Risk Assessment Framework for Electric Vehicle Charging Station Development in the United States as an Ancillary Service

Francisco Haces-Fernandez ()
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Francisco Haces-Fernandez: College of Business Administration, Texas A&M University Kingsville, Kingsville, TX 78363, USA

Energies, 2023, vol. 16, issue 24, 1-17

Abstract: Promoting the accelerated adoption of electric vehicles (EVs) in the United States (US) is one of the main strategies for reducing risk related to climate change. However, the lack of public charging stations (EVCSs) in the US has been identified as a grave obstacle to EV market penetration. The US Federal Government is providing extensive financial incentives to promote EVCSs. This allows diverse businesses to offer EV charging as an ancillary service, without the risks associated with traditional fuel facilities. Locations offering these novel services will reduce their financial operational risks, increase customer traffic and receive additional revenue. However, selecting unsuitable equipment for particular business segments and locations creates a severe risk of underuse and disrepair, leading to the potential failure of these new projects. Furthermore, these unsuccessful EVCSs exacerbate consumer reluctance to EV adoption and foster social opposition to this new technology. This study provides stakeholders with a framework for the optimal placement of EVCSs to maximize their successful deployment and incentivize continuous growth in the EV market. It identifies risk factors related to the placement and operation of EVCSs, aiding in optimal equipment selection for each location. Results from this study highlight EVCS location trends based on location and type of business, with the potential for some retrofitting projects. This framework provides relevant geospatial results for business owners, policy makers, consumers and other stakeholders in the adequacy of new charging infrastructure.

Keywords: electric vehicles (EVs); electric vehicle charging stations (EVCSs); ancillary revenue; geospatial analysis; business intelligence; big data analytics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (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)

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