IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i6p1688-d519656.html
   My bibliography  Save this article

Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads

Author

Listed:
  • C. Birk Jones

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Matthew Lave

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • William Vining

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

  • Brooke Marshall Garcia

    (Sandia National Laboratories, Albuquerque, NM 87123, USA)

Abstract

An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.

Suggested Citation

  • C. Birk Jones & Matthew Lave & William Vining & Brooke Marshall Garcia, 2021. "Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads," Energies, MDPI, vol. 14(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1688-:d:519656
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/6/1688/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/6/1688/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hanemann, Philipp & Behnert, Marika & Bruckner, Thomas, 2017. "Effects of electric vehicle charging strategies on the German power system," Applied Energy, Elsevier, vol. 203(C), pages 608-622.
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    4. Harris, Chioke B. & Webber, Michael E., 2014. "An empirically-validated methodology to simulate electricity demand for electric vehicle charging," Applied Energy, Elsevier, vol. 126(C), pages 172-181.
    5. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    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. Jay Johnson & Timothy Berg & Benjamin Anderson & Brian Wright, 2022. "Review of Electric Vehicle Charger Cybersecurity Vulnerabilities, Potential Impacts, and Defenses," Energies, MDPI, vol. 15(11), pages 1-26, May.
    2. Abood Mourad & Martin Hennebel & Ahmed Amrani & Amira Ben Hamida, 2021. "Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network," Energies, MDPI, vol. 14(9), pages 1-20, April.
    3. Virginia Negri & Alessandro Mingotti & Roberto Tinarelli & Lorenzo Peretto, 2023. "Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks," Energies, MDPI, vol. 16(1), pages 1-20, January.
    4. 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.
    5. Zhouquan Wu & Bo Chen, 2021. "Distributed Electric Vehicle Charging Scheduling with Transactive Energy Management," Energies, MDPI, vol. 15(1), pages 1-16, December.
    6. Bruno Eduardo Carmelito & José Maria de Carvalho Filho, 2023. "Hosting Capacity of Electric Vehicles on LV/MV Distribution Grids—A New Methodology Assessment," Energies, MDPI, vol. 16(3), pages 1-19, February.

    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. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
    2. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    3. Liu, Ke & Liu, Yanli, 2023. "Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load," Applied Energy, Elsevier, vol. 339(C).
    4. Powell, Siobhan & Cezar, Gustavo Vianna & Rajagopal, Ram, 2022. "Scalable probabilistic estimates of electric vehicle charging given observed driver behavior," Applied Energy, Elsevier, vol. 309(C).
    5. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    6. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    7. Philipp Andreas Gunkel & Claire Bergaentzl'e & Ida Gr{ae}sted Jensen & Fabian Scheller, 2020. "From passive to active: Flexibility from electric vehicles in the context of transmission system development," Papers 2011.05830, arXiv.org.
    8. Doluweera, Ganesh & Hahn, Fabian & Bergerson, Joule & Pruckner, Marco, 2020. "A scenario-based study on the impacts of electric vehicles on energy consumption and sustainability in Alberta," Applied Energy, Elsevier, vol. 268(C).
    9. Qiu, Yueming Lucy & Wang, Yi David & Iseki, Hiroyuki & Shen, Xingchi & Xing, Bo & Zhang, Huiming, 2022. "Empirical grid impact of in-home electric vehicle charging differs from predictions," Resource and Energy Economics, Elsevier, vol. 67(C).
    10. Muhammad Ahsan Khan & Akhtar Hussain & Woon-Gyu Lee & Hak-Man Kim, 2023. "An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid," Energies, MDPI, vol. 16(17), pages 1-23, September.
    11. 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.
    12. Gunkel, Philipp Andreas & Bergaentzlé, Claire & Græsted Jensen, Ida & Scheller, Fabian, 2020. "From passive to active: Flexibility from electric vehicles in the context of transmission system development," Applied Energy, Elsevier, vol. 277(C).
    13. Mangipinto, Andrea & Lombardi, Francesco & Sanvito, Francesco Davide & Pavičević, Matija & Quoilin, Sylvain & Colombo, Emanuela, 2022. "Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries," Applied Energy, Elsevier, vol. 312(C).
    14. Einolander, Johannes & Lahdelma, Risto, 2022. "Explicit demand response potential in electric vehicle charging networks: Event-based simulation based on the multivariate copula procedure," Energy, Elsevier, vol. 256(C).
    15. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    16. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    17. Chen, Jiahui & Wang, Fang & He, Xiaoyi & Liang, Xinyu & Huang, Junling & Zhang, Shaojun & Wu, Ye, 2022. "Emission mitigation potential from coordinated charging schemes for future private electric vehicles," Applied Energy, Elsevier, vol. 308(C).
    18. Reza Fachrizal & Joakim Munkhammar, 2020. "Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles," Energies, MDPI, vol. 13(5), pages 1-19, March.
    19. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    20. Lizana, Jesus & Friedrich, Daniel & Renaldi, Renaldi & Chacartegui, Ricardo, 2018. "Energy flexible building through smart demand-side management and latent heat storage," Applied Energy, Elsevier, vol. 230(C), pages 471-485.

    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:14:y:2021:i:6:p:1688-:d:519656. 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.