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A Decision Function Based Smart Charging and Discharging Strategy for Electric Vehicle in Smart Grid

机译:智能电网中基于决策函数的电动汽车智能充放电策略

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摘要

As the number of Electric Vehicle (EV) increases, the uncontrolled EV charging behaviors may cause the grid load fluctuations and other passive effects. In order to balance the EV charging load in the smart grid, an Electric Vehicle Charging and Discharging Strategy (EVCDS) which is based on a Charging Decision Function (CDF) as well as a Discharging Decision Function (DDF) is proposed. In the CDF and DDF, there are three sub-functions related to the residual energy of battery, EV's charging habits, and the charging efficiency of charging station respectively. The residual energy of battery is used to calculate the excepted probability that satisfies the user's mileage requirement, which is an important factor. The charging habits are used to calculate the second sub-function value, which stands for the user's comfort. The charging efficiency and distance are combined together to calculate the third sub-function. All the sub-functions are weighted and combined into the CDF and DDF to decide whether to charge, discharge or do nothing. In the numerical results, we set up a scenario for the commercial vehicles and private vehicles. After compared with other strategies, EVCDS performs well in terms of reducing the charging demand fluctuations and improving the charging demand balance among charging stations.
机译:随着电动汽车(EV)数量的增加,不受控制的EV充电行为可能会导致电网负载波动和其他被动影响。为了平衡智能电网中的EV充电负荷,提出了一种基于充电决策函数(CDF)和放电决策函数(DDF)的电动汽车充放电策略(EVCDS)。在CDF和DDF中,存在三个子功能,分别与电池的剩余能量,电动汽车的充电习惯和充电站的充电效率有关。电池的剩余能量用于计算满足用户里程要求的例外概率,这是一个重要因素。充电习惯用于计算第二子功能值,代表用户的舒适度。充电效率和距离结合在一起以计算第三子功能。所有子功能都经过加权,然后组合到CDF和DDF中,以决定是充电,放电还是不执行任何操作。在数值结果中,我们为商用车和私家车建立了一个场景。与其他策略相比,EVCDS在减少充电需求波动和改善充电站之间的充电需求平衡方面表现良好。

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