A data-driven approach to managing electric vehicle charging infrastructure in parking lots

被引:32
作者
Babic, Jurica [1 ]
Carvalho, Arthur [2 ]
Ketter, Wolfgang [3 ]
Podobnik, Vedran [1 ]
机构
[1] Univ Zagreb, Fac Elect Engn & Comp, Unska 3, HR-10000 Zagreb, Croatia
[2] Miami Univ, Farmer Sch Business, 83 N Patterson Ave, Oxford, OH 45056 USA
[3] Univ Cologne, Fac Management Econ & Social Sci, Universitaetsstrasse 24, North Rhine Westphalia, D-50931 Cologne, Germany
关键词
Electric vehicles; Charging infrastructure; Parking lots; Data analytics; Simulation; OPERATIONS MANAGEMENT; RENEWABLE ENERGY; CHOICE BEHAVIOR; RANGE ANXIETY; SIMULATION; OPTIMIZATION; PREFERENCES; INCENTIVES; STRATEGIES; ALLOCATION;
D O I
10.1016/j.trd.2022.103198
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Underdeveloped charging infrastructure is one of the main barriers to widespread electric vehicle (EV) adoption. Transforming parking lots into EV-enabled parking lots that provide parking and charging services is a solution to address the need for charging stations. Two challenges arise in this context: (1) how many parking spots should receive EV chargers and (2) how to appropriately define the pricing scheme for the charging service. We tackle these challenges by proposing a novel framework based on discrete-event simulations and queueing theory. Our framework can estimate the ideal number of EV chargers and the charging price. These estimates can be used to determine the profitability and payback period for transforming traditional parking lots into EV-enabled parking lots. We illustrate the use of our framework in a real-world setting involving the city of Melbourne, Australia. When doing so, we derive a new methodology to measure the range anxiety phenomenon.
引用
收藏
页数:26
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