Providing a Method of Intelligent Charging of Electric Vehicles with Discrete Rates with the Aim of Flattening Load Profile and Reducing Number of Switching Times

From the point of view of network operation, the lack of proper management of electric vehicles in the network can change the load profile of the network and causes numerous peaks and valleys in it, which is not appropriate from the perspective of network operators. In this paper, an intelligent charging strategy in which according to the information recorded by drivers such as the length of the previous trip, the length of the next trip, arrival time and departure time of the car and with the aim of reducing daily costs and reducing network peak load for integration. The best electric vehicle is provided in the network. Highlights and key points of the article include examining the presence of charging parking lots in power supply instead of developing power transformers and examining the positive consequences of using discrete intelligent charging of electric vehicles to flatten network load profiles and reduce switching times, as well as considering electric vehicle charging schedules. There are various electric vehicles that have a normal distribution for cancellation situations or the presence of all subscribers in their satisfaction coefficient or cost coefficient. Also, two models of intelligent charging with the aim of minimizing the total daily cost and its peak-to-average load ratio have been presented to examine the effects of charging an electric vehicle from an economic and technical point of view. Also, fast and slow charging are two different charging methods that will be examined along with charging parking lots and the type of home charging for these case. In this study, a method for leveling the network load profile was introduced and the effect of the penetration level of electric vehicles on the network load profile was investigated and positive results were obtained. Charging in the early hours prevented the occurrence of peak load and also prevented damage to the network. A 37-bus distribution network has been used for the simulations, in which two technical and economic methods will be examined. In this research, YALMIP powerful toolboxes have been used. The results show that the proposed method will be effective in reducing the number of switching times and leveling the load profile.