Genetic Algorithm for Optimal Charge Scheduling of Electric Vehicle Fleet
Genetic Algorithm for Optimal Charge Scheduling of Electric Vehicle Fleet
复制标题
电动汽车车队充电优化调度的遗传算法
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Maach
中科院分区：
文献类型：
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作者：
Mabrouk Elmehdi;A. Maach
电动汽车（EV）正在逐渐征服更多的道路并取代污染物的常规车辆。它们似乎被用来将能量存储在干净和智能的网格中，以减轻温室气体排放并消除有害的峰值负载。本文建立了一种随机程序，用于建模和分析电动汽车（EV）机队，以产生准确的充电和放电轮廓。为了管理电动汽车车队，我们使用单目标优化，即遗传算法（GA）确定车队中每个EV的最佳充电/放电计划。拟议的优化允许通过在低电力价格期间安排充电模式和在高功率价格期间放电模式，使V2G和G2V操作之间的最佳权衡从EV电池中高度提高收益。此外，我们比较了我们的方法，该方法考虑了最初的电荷（SOC），到达和离开时间的随机性，并在工作场所和家庭停车场中为每个连接的EV到电网和电动汽车电池组的特征，并带有一个幼稚的充电策略。
Electric Vehicles (EV) are gradually conquering more roads and replacing pollutant conventional vehicles. They seem to be used to store energy in clean and smart grids to mitigate greenhouse gas emissions and eliminate harmful peak loads. This paper establishes a stochastic procedure for modeling and analyzing an electric vehicle (EV) fleet to generate an accurate charging and discharging profiles. For the purpose of managing the EV fleet we use a single-objective optimization, namely, the Genetic Algorithm (GA) to determine the optimal charging/discharging schedule for each EV in the fleet. The proposed optimization allows to make the optimal tradeoff between V2G and G2V operations cost to highly increase benefits from EV batteries by scheduling the charging mode in the low power price periods and discharging mode in the high-power price periods. Moreover, we compare our approach that considers the stochastic nature in the initial state-of-charge (SOC), arriving and departing times to the grid and the characteristic of EV battery packs for each connected EV in workplace and home parking lots, with a naive charging strategy.