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An Optimal Management for Charging and Discharging of Electric Vehicles in an Intelligent Parking Lot Considering Vehicle Owner's Random Behaviors

机译:考虑车主随机行为的智能停车场中电动汽车充电和放电的最佳管理

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摘要

Because of missing the stochastic behaviors and decisions of the electric vehicle (EV) owners during the defi-nition 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 infor-mation 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.
机译:由于缺少电动车辆(EV)业主的随机行为和决定,在减少能源管理结构中的充电/放电的能量管理中,他们的时间表一直是缺陷。这些随机行为影响了EV业主与停车场的信息中心之间的交换信息,如抵达和出发时间,EV,电池容量和需求每小时充电率的初始和最终充电状态(SOC)。考虑到EV业主在智能停车场(IPLS)中运营调度中的这些随机行为是本文的主要目标。首先,通过定义EV业主和其他实际情况的随机行为,提出了一种用于电动汽车充电和放电计划的建模,其目的是最大化停车利润,最大限度地减少EV业主的成本。然后,通过确定所有车辆的错误所有者以及所有车辆的初始入学费,并且在定义罚款时也考虑灵活性,提出了IPLS中EVS的完整能源管理结构。这些罚款是这样,无法实现其收费权利的EVS的智能金钱是由错误的EV业主的处罚支付,也为IPLS提供了一些利润。在模拟中的三种不同情景期间验证了所提出的方法的有效性,结果表明了这种增强策略的良好性能。

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