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Battery-Conscious, Economic, and Prioritization-Based Electric Vehicle Residential Scheduling

Author

Listed:
  • Jordan P. Sausen

    (Center of Excellence in Energy and Power Systems, Federal University of Santa Maria, Santa Maria 97105-900, Brazil)

  • Alzenira R. Abaide

    (Center of Excellence in Energy and Power Systems, Federal University of Santa Maria, Santa Maria 97105-900, Brazil)

  • Juan C. Vasquez

    (Center for Research on Microgrids, Aalborg Universitet, 9000 Aalborg, Denmark)

  • Josep M. Guerrero

    (Center for Research on Microgrids, Aalborg Universitet, 9000 Aalborg, Denmark)

Abstract

Advances in communication technologies and protocols among vehicles, charging stations, and controllers have enabled the application of scheduling techniques to prioritize EV fleet charging. From the perspective of users, residential EV charging must particularly address cost-effective solutions to use energy more efficiently and preserve the lifetime of the battery—the most expensive element of an EV. Considering this matter, this research addresses a residential EV charging scheduling model including battery degradation aspects when discharging. Due to the non-linear characteristics of charging and battery degradation, we consider a mixed integer non-linearly constrained formulation with the aim of scheduling the charging and discharging of EVs to satisfy the following goals: prioritizing charging, reducing charging costs and battery degradation, and limiting the power demand requested to the distribution transformer. The results shows that, when EVs are discharged before charging up within a specific state-of-charge range, degradation can be reduced by 5.3%. All charging requests are completed before the next-day departure time, with 16.35% cost reduction achieved by scheduling charging during lower tariff prices, in addition to prevention of overloading of the distribution transformer.

Suggested Citation

  • Jordan P. Sausen & Alzenira R. Abaide & Juan C. Vasquez & Josep M. Guerrero, 2022. "Battery-Conscious, Economic, and Prioritization-Based Electric Vehicle Residential Scheduling," Energies, MDPI, vol. 15(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3714-:d:818792
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    References listed on IDEAS

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    1. Evgeny Nefedov & Seppo Sierla & Valeriy Vyatkin, 2018. "Internet of Energy Approach for Sustainable Use of Electric Vehicles as Energy Storage of Prosumer Buildings," Energies, MDPI, vol. 11(8), pages 1-18, August.
    2. Seyfettin Vadi & Ramazan Bayindir & Alperen Mustafa Colak & Eklas Hossain, 2019. "A Review on Communication Standards and Charging Topologies of V2G and V2H Operation Strategies," Energies, MDPI, vol. 12(19), pages 1-27, September.
    3. Bonges, Henry A. & Lusk, Anne C., 2016. "Addressing electric vehicle (EV) sales and range anxiety through parking layout, policy and regulation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 83(C), pages 63-73.
    4. Paterakis, Nikolaos G. & Gibescu, Madeleine, 2016. "A methodology to generate power profiles of electric vehicle parking lots under different operational strategies," Applied Energy, Elsevier, vol. 173(C), pages 111-123.
    5. Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
    6. Xu, Zhiwei & Hu, Zechun & Song, Yonghua & Zhao, Wei & Zhang, Yongwang, 2014. "Coordination of PEVs charging across multiple aggregators," Applied Energy, Elsevier, vol. 136(C), pages 582-589.
    7. Schoch, Jennifer & Gaerttner, Johannes & Schuller, Alexander & Setzer, Thomas, 2018. "Enhancing electric vehicle sustainability through battery life optimal charging," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 1-18.
    8. Guo, Fang & Zhang, Jingjing & Huang, Zhihong & Huang, Weilai, 2022. "Simultaneous charging station location-routing problem for electric vehicles: Effect of nonlinear partial charging and battery degradation," Energy, Elsevier, vol. 250(C).
    9. Meng, Jian & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qu, Bo, 2016. "Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system," Applied Energy, Elsevier, vol. 162(C), pages 966-979.
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