Two-stage stochastic programming model to locate capacitated EV-charging stations in urban areas under demand uncertainty
Two-stage stochastic programming model to locate capacitated EV-charging stations in urban areas under demand uncertainty
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DOI：
10.1016/j.ejtl.2020.100025
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
S. A. MirHassani;A. Khaleghi;F. Hooshmand
中科院分区：
文献类型：
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作者：
S. A. MirHassani;A. Khaleghi;F. Hooshmand
由于化石燃料的危险影响，政策制定者倾向于用电动汽车取代化石燃料汽车。因此，为用户提供便利的充电站网络的优化设计具有重要意义。针对城市充电站选址问题，提出了一种两阶段随机规划模型。建筑物周围白天可供人参观的停车场被认为是可能安装充电器的地点。考虑到需求是一个不确定的参数，该模型确定了应该配备充电器的停车场以及每个停车场必须放置的充电器的数量和类型。该模型在一个市中心地区的数据集上进行了检验，并使用了一种基于Bders分解的高效启发式算法来求解该模型。结果表明，启发式方法可以在较短的时间内找到近似最优解(最优性差距最大为0.05%)。
Due to the dangerous effects of fossil fuels, policymakers tend to substitute fossil-fuel-based vehicles with electric ones. Thus, the optimal design of a charging station network providing convenient access for the users is of great importance. This paper presents a two-stage stochastic programming model for the problem of locating charging stations in urban areas. Parking lots around the buildings which may be visited by people during the day are considered as potential locations for charger installation. The model determines the parking lots that should be equipped with chargers and the number as well as the type of chargers that must be placed in each parking lot considering the demand as an uncertain parameter. The proposed model is examined on the dataset of a midtown area, taken from the literature, and an efficient heuristic algorithm based on Benders decomposition is utilized to solve the model. The results indicate that the heuristic method can find a near-optimal solution (with the optimality gap of at most 0.05%) in a short time.