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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
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    References listed on IDEAS

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    1. 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.
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    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).

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