IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i9p1351-d111157.html
   My bibliography  Save this article

Probabilistic Modeling of Electric Vehicle Charging Pattern Associated with Residential Load for Voltage Unbalance Assessment

Author

Listed:
  • Azhar Ul-Haq

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Marium Azhar

    (Department of Electrical Engineering, Lahore College for Women University, Lahore 54000, Pakistan)

  • Yousef Mahmoud

    (Worcester Polytechnic Institute, Worcester, MA 01609, USA)

  • Aqib Perwaiz

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Essam A. Al-Ammar

    (Department of Electrical Engineering, King Saud University, Riyadh 12372, Saudi Arabia)

Abstract

It has been recognized that an increased penetration of electric vehicles (EVs) may potentially alter load profile in a distribution network. As EVs are regarded as a diversely distributed load so a deterministic method, to predict EV charging load, may not account for all possible factors that could affect the power system. Thus, a stochastic approach is applied that takes into account various realistic factors such as EV battery capacity, state of charge (SOC), driving habit/need, i.e., involving type and purpose of trip, plug-in time, mileage, recharging frequency per day, charging power rate and dynamic EV charging price under controlled and uncontrolled charging schemes. A probabilistic model of EVs charging pattern associated with residential load profile is developed. The probabilistic model gives an activity based residential load profile and EV charging pattern over a period of 24 h. Then, the model output is used to assess the power quality index such as voltage unbalance factor under different electric vehicle penetration levels at different nodes of the system. An uneven EV charging scenario is identified that could cause the voltage unbalance to exceed its permissible limit.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1351-:d:111157
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/9/1351/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/9/1351/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bartusch, Cajsa & Odlare, Monica & Wallin, Fredrik & Wester, Lars, 2012. "Exploring variance in residential electricity consumption: Household features and building properties," Applied Energy, Elsevier, vol. 92(C), pages 637-643.
    2. Hadley, Stanton W. & Tsvetkova, Alexandra A., 2009. "Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation," The Electricity Journal, Elsevier, vol. 22(10), pages 56-68, December.
    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. 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.
    2. Yuttana Kongjeen & Krischonme Bhumkittipich, 2018. "Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis," Energies, MDPI, vol. 11(6), pages 1-16, June.
    3. Dominik Husarek & Vjekoslav Salapic & Simon Paulus & Michael Metzger & Stefan Niessen, 2021. "Modeling the Impact of Electric Vehicle Charging Infrastructure on Regional Energy Systems: Fields of Action for an Improved e-Mobility Integration," Energies, MDPI, vol. 14(23), pages 1-27, November.
    4. Shafqat Jawad & Junyong Liu, 2023. "Electrical Vehicle Charging Load Mobility Analysis Based on a Spatial–Temporal Method in Urban Electrified-Transportation Networks," Energies, MDPI, vol. 16(13), pages 1-14, July.
    5. 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).
    6. 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).
    7. Lili Gong & Wu Cao & Kangli Liu & Jianfeng Zhao & Xiang Li, 2018. "Spatial and Temporal Optimization Strategy for Plug-In Electric Vehicle Charging to Mitigate Impacts on Distribution Network," Energies, MDPI, vol. 11(6), pages 1-16, May.
    8. 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).
    9. Jiang, Qinhua & Zhang, Ning & Yueshuai He, Brian & Lee, Changju & Ma, Jiaqi, 2024. "Large-scale public charging demand prediction with a scenario- and activity-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

    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. Liu, Wen & Hu, Weihao & Lund, Henrik & Chen, Zhe, 2013. "Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China," Applied Energy, Elsevier, vol. 104(C), pages 445-456.
    2. Muratori, Matteo & Roberts, Matthew C. & Sioshansi, Ramteen & Marano, Vincenzo & Rizzoni, Giorgio, 2013. "A highly resolved modeling technique to simulate residential power demand," Applied Energy, Elsevier, vol. 107(C), pages 465-473.
    3. Kettani, Maryème & Sanin, Maria Eugenia, 2024. "Energy consumption and energy poverty in Morocco," Energy Policy, Elsevier, vol. 185(C).
    4. Sylwia Słupik & Joanna Kos-Łabędowicz & Joanna Trzęsiok, 2021. "Energy-Related Behaviour of Consumers from the Silesia Province (Poland)—Towards a Low-Carbon Economy," Energies, MDPI, vol. 14(8), pages 1-23, April.
    5. Hafiz Abdul Muqeet & Rehan Liaqat & Mohsin Jamil & Asharf Ali Khan, 2023. "A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment," Energies, MDPI, vol. 16(1), pages 1-23, January.
    6. Xiaohua Zhang & Jun Xie & Zhengwei Zhu & Jianfeng Zheng & Hao Qiang & Hailong Rong, 2016. "Smart Grid Cost-Emission Unit Commitment via Co-Evolutionary Agents," Energies, MDPI, vol. 9(10), pages 1-13, October.
    7. Talaei, A. & Begg, K. & Jamasb, T., 2012. "The Large Scale Roll-Out of Electric Vehicles: The Effect on the Electricity Sector and CO2 Emissions," Cambridge Working Papers in Economics 1246, Faculty of Economics, University of Cambridge.
    8. Irfan, Muhammad & Iqbal, Jamshed & Iqbal, Adeel & Iqbal, Zahid & Riaz, Raja Ali & Mehmood, Adeel, 2017. "Opportunities and challenges in control of smart grids – Pakistani perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 652-674.
    9. Xiao, Jingjie, 2013. "Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming," MPRA Paper 58696, University Library of Munich, Germany.
    10. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    11. Li, Xian-Xiang, 2018. "Linking residential electricity consumption and outdoor climate in a tropical city," Energy, Elsevier, vol. 157(C), pages 734-743.
    12. Yang, Shengjie & Yao, Jiangang & Kang, Tong & Zhu, Xiangqian, 2014. "Dynamic operation model of the battery swapping station for EV (electric vehicle) in electricity market," Energy, Elsevier, vol. 65(C), pages 544-549.
    13. Raatikainen, Mika & Skön, Jukka-Pekka & Leiviskä, Kauko & Kolehmainen, Mikko, 2016. "Intelligent analysis of energy consumption in school buildings," Applied Energy, Elsevier, vol. 165(C), pages 416-429.
    14. Axsen, Jonn & Kurani, Kenneth S. & McCarthy, Ryan & Yang, Christopher, 2011. "Plug-in hybrid vehicle GHG impacts in California: Integrating consumer-informed recharge profiles with an electricity-dispatch model," Energy Policy, Elsevier, vol. 39(3), pages 1617-1629, March.
    15. Sijousa Basumatary & Mridula Devi & Konita Basumatary, 2021. "Determinants of Household Electricity Demand in Rural India: A Case Study of the Impacts of Government Subsidies and Surcharges," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 243-249.
    16. S Satheesh Kumar & B Ashok Kumar & S Senthilrani, 2024. "Review of electric vehicle (EV) charging using renewable solar photovoltaic (PV) nano grid," Energy & Environment, , vol. 35(2), pages 1089-1117, March.
    17. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    18. Schücking, Maximilian & Jochem, Patrick & Fichtner, Wolf & Wollersheim, Olaf & Stella, Kevin, 2017. "Charging strategies for economic operations of electric vehicles in commercial applications," MPRA Paper 91599, University Library of Munich, Germany.
    19. Wilkerson, Jordan & Larsen, Peter & Barbose, Galen, 2014. "Survey of Western U.S. electric utility resource plans," Energy Policy, Elsevier, vol. 66(C), pages 90-103.
    20. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.

    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:10:y:2017:i:9:p:1351-:d:111157. 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.