考虑负效应最小化的电动汽车补能诱导研究

随着政府对“碳达峰”等环保政策的贯彻落实，电动汽车凭借节能环保等优点得到了迅速发展。由于电动汽车续航里程短，充电时间长，且路网中的补能需求与充电桩存在时空错配的现象，导致电动汽车补能排队时间长和驾驶员产生里程焦虑等一系列负效应。为此，本文首先从整体路网补能负效应最小化角度，通过引入激励手段实现最优补能方案，建立电动汽车补能诱导双层优化模型。其中，上层为路网补能负效应最小化诱导激励模型；下层为带有补能站点选择的混行路网均衡模型。然后，采用遗传算法求解上层模型，下层模型通过Frank-Wolfe算法求解，得出路网中补能车辆的最优诱导方案。最后，以经典Nguyen-Dupius路网为例验证模型，并进行灵敏度分析。结果表明，尽管本文提出的补能诱导模型增加了规划者的激励成本，但总社会补能负效应成本降低，证明了补能诱导的有效性。

Electric Vehicle Charging Induction with Minimization of Negative Effects

With the implementation of environmental protection policies such as "carbon peak" by the government, electric vehicles (EV) have developed rapidly with the advantages of energy saving and environmental friendliness. Due to the short cruising range, long charging time of electric vehicles and the space-time mismatch between recharge demand and charging pile in the road network, a series of negative effects have been found such as long queuing time and drivers' anxiety on the mileage ranges. This paper introduces incentives to achieve the optimal energy supplementation scheme and proposes a bi-level optimization model for electric vehicle energy supplementation to minimize the negative effect of EV charging on the road network. The upper layer is the induced excitation model which minimizes the negative effect of road network charging. The lower layer is a mixed road network equalization model with charging station selection. The genetic algorithm is designed to solve the upper model and the Frank-Wolfe algorithm is used to solve the lower model to obtain the optimal induction scheme of the energy-replenishing vehicles in the road network. The classic Nguyen-Dupius road network is taken as an example to verify and conduct sensitivity analysis. The results show that although the proposed charging induction model increases the incentive cost of planners, the total social charging negative effect cost is reduced, which proves the effectiveness of this model.