IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i12p4454-d842278.html
   My bibliography  Save this article

Novel PEV Charging Approaches for Extending Transformer Life

Author

Listed:
  • Theron Smith

    (Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697-3550, USA)

  • Joseph Garcia

    (Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA 92697-3550, USA)

  • Gregory Washington

    (George Mason University, Fairfax, VA 22030, USA)

Abstract

The study investigates how variable rate charging can affect PEV charging and identifies how this capability can be integrated into residential neighborhoods. The results show that creating PEV chargers that can deliver variable rates will enhance uncontrolled and controlled PEV charging. The integration is summarized into 4 phases. In phase 1, uncontrolled PEV chargers should be enabled to provide any rate to vehicles within 0 to 11.5 kW, which can reduce overloading by up to 28.34%. Phase 2 introduces smart chargers that use forecasted data to determine the optimal time intervals for PEVs to charge using a fixed rate of 4.8 kW, capable of reducing overloading by 42.69%. In Phase 3, a controlled smart charging strategy that can deliver any rate to a vehicle using SRVF’s approach is proposed, which will reduce overloading by up to 42.87%. Lastly, phase 4 recommends a smart charging control that can deliver any rate to a vehicle using RIVF’s approach, reducing overloading by up to 43.37%.

Suggested Citation

  • Theron Smith & Joseph Garcia & Gregory Washington, 2022. "Novel PEV Charging Approaches for Extending Transformer Life," Energies, MDPI, vol. 15(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4454-:d:842278
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/12/4454/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/12/4454/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Florian van Triel & Timothy E. Lipman, 2020. "Modeling the Future California Electricity Grid and Renewable Energy Integration with Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-20, October.
    3. Kang Miao Tan & Vigna K. Ramachandaramurthy & Jia Ying Yong & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg, 2017. "Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling," Energies, MDPI, vol. 10(11), pages 1-21, November.
    4. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    5. Jinil Han & Jongyoon Park & Kyungsik Lee, 2017. "Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power," Energies, MDPI, vol. 10(7), pages 1-15, July.
    6. Weige Zhang & Di Zhang & Biqiang Mu & Le Yi Wang & Yan Bao & Jiuchun Jiang & Hugo Morais, 2017. "Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids," Energies, MDPI, vol. 10(2), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lijuan Sun & Menggang Chen & Yawei Shi & Lifeng Zheng & Songyang Li & Jun Li & Huijuan Xu, 2022. "Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System," Energies, MDPI, vol. 15(24), pages 1-13, December.
    2. 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.
    3. Ana Pavlićević & Saša Mujović, 2022. "Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses," Energies, MDPI, vol. 15(19), pages 1-24, September.
    4. Nam Hoai Nguyen & Quynh T. Tran & Thao V. Nguyen & Nam Tran & Leon Roose & Saeed Sepasi & Maria Luisa Di Silvestre, 2023. "A Method for Assessing the Feasibility of Integrating Planned Unidirectional EV Chargers into the Distribution Grid: A Case Study in Danang, Vietnam," Energies, MDPI, vol. 16(9), pages 1-16, April.
    5. Amanda M. P. Barros & Jorge H. Angelim & Carolina M. Affonso, 2023. "Impact on Distribution Transformer Life Using Electric Vehicles with Long-Range Battery Capacity," Energies, MDPI, vol. 16(12), pages 1-13, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan Bao & Fangyu Chang & Jinkai Shi & Pengcheng Yin & Weige Zhang & David Wenzhong Gao, 2022. "An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory," Energies, MDPI, vol. 15(14), pages 1-20, July.
    2. Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    3. Jorge García Álvarez & Miguel Ángel González & Camino Rodríguez Vela & Ramiro Varela, 2018. "Electric Vehicle Charging Scheduling by an Enhanced Artificial Bee Colony Algorithm," Energies, MDPI, vol. 11(10), pages 1-19, 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. Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.
    6. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    7. Chitchai Srithapon & Prasanta Ghosh & Apirat Siritaratiwat & Rongrit Chatthaworn, 2020. "Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost," Energies, MDPI, vol. 13(2), pages 1-20, January.
    8. Omowunmi Mary Longe & Khmaies Ouahada & Suvendi Rimer & Hendrik C. Ferreira & A. J. Han Vinck, 2017. "Distributed Optimisation Algorithm for Demand Side Management in a Grid-Connected Smart Microgrid," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    9. Xuejun Zheng & Shaorong Wang & Zia Ullah & Mengmeng Xiao & Chang Ye & Zhangping Lei, 2021. "A Novel Optimization Method for a Multi-Year Planning Scheme of an Active Distribution Network in a Large Planning Zone," Energies, MDPI, vol. 14(12), pages 1-16, June.
    10. Soomin Woo & Zhe Fu & Elpiniki Apostolaki-Iosifidou & Timothy E. Lipman, 2021. "Economic and Environmental Benefits for Electricity Grids from Spatiotemporal Optimization of Electric Vehicle Charging," Energies, MDPI, vol. 14(24), pages 1-22, December.
    11. Ioannis Karakitsios & Dimitrios Lagos & Aris Dimeas & Nikos Hatziargyriou, 2023. "How Can EVs Support High RES Penetration in Islands," Energies, MDPI, vol. 16(1), pages 1-17, January.
    12. Hao, Daning & Qi, Lingfei & Tairab, Alaeldin M. & Ahmed, Ammar & Azam, Ali & Luo, Dabing & Pan, Yajia & Zhang, Zutao & Yan, Jinyue, 2022. "Solar energy harvesting technologies for PV self-powered applications: A comprehensive review," Renewable Energy, Elsevier, vol. 188(C), pages 678-697.
    13. Yian Yan & Huang Wang & Jiuchun Jiang & Weige Zhang & Yan Bao & Mei Huang, 2019. "Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station," Energies, MDPI, vol. 12(3), pages 1-17, February.
    14. Tommy Lundgren & Mattias Vesterberg, 2024. "Efficiency in electricity distribution in Sweden and the effects of small-scale generation, electric vehicles and dynamic tariffs," Journal of Productivity Analysis, Springer, vol. 62(2), pages 121-137, October.
    15. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    16. Rahman, Syed & Khan, Irfan Ahmed & Khan, Ashraf Ali & Mallik, Ayan & Nadeem, Muhammad Faisal, 2022. "Comprehensive review & impact analysis of integrating projected electric vehicle charging load to the existing low voltage distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    17. 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).
    18. Jun Bi & Yongxing Wang & Shuai Sun & Wei Guan, 2018. "Predicting Charging Time of Battery Electric Vehicles Based on Regression and Time-Series Methods: A Case Study of Beijing," Energies, MDPI, vol. 11(5), pages 1-18, April.
    19. Powell, Siobhan & Martin, Sonia & Rajagopal, Ram & Azevedo, Inês M.L. & de Chalendar, Jacques, 2024. "Future-proof rates for controlled electric vehicle charging: Comparing multi-year impacts of different emission factor signals," Energy Policy, Elsevier, vol. 190(C).
    20. Zhongbao Wei & Feng Leng & Zhongjie He & Wenyu Zhang & Kaiyuan Li, 2018. "Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method," Energies, MDPI, vol. 11(7), pages 1-16, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:12:p:4454-:d:842278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.