%0 Journal Article %A tazikeh lemeski, Atena %A Ebrahimi, Reza %A Zakariazadeh, Alireza %T Self-scheduling of electric vehicles aggregator in the energy market based on TOU pricing plan %J Iranian Electric Industry Journal of Quality and Productivity %V 10 %N 4 %U http://ieijqp.ir/article-1-812-en.html %R %D 2022 %K Electric Vehicle Aggregator, Charging and Discharging Management, TOU Pricing, Energy Market, %X In recent years, the issue of air pollution caused by greenhouse gas emissions and rising energy prices have contributed to developing and increasing the number of electric vehicles. Despite the many advantages of these vehicles, their increasing number and consequently their simultaneous charging in the distribution network can have destructive effects such as increased peak load, increased losses, unauthorized voltage drop, etc. On the other hand, managing the charging of vehicles by aggregators and using them as flexible loads and, if there is vehicle-to-grid (V2G) capability, as distributed generation units distributed across the distribution network can bring many financial and technical opportunities for the network. Accordingly, managing and planning the charging and discharging of these vehicles from the view point of network operators, aggregators, or vehicle owners in a centralized and decentralized manner are among the interesting topics that many articles have dealt with so far. This paper presents, a new solution for self-scheduling the charging and discharging of the private aggregator of electric vehicles to increase their profitability in the distribution network. Given the private ownership of the aggregator, it is obvious that the only factor influencing planning is cost reduction or profit enhancement, so its effect is unknown and/or negative on network indicators such as losses and voltage profiles. To solve this problem, a Time of Use (TOU) pricing model has been proposed by the Distribution Network Operator (DSO), so the aggregator plans to charge and discharge vehicles so that it can improve indicators such as losses and voltage profiles of the network in addition to be profitable. Density functions might have been used to include the uncertainty of vehicle drivers' behavior and to model the possible parameters related to him/her. Finally, the proposed approach is applied to a 33-bus test network by a genetic optimization algorithm using a private aggregator. The simulation results show that, in addition to maximizing the aggregator gain, the proposed method smoothes the network load curve, which reduces losses and improves voltage profile. It seems that in the probabilistic environment of vehicle behavior, the combination of TOU in private aggregator planning, which has led to an increase in their profits and at the same time in terms of the use of improved technical indicators, has not been studied yet. %> http://ieijqp.ir/article-1-812-en.pdf %P 38-46 %& 38 %! Self-scheduling of EV aggregators %9 Research %L A-10-1374-1 %+ %G eng %@ 2322-2344 %[ 2022