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A multi-objective optimization model for EVSE deployment at workplaces with smart charging strategies and scheduling policies

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  • Erdogan, Nuh
  • Kucuksari, Sadik
  • Murphy, Jimmy

Abstract

This study proposes a multi-objective optimization model to determine the optimal charging infrastructure for a transition to plug-in electric vehicles (PEVs) at workplaces. The developed model considers all cost aspects of a workplace charging station, i.e., daily levelized electric vehicle supply equipment (EVSE) infrastructure cost, PEV energy and demand charges. These single-objective functions are aggregated in a multi-objective optimization framework to find the Pareto optimal solutions. Smart charging strategies with interrupted and uninterrupted power profiles are proposed to maximize the use of EVSE units. The charging behavior model is developed based on collected workplace charging data. The model is tested with various scheduling policies to investigate their impact on the behaviors of EVSE types from different perspectives. Finally, a sensitivity analysis is performed to assess the impacts of battery sizes and onboard charger ratings on cost behavior. It is shown that the proposed model can achieve up to 7.8% and 14.6% cost savings as compared to single-objective optimal models and the current charging practice, respectively. The unit cost is found to be more sensitive to scheduling policies than the charging strategies. It is also found that the flexibility ratio policy gives the best PEV scheduling with the lowest unit cost and the most efficient use of the grid assets.

Suggested Citation

  • Erdogan, Nuh & Kucuksari, Sadik & Murphy, Jimmy, 2022. "A multi-objective optimization model for EVSE deployment at workplaces with smart charging strategies and scheduling policies," Energy, Elsevier, vol. 254(PA).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pa:s0360544222010647
    DOI: 10.1016/j.energy.2022.124161
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    References listed on IDEAS

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    1. Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Deveci, Muhammet, 2021. "An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations," Applied Energy, Elsevier, vol. 304(C).
    2. Zhao, Jiayun & Kucuksari, Sadik & Mazhari, Esfandyar & Son, Young-Jun, 2013. "Integrated analysis of high-penetration PV and PHEV with energy storage and demand response," Applied Energy, Elsevier, vol. 112(C), pages 35-51.
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    4. Ramos Muñoz, Edgar & Jabbari, Faryar, 2020. "A decentralized, non-iterative smart protocol for workplace charging of battery electric vehicles," Applied Energy, Elsevier, vol. 272(C).
    5. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
    6. Powell, Siobhan & Kara, Emre Can & Sevlian, Raffi & Cezar, Gustavo Vianna & Kiliccote, Sila & Rajagopal, Ram, 2020. "Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging," Applied Energy, Elsevier, vol. 276(C).
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    Cited by:

    1. Sprei, Frances & Kempton, Willett, 2024. "Mental models guide electric vehicle charging," Energy, Elsevier, vol. 292(C).
    2. Deveci, Muhammet & Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Cali, Umit, 2023. "A rough Dombi Bonferroni based approach for public charging station type selection," Applied Energy, Elsevier, vol. 345(C).
    3. Yin, Wanjun & Jia, Leilei & Ji, Jianbo, 2024. "Energy optimal scheduling strategy considering V2G characteristics of electric vehicle," Energy, Elsevier, vol. 294(C).

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