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Charging Scheduling of Electric Vehicles (EV) in Probabilistic Scenario considering Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G)

机译:考虑到车对网(G2V)和车对网(V2G)的概率方案中的电动汽车(EV)的充电调度

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This paper aims a Charging Scheduling Algorithm (CSA) with the intension to minimize the total daily charging cost of electric vehicles present in the parking area of university or any workplace. The uncertainties about each trip distances and daily mileages of EVs are considered and 2m Point Estimation Method (2m-PEM) is used to tackle this stochastic framework. Moreover, in this proposed algorithm both V2G and G2V technology is contemplated simultaneously. Soft computing technique is integrated with this algorithm in order to minimize the complexity of the charging management problem. Two test cases, containing lower battery capacity vehicles and higher battery capacity vehicles are considered and the proposed algorithm is applied. Later, the robustness of the algorithm is verified using Wilcoxon Signed rank test (WSRT).
机译:本文旨在制定一种充电调度算法(CSA),旨在最大程度地减少大学停车场或任何工作场所中存在的电动汽车的每日总充电成本。考虑了电动汽车每次行驶距离和每日行驶里程的不确定性,并使用2m点估计方法(2m-PEM)来解决此随机框架。此外,在该提出的算法中,同时考虑了V2G和G2V技术。软计算技术与该算法集成在一起,以最大程度地减少充电管理问题的复杂性。考虑了两个测试案例,分别包含较低电池容量的车辆和较高电池容量的车辆,并应用了所提出的算法。后来,使用Wilcoxon符号秩检验(WSRT)验证了该算法的鲁棒性。

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