IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1445-d1358686.html
   My bibliography  Save this article

A Model for Electrifying Fire Ambulance Service Stations Considering Practical Service Data and Charging Strategies

Author

Listed:
  • Yih-Her Yan

    (Department of Electrical Engineering, National Formosa University, Yunlin County 632, Taiwan)

  • Rong-Ceng Leou

    (Department of Electrical Engineering, National Formosa University, Yunlin County 632, Taiwan)

  • Chien-Chin Ko

    (Mituo Branch of Fire Bureau, Kaohsiung City Government, Kaohsiung 833, Taiwan)

Abstract

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs’ return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs.

Suggested Citation

  • Yih-Her Yan & Rong-Ceng Leou & Chien-Chin Ko, 2024. "A Model for Electrifying Fire Ambulance Service Stations Considering Practical Service Data and Charging Strategies," Energies, MDPI, vol. 17(6), pages 1-13, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1445-:d:1358686
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1445/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/6/1445/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ma, Shao-Chao & Fan, Ying, 2020. "A deployment model of EV charging piles and its impact on EV promotion," Energy Policy, Elsevier, vol. 146(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    2. Li, Jingjing & Nian, Victor & Jiao, Jianling, 2022. "Diffusion and benefits evaluation of electric vehicles under policy interventions based on a multiagent system dynamics model," Applied Energy, Elsevier, vol. 309(C).
    3. Zunian Luo, 2022. "Powering Up a Slow Charging Market: How Do Government Subsidies Affect Charging Station Supply?," Papers 2210.14908, arXiv.org, revised Jan 2023.
    4. Yanting Gu & Fiorenza Belussi & Rajneesh Narula, 2023. "Entering European countries: advantages and difficulties for Chinese electric vehicle firms," "Marco Fanno" Working Papers 0302, Dipartimento di Scienze Economiche "Marco Fanno".
    5. Wang, Yitong & Fan, Ruguo & Du, Kang & Bao, Xuguang, 2023. "Exploring incentives to promote electric vehicles diffusion under subsidy abolition: An evolutionary analysis on multiplex consumer social networks," Energy, Elsevier, vol. 276(C).
    6. Chen, Rongkai & Fan, Ruguo & Wang, Dongxue & Yao, Qianyi, 2023. "Effects of multiple incentives on electric vehicle charging infrastructure deployment in China: An evolutionary analysis in complex network," Energy, Elsevier, vol. 264(C).
    7. Li, Yina & Liang, Chenchen & Ye, Fei & Zhao, Xiande, 2023. "Designing government subsidy schemes to promote the electric vehicle industry: A system dynamics model perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    8. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    9. Liu, Bingchun & Song, Chengyuan & Wang, Qingshan & Zhang, Xinming & Chen, Jiali, 2022. "Research on regional differences of China's new energy vehicles promotion policies: A perspective of sales volume forecasting," Energy, Elsevier, vol. 248(C).
    10. Konstantina Anastasiadou & Nikolaos Gavanas, 2022. "State-of-the-Art Review of the Key Factors Affecting Electric Vehicle Adoption by Consumers," Energies, MDPI, vol. 15(24), pages 1-23, December.
    11. Liu, Bingchun & Song, Chengyuan & Liang, Xiaoqin & Lai, Mingzhao & Yu, Zhecheng & Ji, Jie, 2023. "Regional differences in China's electric vehicle sales forecasting: Under supply-demand policy scenarios," Energy Policy, Elsevier, vol. 177(C).
    12. Zhu, Min & Dong, Peiwu & Ju, Yanbing & Li, Jiajun & Ran, Lun, 2023. "Effects of government subsidies on heavy-duty hydrogen fuel cell truck penetration: A scenario-based system dynamics model," Energy Policy, Elsevier, vol. 183(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1445-:d:1358686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.