Optimal placement and sizing of charging infrastructure for EVs under information-sharing
Christian Anker Vandet and
Jeppe Rich
Technological Forecasting and Social Change, 2023, vol. 187, issue C
Abstract:
In the next decade, charging demand from an increasing number of electric vehicles will require the charging infrastructure to be further developed. However, the planning of an optimal charging infrastructure is a complex problem as it involves representation of charging demand in space and time, interaction with supply through queuing models and optimisation of placements and sizing of charging stations. The paper takes on this challenge by proposing how trip diaries can be used to develop a space–time demand simulator for electric vehicle movements and be integrated with models for optimal locations of charging stations. In the paper, charging demand is integrated with an information-sharing system, which pass waiting time predictions from the system to the users. An approximation of expected waiting time, depending on generic station specific inputs, is derived from queuing theory. The methodology is applied to the city of Copenhagen and it is found that information sharing lead to better utilisation of charging capacity. Even in a situation where 50% of the population share information, the system performance is almost on par with a situation where all agents are informed. The paper underlines the need to for information sharing in the planning of future charging systems.
Keywords: Charging location and sizing; Agent-based simulation; G/G/c queuing systems; Optimisation; Information-sharing; Transportation modelling (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162522007260
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:187:y:2023:i:c:s0040162522007260
DOI: 10.1016/j.techfore.2022.122205
Access Statistics for this article
Technological Forecasting and Social Change is currently edited by Fred Phillips
More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().