A novel data-driven joint model enhances infrastructure planning and smart charging of shared electric vehicles

To address the high upfront cost of electric vehicles (EVs), attempts have been made to introduce EVs into the carsharing system at the early stage of transportation electrification. For electric carsharing systems, infrastructure planning and operation are not only essential components but are also interconnected. A recent breakthrough study presented by researchers from the Hong Kong Polytechnic University introduces a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles (SEVs). This advanced method promises to explore how to effectively plan or manage the electric carsharing system from both the users' and operators’ perspectives.

This study is focused on infrastructure planning and smart charging strategies for SEVs, which are of great importance and relevant to several stakeholders (e.g., SEV operators and grid companies). The data-driven joint model can deploy to rental stations and chargers, and operate these facilities simultaneously, would be helpful for SEV operators and grid companies in their decision-making, such as infrastructure planning (e.g., rental and charging stations), policy-making (e.g., electricity price), and technology investment (e.g., V2G technology).

The data-driven optimization approach comprises two components: data preprocessing and simulation. The data preprocessing is used to extract parking and charging events from real-world SEV trajectory data, which are used to identify rental stations through a clustering algorithm. Further, the parking and charging events, as well as the added rental stations, are used as inputs for the simulation.

Smart strategies could influence SEV users’ behavior and further electricity demand and cost. The model takes into account two prevalent smart charging strategies: The Time of-Use (TOU) tariff and Vehicle-to-Grid (V2G) technology. The TOU strategy incentivizes SEVs to charge during off-peak periods, while the V2G strategy encourages SEVs to charge during off-peak periods and discharge during peak periods.

Their findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation For SEV operators, the use of TOU and V2G strategies could potentially reduce charging costs by 17.93% and 34.97% respectively. In the scenarios with V2G applied, the average discharging demand is 2.15 kWh per day per SEV, which accounts for 42.02% of the actual average charging demand of SEVs.

In future, researchers must work diligently on two fronts. First, 52.36% of parking events in the current scenario do not have charging events associated, indicating that these parked SEVs could also get recharged, for example, through more chargers deployed or introducing autonomous vehicles into carsharing system. Specifically, researchers could further explore the potential of autonomous SEVs which could automatically drive to rental stations with chargers available and get recharged by themselves. Second, the micro-simulation approach could be further improved by considering SEV users’ heterogeneous preferences and needs. Different SEV users might have different preferences towards charging cost and time, which might vary across activity types (e.g., shopping and leisure) that they would perform. Such detailed behavioral rules could be incorporated into the micro-simulation approach, so as to improve model realism.

Reference

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[2] Das Himadry Shekhar, Rahman Mohammad Mominur, Li S, Tan CW. Electric vehicles standards, charging infrastructure, and impact on grid integration: a technological review. Renew Sustain Energy Rev 2020;120:109618.

[3] Li Chaojie, Dong Zhaoyang, Chen Guo, Zhou Bo, Zhang Jingqi, Yu Xinghuo. Datadriven planning of electric vehicle charging infrastructure: a case study of sydney, Australia. IEEE Trans Smart Grid 2021;12(4):3289–304.

Author: Junbei Liu, Xiong Yang, Chengxiang Zhuge

Title of original paper: A joint model of infrastructure planning and smart charging strategies for shared electric vehicles

Article link: https://doi.org/10.1016/j.geits.2024.100168.

Journal: Green Energy and Intelligent Transportation

https://www.sciencedirect.com/science/article/pii/S2773153724000203