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Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption

Author

Listed:
  • Hedayat Saboori

    (Electrical Engineering Department, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Shahram Jadid

    (Electrical Engineering Department, Iran University of Science and Technology, Tehran 16846-13114, Iran)

  • Mehdi Savaghebi

    (Electrical Engineering Section, Department of Mechanical and Electrical Engineering, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark)

Abstract

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

Suggested Citation

  • Hedayat Saboori & Shahram Jadid & Mehdi Savaghebi, 2021. "Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption," Energies, MDPI, vol. 14(3), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:736-:d:490355
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    References listed on IDEAS

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    Cited by:

    1. Hyung-Wook Kang & Hyun-Seong Lee & Jae-Ho Rhee & Kun-A Lee, 2023. "DC Voltage Source Based on a Battery of Supercapacitors with a Regulator in the Form of an Isolated Boost LCC Resonant Converter," Energies, MDPI, vol. 16(18), pages 1-15, September.
    2. Afshar, Shahab & Pecenak, Zachary K. & Barati, Masoud & Disfani, Vahid, 2022. "Mobile charging stations for EV charging management in urban areas: A case study in Chattanooga," Applied Energy, Elsevier, vol. 325(C).
    3. Hedayat Saboori & Shahram Jadid & Mehdi Savaghebi, 2021. "Spatio-Temporal and Power–Energy Scheduling of Mobile Battery Storage for Mitigating Wind and Solar Energy Curtailment in Distribution Networks," Energies, MDPI, vol. 14(16), pages 1-19, August.
    4. Zhehan Zhao & Alireza Soroudi, 2022. "Optimal Deployment of Mobile MSSSC in Transmission System," Energies, MDPI, vol. 15(11), pages 1-27, May.
    5. Mehrjerdi, Hasan & Mahdavi, Sajad & Hemmati, Reza, 2021. "Resilience maximization through mobile battery storage and diesel DG in integrated electrical and heating networks," Energy, Elsevier, vol. 237(C).
    6. Nnaemeka Vincent Emodi & Scott Dwyer & Kriti Nagrath & John Alabi, 2022. "Electromobility in Australia: Tariff Design Structure and Consumer Preferences for Mobile Distributed Energy Storage," Sustainability, MDPI, vol. 14(11), pages 1-18, May.
    7. Golsefidi, Atefeh Hemmati & Hüttel, Frederik Boe & Peled, Inon & Samaranayake, Samitha & Pereira, Francisco Câmara, 2023. "A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    8. Afshar, Shahab & Macedo, Pablo & Mohamed, Farog & Disfani, Vahid, 2021. "Mobile charging stations for electric vehicles — A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).

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