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Allocation of e-car charging: Assessing the utilization of charging infrastructures by location

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  • Baresch, Martin
  • Moser, Simon

Abstract

The availability of charging infrastructure forms the basis for the enforced market penetration of electric cars. This paper aims to examine the distribution of the allocation of future charges to the various types of charging stations in order to provide a starting point for the evaluation of the need for charging infrastructure, i.e. its number, design and cost-effectiveness. For the case study of Austria, a new approach to derive the allocation of charging processes is applied by using demographic variables and decision rules. As a result, it is found that 88% of the charges are conducted when the user is at home, which is in line with literature. Significantly, fewer charges, approx. 8.8%, are carried out at the workplace. It is a relevant finding of this work relativizing the importance of workplace charging. Only 1.7% are conducted at ‘public charging infrastructure’ (e.g. public car parks, supermarkets, etc.; note that also charging at assigned and freely selectable parking lots when the owner is at home is considered home charging) and 1.5% are conducted at ‘fast charging infrastructure’ (i.e. motorways). It is observed that ‘public charging infrastructure’ is only used for a small proportion of the charges. In conclusion, it can be deduced that some ‘public’ or fast charging stations are unable to recover their costs through charging processes but are subject to other business models.

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  • Baresch, Martin & Moser, Simon, 2019. "Allocation of e-car charging: Assessing the utilization of charging infrastructures by location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 388-395.
    • Handle: RePEc:eee:transa:v:124:y:2019:i:c:p:388-395
    DOI: 10.1016/j.tra.2019.04.009
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    2. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    3. Chen, Jiahui & Wang, Fang & He, Xiaoyi & Liang, Xinyu & Huang, Junling & Zhang, Shaojun & Wu, Ye, 2022. "Emission mitigation potential from coordinated charging schemes for future private electric vehicles," Applied Energy, Elsevier, vol. 308(C).
    4. Antonia Golab & Sebastian Zwickl-Bernhard & Hans Auer, 2022. "Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network," Energies, MDPI, vol. 15(6), pages 1-26, March.
    5. Wang, Dong & Liao, Feixiong & Gao, Ziyou & Tian, Qiong, 2022. "Analysis of activity duration-related charging behavioral responses of electric vehicle travelers to charging services," Transport Policy, Elsevier, vol. 123(C), pages 73-81.
    6. José F. C. Castro & Davidson C. Marques & Luciano Tavares & Nicolau K. L. Dantas & Amanda L. Fernandes & Ji Tuo & Luiz H. A. de Medeiros & Pedro Rosas, 2022. "Energy and Demand Forecasting Based on Logistic Growth Method for Electric Vehicle Fast Charging Station Planning with PV Solar System," Energies, MDPI, vol. 15(17), pages 1-21, August.
    7. Preuß, Sabine & Kunze, Robert & Zwirnmann, Jakob & Meier, Jonas & Plötz, Patrick & Wietschel, Martin, 2021. "The share of renewable electricity in electric vehicle charging in Europe is higher than grid mix," Working Papers "Sustainability and Innovation" S11/2021, Fraunhofer Institute for Systems and Innovation Research (ISI).
    8. Sutton, Katrina & Hardman, Scott & Tal, Gil, 2022. "Strategies to Reduce Congestion and Increase Access to Electric Vehicle Charging Stations at Workplaces," Institute of Transportation Studies, Working Paper Series qt2345r48k, Institute of Transportation Studies, UC Davis.
    9. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.

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