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A bi-level optimisation framework for electric vehicle fleet charging management

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  • Škugor, Branimir
  • Deur, Joško

Abstract

The paper proposes a bi-level optimisation framework for Electric Vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final State-of-Charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:184:y:2016:i:c:p:1332-1342
    DOI: 10.1016/j.apenergy.2016.03.091
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    References listed on IDEAS

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    Cited by:

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    3. Abbasi, Mohammad Hossein & Taki, Mehrdad & Rajabi, Amin & Li, Li & Zhang, Jiangfeng, 2019. "Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach," Applied Energy, Elsevier, vol. 239(C), pages 1294-1307.
    4. Thomas Märzinger & David Wöss & Petra Steinmetz & Werner Müller & Tobias Pröll, 2021. "Novel Modelling Approach for the Calculation of the Loading Performance of Charging Stations for E-Trucks to Represent Fleet Consumption," Energies, MDPI, vol. 14(12), pages 1-15, June.
    5. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.
    6. Wang, Kang & Wang, Haixin & Yang, Zihao & Feng, Jiawei & Li, Yanzhen & Yang, Junyou & Chen, Zhe, 2023. "A transfer learning method for electric vehicles charging strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 343(C).
    7. Miao, Hongzhi & Jia, Hongfei & Li, Jiangchen & Qiu, Tony Z., 2019. "Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology," Energy, Elsevier, vol. 169(C), pages 797-818.
    8. Mehta, R. & Verma, P. & Srinivasan, D. & Yang, Jing, 2019. "Double-layered intelligent energy management for optimal integration of plug-in electric vehicles into distribution systems," Applied Energy, Elsevier, vol. 233, pages 146-155.
    9. Zhang, Yaoli & Liu, Xingyu & Wei, Wenshen & Peng, Tianji & Hong, Gang & Meng, Chao, 2020. "Mobile charging: A novel charging system for electric vehicles in urban areas," Applied Energy, Elsevier, vol. 278(C).
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