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A novel model of electric vehicle fleet aggregate battery for energy planning studies

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  • Škugor, Branimir
  • Deur, Joško

Abstract

The paper proposes an aggregate battery modelling approach for an (electric vehicle) EV fleet, which is aimed for energy planning studies of EV-grid integration. The proposed model improves on the existing, basic aggregate battery modelling approach by accounting for a variable structure of the aggregate battery systems, variable (state of charge) SoC constraints and specific input time-distributions such as those of average SoC at destination and number of arriving and departing vehicles. In the particular case-study presented, the input distributions are reconstructed from a large set of delivery vehicle fleet driving missions, including simulation of individual vehicle behaviours over the full set of driving cycles. The charging power input is obtained by using a dynamic programming-based optimisation algorithm aimed at finding a global optimum in terms of minimised electricity cost. For the purpose of proposed model validation and its comparison with the basic model, a distributed fleet vehicle model is developed, where a specific algorithm is proposed for distributing the optimised charging power input to charging inputs of individual vehicles.

Suggested Citation

  • Škugor, Branimir & Deur, Joško, 2015. "A novel model of electric vehicle fleet aggregate battery for energy planning studies," Energy, Elsevier, vol. 92(P3), pages 444-455.
    • Handle: RePEc:eee:energy:v:92:y:2015:i:p3:p:444-455
    DOI: 10.1016/j.energy.2015.05.030
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    References listed on IDEAS

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    1. Lund, Henrik & Kempton, Willett, 2008. "Integration of renewable energy into the transport and electricity sectors through V2G," Energy Policy, Elsevier, vol. 36(9), pages 3578-3587, September.
    2. Škugor, Branimir & Deur, Joško, 2015. "Dynamic programming-based optimisation of charging an electric vehicle fleet system represented by an aggregate battery model," Energy, Elsevier, vol. 92(P3), pages 456-465.
    3. Hedegaard, Karsten & Ravn, Hans & Juul, Nina & Meibom, Peter, 2012. "Effects of electric vehicles on power systems in Northern Europe," Energy, Elsevier, vol. 48(1), pages 356-368.
    4. Cardoso, G. & Stadler, M. & Bozchalui, M.C. & Sharma, R. & Marnay, C. & Barbosa-Póvoa, A. & Ferrão, P., 2014. "Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules," Energy, Elsevier, vol. 64(C), pages 17-30.
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    Citations

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    Cited by:

    1. Aziz, Muhammad & Oda, Takuya & Ito, Masakazu, 2016. "Battery-assisted charging system for simultaneous charging of electric vehicles," Energy, Elsevier, vol. 100(C), pages 82-90.
    2. Morteza Nazari-Heris & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "An Updated Review and Outlook on Electric Vehicle Aggregators in Electric Energy Networks," Sustainability, MDPI, vol. 14(23), pages 1-24, November.
    3. Shi You & Junjie Hu & Charalampos Ziras, 2016. "An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †," Energies, MDPI, vol. 9(11), pages 1-18, November.
    4. Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    5. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    6. McPherson, Madeleine & Ismail, Malik & Hoornweg, Daniel & Metcalfe, Murray, 2018. "Planning for variable renewable energy and electric vehicle integration under varying degrees of decentralization: A case study in Lusaka, Zambia," Energy, Elsevier, vol. 151(C), pages 332-346.
    7. Škugor, Branimir & Deur, Joško, 2016. "A bi-level optimisation framework for electric vehicle fleet charging management," Applied Energy, Elsevier, vol. 184(C), pages 1332-1342.
    8. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.

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