文献类型：期刊文献

英文题名：Public recharging infrastructure location strategy for promoting electric vehicles: A bi-level programming approach

作者：Li, Ying[1];Zhang, Pengwei[1];Wu, Yifan[1]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China

年份：2018

卷号：172

起止页码：2720

外文期刊名：JOURNAL OF CLEANER PRODUCTION

收录：;EI(收录号：20171103433580);WOS:【SSCI(收录号:WOS:000423002200127)，SCI-EXPANDED(收录号:WOS:000423002200127)】；

基金：This work was supported in part by the National Natural Science Foundation of China (71471062, 71431004), the Shanghai Pujiang Program (17PJC023), the Shanghai Educational Development Foundation (11CG33) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Recharging infrastructure location; Electric vehicle; Mixed fleet; Bi-level programming

摘要：Governments take an active role in promoting electric vehicles (EVs), but the lack of recharging infrastructures restricts companies to adopt EVs. To promote EVs' penetration in companies, a suitable public recharging infrastructure grid should be systematically designed by governments. This paper proposes a public recharging infrastructure location strategy for governments based on the bi-level programming. In the upper-level problem, the government optimizes his location strategy, i.e., selects infrastructures from candidate locations, to minimize the construction budget and meet desired EV adoption rate. In the lower-level problem, the company decides the percentage of the electric vehicles in her mixed fleet and the corresponding vehicle routing plan to minimize her operational cost utilizing the infrastructures constructed by the government. A two-phase heuristic combining variable neighborhood descent and scatter search is presented to solve the problem. The hybrid method hires scatter search to derive the optimal routing plan of mixed fleet and variable neighborhood descent to select infrastructure locations. The proposed method is examined against Cplex using benchmark instances. The results from extensive numerical studies reveal that the government should thoughtfully determine the desired adoption rate. The short-term optimal locations might be inefficient design for the long run if the rate varies. In order to minimize the budget, the government may not choose the infrastructure locations that are the most beneficial for the company. It's hard to achieve the desired adoption rate while considering the covering areas of the infrastructures merely. The subsidy policy and recharging infrastructure location strategy should be systematically designed to achieve a higher promoting effect with a lower budget. (C) 2017 Elsevier Ltd. All rights reserved.