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Charging optimization for electric vehicles in large Park Ride areas

机译:大型公园和乘车区电动汽车的充电优化

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Smart mobility can be achieved through the logistic and energy optimization of multimodal transportation systems where electric vehicles (EVs) play an important role. Suitable locations to realize this integration are suburbs of metropolitan areas of large cities where Park & Ride (P&R) facilities permit public transport connections with private mobility. The paper presents the application of a linear programming optimization algorithm to a P&R equipped with charging stations. The initial state of charge of every EV and the park occupancy have been randomly selected according to real mobility scenarios. Thus, the effectiveness of the algorithm has been tested comparing the obtained optimal load profile of the P&R facility with the non-optimal one. It has been proved that the maximum power requested by the P&R can be dramatically reduced by optimizing the recharge. This leads to a downsizing of the distribution devices (e.g., power transformer) and a significant increase in efficiency.
机译:通过在电动汽车(EV)发挥重要作用的多式联运系统中进行物流和能源优化,可以实现智能出行。实现这种整合的合适地点是大城市郊区,Park&Ride(P&R)设施允许公共交通与私人出行相连。本文介绍了线性规划优化算法在装备充电站的P&R中的应用。根据实际出行场景随机选择了每个电动汽车的初始充电状态和公园占用率。因此,已经对算法的有效性进行了测试,将获得的P&R设施的最佳负荷曲线与非最佳负荷曲线进行了比较。事实证明,通过优化充电,可以大幅降低P&R要求的最大功率。这导致分配装置(例如,电力变压器)的尺寸减小并且效率显着提高。

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