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Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data

Author

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  • Semen Uimonen

    (School of Electrical Engineering, Aalto University, P.O. Box 15500, 00076 Espoo, Finland)

  • Matti Lehtonen

    (School of Electrical Engineering, Aalto University, P.O. Box 15500, 00076 Espoo, Finland)

Abstract

Transportation vehicles are a large contributor of the carbon dioxide emissions to the atmosphere. Electric Vehicles (EVs) are a promising solution to reduce the CO 2 emissions which, however, requires the right electric power production mix for the largest impact. The increase in the electric power consumption caused by the EV charging demand could be matched by the growing share of Renewable Energy Sources (RES) in the power production. EVs are becoming a popular sustainable mean of transportation and the expansion of EV units due to the stochastic nature of charging behavior and increasing share of RES creates additional challenges to the stability in the power systems. Modeling of EV charging fleets allows understanding EV charging capacity and demand response (DR) potential of EV in the power systems. This article focuses on modeling of daily EV charging profiles for buildings with various number of chargers and daily events. The article presents a modeling approach based on the charger occupancy data from the local charging sites. The approach allows one to simulate load profiles and to find how many chargers are necessary to suffice the approximate demand of EV charging from the traffic characteristics, such as arrival time, duration of charging, and maximum charging power. Additionally, to better understand the potential impact of demand response, the modeling approach allows one to compare charging profiles, while adjusting the maximum power consumption of chargers.

Suggested Citation

  • Semen Uimonen & Matti Lehtonen, 2020. "Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data," Energies, MDPI, vol. 13(21), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5700-:d:438067
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    Cited by:

    1. Nattavit Piamvilai & Somporn Sirisumrannukul, 2022. "Optimal Scheduling of Movable Electric Vehicle Loads Using Generation of Charging Event Matrices, Queuing Management, and Genetic Algorithm," Energies, MDPI, vol. 15(10), pages 1-26, May.
    2. 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.
    3. Pedro Faria & Zita Vale, 2022. "Realistic Load Modeling for Efficient Consumption Management Using Real-Time Simulation and Power Hardware-in-the-Loop," Energies, MDPI, vol. 16(1), pages 1-15, December.
    4. Christopher Hecht & Jan Figgener & Xiaohui Li & Lei Zhang & Dirk Uwe Sauer, 2023. "Standard Load Profiles for Electric Vehicle Charging Stations in Germany Based on Representative, Empirical Data," Energies, MDPI, vol. 16(6), pages 1-21, March.
    5. Pablo Tamay & Esteban Inga, 2022. "Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    6. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli, 2021. "Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions," Energies, MDPI, vol. 14(4), pages 1-15, February.
    7. Khaleghikarahrodi, Mehrsa & Macht, Gretchen A., 2023. "Patterns, no patterns, that is the question: Quantifying users’ electric vehicle charging," Transport Policy, Elsevier, vol. 141(C), pages 291-304.
    8. Pramote Jaruwatanachai & Yod Sukamongkol & Taweesak Samanchuen, 2023. "Predicting and Managing EV Charging Demand on Electrical Grids: A Simulation-Based Approach," Energies, MDPI, vol. 16(8), pages 1-22, April.

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