شماره ركورد كنفرانس :
3788
عنوان مقاله :
Optimal Charging Schedule of Electric Vehicles at Battery Swapping Stations in a Smart Distribution Network
عنوان به زبان ديگر :
Optimal Charging Schedule of Electric Vehicles at Battery Swapping Stations in a Smart Distribution Network
پديدآورندگان :
Salimi Amiri Saeed salimi_s@elec.iust.ac.ir Electrical Engineering Department Iran University of Science and Technology Tehran, Iran , Jadid Shahram jadid@iust.ac.ir Electrical Engineering Department Iran University of Science and Technology Tehran, Iran
كليدواژه :
Electric vehicle , battery swapping station , charging strategy , modified GA , PSO algorithm.
عنوان كنفرانس :
هفتمين كنفرانس ملي شبكه هاي هوشمند انرژي 96
چكيده فارسي :
Motivated by indispensable requirements of large penetration of electric vehicles (EVs), battery swapping is an efficient performance to exert benefits of changing batteries within a short time period and charging them during off-peak hours. This paper proposes a strategy trying to find the best charging procedure of electric vehicles in an environment toward battery swapping stations (BSSs). The goal of the strategy is to minimize the charging cost as well as to reduce energy loss. Voltage deviation of buses, power flow of network branches, and maximum power consumption of BSSs are considered as constraints of this optimization problem. In order to solve the issue, a population-based evolutionary approach, which is a modified hybrid form of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, is employed. The strategy is implemented on IEEE 33-bus distribution network test system and numerical results are illustrated.
چكيده لاتين :
Motivated by indispensable requirements of large penetration of electric vehicles (EVs), battery swapping is an efficient performance to exert benefits of changing batteries within a short time period and charging them during off-peak hours. This paper proposes a strategy trying to find the best charging procedure of electric vehicles in an environment toward battery swapping stations (BSSs). The goal of the strategy is to minimize the charging cost as well as to reduce energy loss. Voltage deviation of buses, power flow of network branches, and maximum power consumption of BSSs are considered as constraints of this optimization problem. In order to solve the issue, a population-based evolutionary approach, which is a modified hybrid form of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, is employed. The strategy is implemented on IEEE 33-bus distribution network test system and numerical results are illustrated.
