IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v305y2022ics0306261921010679.html
   My bibliography  Save this article

Electric vehicles and power quality in low voltage networks: Real data analysis and modeling

Author

Listed:
  • Torres, S.
  • Durán, I.
  • Marulanda, A.
  • Pavas, A.
  • Quirós-Tortós, J.

Abstract

Electric vehicles (EVs) will help to decarbonize energy systems. However, their connection to on-board level 2 chargers (7.2 kW) at household facilities brings challenges to Distribution Network Operators (DNOs) as they can affect the power quality of low voltage (LV) networks. In order to truly assess these effects, the electrical behavior of the on-board charger in terms of its non-linear content, power demand, and charge rate must be understood first. Nonetheless, most modeling methodologies with this aim result in circuital approaches, and thus, in heavy computational burdens, or assume simplified representations that do not correspond to the reality of the charge. To overcome this, we present a new methodology to model the power quality characteristics of EVs based on measured data from the harmonic spectra of the charger. The model provides a precise and efficient electrical characterization, where probabilistic models of the harmonic spectra are used to compute the power demand during every stage of the charge. Due to its probabilistic nature, these harmonic spectra are represented using Gaussian Mixture Models. We validate the model contrasting simulated data versus real measured one. Then, we illustrate a case study of the model in a LV network power quality assessment with different EV penetration levels, considering time-series harmonic power flows with 10-min resolution under a Monte Carlo approach. Obtained results revealed an increase in the network chargeability and voltage unbalance, along with an increased content of the third harmonic, which appears to be the most intense.

Suggested Citation

  • Torres, S. & Durán, I. & Marulanda, A. & Pavas, A. & Quirós-Tortós, J., 2022. "Electric vehicles and power quality in low voltage networks: Real data analysis and modeling," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921010679
    DOI: 10.1016/j.apenergy.2021.117718
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921010679
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117718?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Azhar Ul-Haq & Marium Azhar & Yousef Mahmoud & Aqib Perwaiz & Essam A. Al-Ammar, 2017. "Probabilistic Modeling of Electric Vehicle Charging Pattern Associated with Residential Load for Voltage Unbalance Assessment," Energies, MDPI, vol. 10(9), pages 1-18, September.
    2. Joanna Baraniak & Jacek Starzyński, 2020. "Modeling the Impact of Electric Vehicle Charging Systems on Electric Power Quality," Energies, MDPI, vol. 13(15), pages 1-13, August.
    3. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    4. Lyu, Lin & Yang, Xinran & Xiang, Yue & Liu, Junyong & Jawad, Shafqat & Deng, Runqi, 2020. "Exploring high-penetration electric vehicles impact on urban power grid based on voltage stability analysis," Energy, Elsevier, vol. 198(C).
    5. Morsy Nour & José Pablo Chaves-Ávila & Gaber Magdy & Álvaro Sánchez-Miralles, 2020. "Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems," Energies, MDPI, vol. 13(18), pages 1-34, September.
    6. 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).
    7. Martin Spitzer & Jonas Schlund & Elpiniki Apostolaki-Iosifidou & Marco Pruckner, 2019. "Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-19, October.
    8. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
    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. Cruz, José Ramón Serrano & López, J. Javier & Climent, Héctor & Gómez-Vilanova, Alejandro, 2023. "Method for turbocharging and supercharging 2-stroke engines, applied to an opposed-piston new concept for hybrid powertrains," Applied Energy, Elsevier, vol. 351(C).
    2. Xie, Xiangmin & Chen, Daolian, 2022. "Data-driven dynamic harmonic model for modern household appliances," Applied Energy, Elsevier, vol. 312(C).
    3. Antić, Tomislav & Capuder, Tomislav, 2024. "A geographic information system-based modelling, analysing and visualising of low voltage networks: The potential of demand time-shifting in the power quality improvement," Applied Energy, Elsevier, vol. 353(PA).
    4. Bernd Thormann & Thomas Kienberger, 2022. "Estimation of Grid Reinforcement Costs Triggered by Future Grid Customers: Influence of the Quantification Method (Scaling vs. Large-Scale Simulation) and Coincidence Factors (Single vs. Multiple Appl," Energies, MDPI, vol. 15(4), pages 1-26, 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. Fretzen, Ulrich & Ansarin, Mohammad & Brandt, Tobias, 2021. "Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging," Applied Energy, Elsevier, vol. 282(PA).
    2. Strobel, Leo & Schlund, Jonas & Pruckner, Marco, 2022. "Joint analysis of regional and national power system impacts of electric vehicles—A case study for Germany on the county level in 2030," Applied Energy, Elsevier, vol. 315(C).
    3. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    4. Yashraj Tripathy & Andrew McGordon & Anup Barai, 2020. "Improving Accessible Capacity Tracking at Low Ambient Temperatures for Range Estimation of Battery Electric Vehicles," Energies, MDPI, vol. 13(8), pages 1-18, April.
    5. K. S. Reddy & S. Aravindhan & Tapas K. Mallick, 2017. "Techno-Economic Investigation of Solar Powered Electric Auto-Rickshaw for a Sustainable Transport System," Energies, MDPI, vol. 10(6), pages 1-15, May.
    6. Stefano De Pinto & Pablo Camocardi & Christoforos Chatzikomis & Aldo Sorniotti & Francesco Bottiglione & Giacomo Mantriota & Pietro Perlo, 2020. "On the Comparison of 2- and 4-Wheel-Drive Electric Vehicle Layouts with Central Motors and Single- and 2-Speed Transmission Systems," Energies, MDPI, vol. 13(13), pages 1-24, June.
    7. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    8. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    9. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    10. Kapetanović, Marko & Núñez, Alfredo & van Oort, Niels & Goverde, Rob M.P., 2021. "Reducing fuel consumption and related emissions through optimal sizing of energy storage systems for diesel-electric trains," Applied Energy, Elsevier, vol. 294(C).
    11. Julia Vopava & Christian Koczwara & Anna Traupmann & Thomas Kienberger, 2019. "Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach," Energies, MDPI, vol. 13(1), pages 1-23, December.
    12. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    13. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    14. Elkin D. Reyes & Arturo S. Bretas & Sergio Rivera, 2020. "Marginal Uncertainty Cost Functions for Solar Photovoltaic, Wind Energy, Hydro Generators, and Plug-In Electric Vehicles," Energies, MDPI, vol. 13(23), pages 1-20, December.
    15. Paulo M. De Oliveira-De Jesus & Mario A. Rios & Gustavo A. Ramos, 2018. "Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration," Energies, MDPI, vol. 11(8), pages 1-19, July.
    16. Yi, Tao & Cheng, Xiaobin & Chen, Yaxuan & Liu, Jinpeng, 2020. "Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle," Energy, Elsevier, vol. 208(C).
    17. Han, Zhongliang & Xu, Nan & Chen, Hong & Huang, Yanjun & Zhao, Bin, 2018. "Energy-efficient control of electric vehicles based on linear quadratic regulator and phase plane analysis," Applied Energy, Elsevier, vol. 213(C), pages 639-657.
    18. Asad Waqar Malik & Zahid Anwar, 2022. "Do Charging Stations Benefit from Cryptojacking? A Novel Framework for Its Financial Impact Analysis on Electric Vehicles," Energies, MDPI, vol. 15(16), pages 1-15, August.
    19. Torkey, Alaa & Abdelgawad, Hossam, 2022. "Framework for planning of EV charging infrastructure: Where should cities start?," Transport Policy, Elsevier, vol. 128(C), pages 193-208.
    20. Liu, Hongxiang & Han, Ling & Cao, Yue, 2020. "Improving transmission efficiency and reducing energy consumption with automotive continuously variable transmission: A model prediction comprehensive optimization approach," Applied Energy, Elsevier, vol. 274(C).

    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:eee:appene:v:305:y:2022:i:c:s0306261921010679. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.