An Optimal Management for Charging and Discharging of Electric Vehicles in an Intelligent Parking Lot Considering Vehicle Owner's Random Behaviors. (March 2021) - Alinejad, Mahyar; Rezaei, Omid; Kazemi, Ahad; Bagheri, Saeed
Highlights: Maximizing the profits of a parking lot and electric vehicle owners. Optimal charging/discharging schedules for EVs Considering their owner's random behaviors . Providing fines for vehicles that are eligible for incomplete charging. Increasing the level of vehicle charging to the expected values. Decreasing the number of charging in the peak times and charging the vehicles in the offpeak times. Abstract: Because of missing the stochastic behaviors and decisions of the electric vehicle (EV) owners during the definition of the structure of energy management for charging/discharging of EVs There has always been a flaw in their schedule. These random behaviors affect the exchanged information between EV owners and the information center of the parking lot such as arrival and departure time, initial and final State of Charge (SOC) of EV, battery capacity, and demand hourly charge rate. Considering these stochastic behaviors of EV owners in operational scheduling in intelligent parking lots (IPLs) is the main goal of this article. Firstly by defining the random behavior of EV owners and other real situations, the modeling of the charging and discharging plan for electric vehicles with the aim of maximizing parking profit and minimizing costs for EV owners is presented. Then by determining penalties for faulty EV owners and the initial entrance fee for all vehicles, and also considering flexibility when defining the fines, a complete structure of energy management for EVs in IPLs is presented. These fines are such that, the smart money for EVs that cannot achieve their rights for charging, is paid by the penalties for faulty EV owners, and also make some profits for the IPLs. The effectiveness of the proposed method is validated during three different scenarios in the simulation, and the results show the good performance of this enhanced strategy for the management of EVs.