Volume 19, Issue 4 (JIAEEE Vol.19 No.4 2022)                   Journal of Iranian Association of Electrical and Electronics Engineers 2022, 19(4): 27-37 | Back to browse issues page


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Radmanesh H, Nasiri A. A Smart Charging Method for Optimum Electric Vehicles Integration in the Distribution System in Presence of Demand Response Program. Journal of Iranian Association of Electrical and Electronics Engineers 2022; 19 (4) :27-37
URL: http://jiaeee.com/article-1-1279-en.html
Engineering Department, Islamic Azad University Central Tehran Branch
Abstract:   (258 Views)
Electric vehicle charging in the distribution network is one of the common techniques for technical and economic management of energy distribution, which, if implemented properly, will bring several benefits such as reducing network peak load, charging costs reduction, loss minimization, and etc. In most traditional charging methods, the constraints of fully charging electric vehicles at departure time from the parking lot have always been considered, while, in fact, it is not necessary to fully charge electric vehicles. Instead, it is better for each vehicle to be charged smartly based on its required energy for daily trips. In order to implement this smart method, electric vehicle owners provide information about the number of trips and the length of their route for the parking charge management unit. Then, the desired charge calculation is done according to the vehicle's specifications and their initial state of charge of the battery at arrival time to the parking lot. Finally, the charge manager will schedule charging based on the time of use tariff, the limitation of the distribution transformers, the charging level (normal or fast), etc. to so minimize the charging cost in compliance with the technical and economic constraints. The result of smart charging is compared in normal and fast charging mode and several limitations of the distribution network in the presence/absence of the demand response program. YALMIP and MOSEK software have been applied as solvers of the mixed-integer linear programming model.
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Type of Article: Research | Subject: Power
Received: 2021/03/3 | Accepted: 2021/09/19 | Published: 2022/10/28

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