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Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging

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  • Powell, Siobhan
  • Kara, Emre Can
  • Sevlian, Raffi
  • Cezar, Gustavo Vianna
  • Kiliccote, Sila
  • Rajagopal, Ram

Abstract

To accelerate adoption of non-residential charging for electric vehicles, sites must maximize utilization of existing electrical infrastructure. In this study we model electric vehicle charging at a workplace using real charging data and evaluate the lifetime of the site’s transformer as the number of charging stations is incrementally increased. We implement and compare a range of control schemes for workplace charging including minimizing the peak load, capping the total load, minimizing bills under different rate structures with time-of-use energy costs and demand charges, and directly minimizing the transformer’s aging. These are compared by the number of vehicles they allow the transformer to support, the transformer’s health, and the operator’s electricity bill. We draw a connection between minimizing the peak load and improving the transformer’s health. We observe that minimizing the electricity bill is the best scheme by both criteria when the bill includes a demand charge; in our experiment it allowed the infrastructure to support over 67% more cars than under uncontrolled charging. To protect the transformer we recommend that demand charges or capacity management be applied to parking lots of charging electric vehicles with high infrastructure utilization, and operators schedule charging to minimize their electricity bills.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:276:y:2020:i:c:s0306261920308643
    DOI: 10.1016/j.apenergy.2020.115352
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    References listed on IDEAS

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    1. Ramos Muñoz, Edgar & Razeghi, Ghazal & Zhang, Li & Jabbari, Faryar, 2016. "Electric vehicle charging algorithms for coordination of the grid and distribution transformer levels," Energy, Elsevier, vol. 113(C), pages 930-942.
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    Citations

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    Cited by:

    1. 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).
    2. Ildar Daminov & Rémy Rigo-Mariani & Raphael Caire & Anton Prokhorov & Marie-Cécile Alvarez-Hérault, 2021. "Demand Response Coupled with Dynamic Thermal Rating for Increased Transformer Reserve and Lifetime," Energies, MDPI, vol. 14(5), pages 1-27, March.
    3. Siobhan Powell & Gustavo Vianna Cezar & Liang Min & Inês M. L. Azevedo & Ram Rajagopal, 2022. "Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption," Nature Energy, Nature, vol. 7(10), pages 932-945, October.
    4. Edgar Ramos Muñoz & Faryar Jabbari, 2022. "An Octopus Charger-Based Smart Protocol for Battery Electric Vehicle Charging at a Workplace Parking Structure," Energies, MDPI, vol. 15(17), pages 1-25, September.
    5. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    6. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    7. Daneshzand, Farzaneh & Coker, Phil J & Potter, Ben & Smith, Stefan T, 2023. "EV smart charging: How tariff selection influences grid stress and carbon reduction," Applied Energy, Elsevier, vol. 348(C).
    8. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Li, Shaojie & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2023. "Building-centric investigation into electric vehicle behavior: A survey-based simulation method for charging system design," Energy, Elsevier, vol. 271(C).
    9. Abhinav Tiwari & Hany Farag, 2022. "Analysis and Modeling of Value Creation Opportunities and Governing Factors for Electric Vehicle Proliferation," Energies, MDPI, vol. 16(1), pages 1-26, December.
    10. 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).
    11. Shiping Xu & Lili Wang, 2023. "Do Green Information and Communication Technologies (ICT) and Smart Urbanization Reduce Environmental Pollution in China?," Sustainability, MDPI, vol. 15(19), pages 1-18, October.
    12. Moradi Amani, A. & Sajjadi, S.S. & Al Khafaf, N. & Song, H. & Jalili, M. & Yu, X. & Meegahapola, L. & McTaggart, P., 2023. "Technology balancing for reliable EV uptake in distribution grids: An Australian case study," Renewable Energy, Elsevier, vol. 206(C), pages 939-948.
    13. Powell, Siobhan & Vianna Cezar, Gustavo & Apostolaki-Iosifidou, Elpiniki & Rajagopal, Ram, 2022. "Large-scale scenarios of electric vehicle charging with a data-driven model of control," Energy, Elsevier, vol. 248(C).
    14. Kang, Zixuan & Ye, Zhongnan & Lam, Chor-Man & Hsu, Shu-Chien, 2023. "Sustainable electric vehicle charging coordination: Balancing CO2 emission reduction and peak power demand shaving," Applied Energy, Elsevier, vol. 349(C).

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