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Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems

Author

Listed:
  • Tobias Rodemann

    (Honda Research Institute Europe GmbH, 63073 Offenbach am Main, Germany)

  • Tom Eckhardt

    (EA Systems Dresden GmbH, 01187 Dresden, Germany)

  • René Unger

    (EA Systems Dresden GmbH, 01187 Dresden, Germany)

  • Torsten Schwan

    (EA Systems Dresden GmbH, 01187 Dresden, Germany)

Abstract

The development of efficient electric vehicle (EV) charging infrastructure requires a modeling of customer behavior at an appropriate level of detail. Since only limited information about real customers is available, most simulation approaches employ a stochastic approach by combining known or estimated customer features with random variations. A typical example is to model EV charging customers by an arrival and a targeted departure time, plus the requested amount of energy or increased state of charge (SoC), where values are drawn from normal (Gaussian) distributions with mean and variance values derived from user studies of obviously limited sample size. In this work, we compare this basic approach with a more detailed customer model employing a multi-agent simulation (MAS) framework in order to investigate how a customer behavior that responds to external factors (like weather) or historical data (like satisfaction in past charging sessions) impacts the essential key performance indicators of the charging system. Our findings show that small changes in the way customers are modeled can lead to quantitative and qualitative differences in the simulated performance of EV charging systems.

Suggested Citation

  • Tobias Rodemann & Tom Eckhardt & René Unger & Torsten Schwan, 2019. "Using Agent-Based Customer Modeling for the Evaluation of EV Charging Systems," Energies, MDPI, vol. 12(15), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2858-:d:251470
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    References listed on IDEAS

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    1. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    2. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
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    Cited by:

    1. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Jian Chen & Fangyi Li & Ranran Yang & Dawei Ma, 2020. "Impacts of Increasing Private Charging Piles on Electric Vehicles’ Charging Profiles: A Case Study in Hefei City, China," Energies, MDPI, vol. 13(17), pages 1-17, August.
    3. Steffen Limmer, 2019. "Evaluation of Optimization-Based EV Charging Scheduling with Load Limit in a Realistic Scenario," Energies, MDPI, vol. 12(24), pages 1-16, December.
    4. 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).

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