IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v272y2020ics0306261920306656.html
   My bibliography  Save this article

Electric vehicle based smart e-mobility system – Definition and comparison to the existing concept

Author

Listed:
  • Pavić, Ivan
  • Pandžić, Hrvoje
  • Capuder, Tomislav

Abstract

The existing models designed to reap the benefits of electric vehicles’ flexibility in the literature almost exclusively identify charging stations as active players exploiting this flexibility. Such stations are seen as static loads able to provide flexibility only when electric vehicles are connected to them. This standpoint, however, suffers from two major issues. First, the charging stations need to anticipate important parameters of the incoming vehicles, e.g. time of arrival/departure, state-of-energy at arrival/departure. Second, it interacts with vehicles only when connected to a specific charging station, thus overlooking the arbitrage opportunities when they are connected to other stations. This conventional way of addressing the electric vehicles is referred to as charging station-based e-mobility system. A new viewpoint is presented in this paper, where electric vehicles are observed as dynamic movable storage that can provide flexibility at any charging station. The paper defines both the existing system, where the flexibility is viewed from the standpoint of charging stations, and the proposed one, where the flexibility is viewed from the vehicles’ standpoint. The both concepts are mathematically formulated as linear optimization programs and run over a simple case study to numerically evaluate the differences. Each of the four issues identified are individually examined and omission of corresponding constraints is analysed and quantified. The main result is that the proposed system yields better results for the vehicle owners.

Suggested Citation

  • Pavić, Ivan & Pandžić, Hrvoje & Capuder, Tomislav, 2020. "Electric vehicle based smart e-mobility system – Definition and comparison to the existing concept," Applied Energy, Elsevier, vol. 272(C).
  • Handle: RePEc:eee:appene:v:272:y:2020:i:c:s0306261920306656
    DOI: 10.1016/j.apenergy.2020.115153
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920306656
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.115153?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    2. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    3. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2015. "Value of flexible electric vehicles in providing spinning reserve services," Applied Energy, Elsevier, vol. 157(C), pages 60-74.
    4. Michael Wolinetz & Jonn Axsen & Jotham Peters & Curran Crawford, 2018. "Simulating the value of electric-vehicle–grid integration using a behaviourally realistic model," Nature Energy, Nature, vol. 3(2), pages 132-139, February.
    5. Yanyan Xu & Serdar Çolak & Emre C. Kara & Scott J. Moura & Marta C. González, 2018. "Planning for electric vehicle needs by coupling charging profiles with urban mobility," Nature Energy, Nature, vol. 3(6), pages 484-493, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesco Simmini & Tommaso Caldognetto & Mattia Bruschetta & Enrico Mion & Ruggero Carli, 2021. "Model Predictive Control for Efficient Management of Energy Resources in Smart Buildings," Energies, MDPI, vol. 14(18), pages 1-19, September.
    2. Hassam ur Rehman & Jan Diriken & Ala Hasan & Stijn Verbeke & Francesco Reda, 2021. "Energy and Emission Implications of Electric Vehicles Integration with Nearly and Net Zero Energy Buildings," Energies, MDPI, vol. 14(21), pages 1-30, October.
    3. Armando Cartenì & Ilaria Henke & Clorinda Molitierno & Luigi Di Francesco, 2020. "Strong Sustainability in Public Transport Policies: An e-Mobility Bus Fleet Application in Sorrento Peninsula (Italy)," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
    4. Zhang, Yuanjian & Gao, Bingzhao & Jiang, Jingjing & Liu, Chengyuan & Zhao, Dezong & Zhou, Quan & Chen, Zheng & Lei, Zhenzhen, 2023. "Cooperative power management for range extended electric vehicle based on internet of vehicles," Energy, Elsevier, vol. 273(C).
    5. Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
    6. Najafi, Arsalan & Jasiński, Michał & Leonowicz, Zbigniew, 2022. "A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem," Energy, Elsevier, vol. 249(C).

    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. Milan Straka & Rui Carvalho & Gijs van der Poel & v{L}ubov{s} Buzna, 2020. "Explaining the distribution of energy consumption at slow charging infrastructure for electric vehicles from socio-economic data," Papers 2006.01672, arXiv.org, revised Jun 2020.
    2. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    3. Siobhan Powell & Gustavo Vianna Cezar & Liang Min & Inês M. L. Azevedo & Ram Rajagopal, 2022. "Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption," Nature Energy, Nature, vol. 7(10), pages 932-945, October.
    4. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    5. Dingyi Lu & Yunqian Lu & Kexin Zhang & Chuyuan Zhang & Shao-Chao Ma, 2023. "An Application Designed for Guiding the Coordinated Charging of Electric Vehicles," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    6. Mowry, Andrew M. & Mallapragada, Dharik S., 2021. "Grid impacts of highway electric vehicle charging and role for mitigation via energy storage," Energy Policy, Elsevier, vol. 157(C).
    7. Crozier, Constance & Morstyn, Thomas & McCulloch, Malcolm, 2020. "The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems," Applied Energy, Elsevier, vol. 268(C).
    8. Groppi, Daniele & Feijoo, Felipe & Pfeifer, Antun & Garcia, Davide Astiaso & Duic, Neven, 2023. "Analyzing the impact of demand response and reserves in islands energy planning," Energy, Elsevier, vol. 278(C).
    9. Heuberger, Clara F. & Bains, Praveen K. & Mac Dowell, Niall, 2020. "The EV-olution of the power system: A spatio-temporal optimisation model to investigate the impact of electric vehicle deployment," Applied Energy, Elsevier, vol. 257(C).
    10. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.
    11. Malik, Anam & Ravishankar, Jayashri, 2018. "A hybrid control approach for regulating frequency through demand response," Applied Energy, Elsevier, vol. 210(C), pages 1347-1362.
    12. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    13. Teng, Fei & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Zeng, Pingliang & Strbac, Goran, 2017. "Challenges on primary frequency control and potential solution from EVs in the future GB electricity system," Applied Energy, Elsevier, vol. 194(C), pages 353-362.
    14. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    15. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    16. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    17. Jenkins, J.D. & Zhou, Z. & Ponciroli, R. & Vilim, R.B. & Ganda, F. & de Sisternes, F. & Botterud, A., 2018. "The benefits of nuclear flexibility in power system operations with renewable energy," Applied Energy, Elsevier, vol. 222(C), pages 872-884.
    18. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    19. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    20. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(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:eee:appene:v:272:y:2020:i:c:s0306261920306656. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.