Optimized Charge and Discharge Management; Neural Networks; Electric Vehicles.


Bessa, Ricardo J., Manuel A. Matos, Filipe Joel Soares, and João A. Peças Lopes. “Optimized Bidding of a EV Aggregation Agent in the Electricity Market.” IEEE Transactions on Smart Grid 3, no. 1 (March 2012): 443–452. doi:10.1109/tsg.2011.2159632.

Liu, Ryan, Luther Dow, and Edwin Liu. “A Survey of PEV Impacts on Electric Utilities.” ISGT 2011 (January 2011). doi:10.1109/isgt.2011.5759171.

Chen, Changsong, and Shanxu Duan. “Optimal Integration of Plug-In Hybrid Electric Vehicles in Microgrids.” IEEE Transactions on Industrial Informatics 10, no. 3 (August 2014): 1917–1926. doi:10.1109/tii.2014.2322822.

Sekyung Han, Soohee Han, and Kaoru Sezaki. “Development of an Optimal Vehicle-to-Grid Aggregator for Frequency Regulation.” IEEE Transactions on Smart Grid 1, no. 1 (June 2010): 65–72. doi:10.1109/tsg.2010.2045163.

Li, Zhang Xueqing Liang Jun Zhang, Yu Dayang Han Xueshan Zhang Feng, and Zhang Xi. "Approach for plug-in electric vehicles charging scheduling considering wind and photovoltaic power in Chinese regional power grids [J]." Transactions of China Electrotechnical Society 2 (2013): 003.

O’Connell, Niamh, Qiuwei Wu, Jacob Østergaard, Arne Hejde Nielsen, Seung Tae Cha, and Yi Ding. “Day-Ahead Tariffs for the Alleviation of Distribution Grid Congestion from Electric Vehicles.” Electric Power Systems Research 92 (November 2012): 106–114. doi:10.1016/j.epsr.2012.05.018.

Branch, Mary Ann, and Andrew Grace. MATLAB: optimization toolbox: user's guide version 1.5. The MathWorks, 1996.

Masoum, Amir S., Sara Deilami, Mohammad A.S. Masoum, Ahmed Abu-Siada, and Syed Islam. “Online Coordination of Plug-in Electric Vehicle Charging in Smart Grid with Distributed Wind Power Generation Systems.” 2014 IEEE PES General Meeting | Conference & Exposition (July 2014). doi:10.1109/pesgm.2014.6939133.

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DOI: 10.28991/ijse-01123


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