IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224036119.html
   My bibliography  Save this article

Capacity optimization of PV and battery storage for EVCS with multi-venues charging behavior difference towards economic targets

Author

Listed:
  • Huang, Wenxin
  • Wang, Jianguo
  • Wang, Jianping
  • Zhou, Mi
  • Cao, Jinxin
  • Cai, Li

Abstract

EV users served by multi-venues Electric Vehicle Charging Stations (EVCS) have different charging behaviors, encompassing aspects such as charging duration, energy consumption, and behavioral dispersion, which affect the integrated role of photovoltaic (PV) and battery storage (BS). This study analyzes the charging behavior of drivers at different venues EVCS in Wuhan, China. Additionally, it generates EV fleet load profiles through Monte Carlo simulation, accounting for fluctuations in charging loads due to charging congestion and seasonal differences. A robust optimization (RO) model is proposed for the integration of PV-BS capacity at multi-venues EVCS, with the objective of annual planning and operation economics, further considering PV-EV uncertainty, BS arbitrage and cycle-life decay, and EV fleet charging power control. The planning and operation processes are explicitly given in a hierarchical form and solved by the G&CG algorithm. The results show that the optimal integration capacity of PV and BS is subject to variability based on the charging behaviors observed at different EVCS venues. Furthermore, the sensitivity analysis discusses the implications of broader variations in charging behaviors and their dispersion on capacity integration, as well as the influence of diverse solar irradiation conditions. The insights derived from this study can inform PV-BS capacity integration strategies for EVCS across various venues.

Suggested Citation

  • Huang, Wenxin & Wang, Jianguo & Wang, Jianping & Zhou, Mi & Cao, Jinxin & Cai, Li, 2024. "Capacity optimization of PV and battery storage for EVCS with multi-venues charging behavior difference towards economic targets," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224036119
    DOI: 10.1016/j.energy.2024.133833
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224036119
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133833?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. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    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. Yang Gao & Xiaohong Zhang & Qingyuan Yan & Yanxue Li, 2025. "Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road–Electric Grid Interaction and User Psychology," Sustainability, MDPI, vol. 17(6), pages 1-27, March.

    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. Fu, Zhi & Liu, Xiaochen & Zhang, Ji & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2025. "Orderly solar charging of electric vehicles and its impact on charging behavior: A year-round field experiment," Applied Energy, Elsevier, vol. 381(C).
    2. Muhammad Naveed Iqbal & Lauri Kütt & Matti Lehtonen & Robert John Millar & Verner Püvi & Anton Rassõlkin & Galina L. Demidova, 2021. "Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles," Sustainability, MDPI, vol. 13(3), pages 1-14, February.
    3. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
    4. Rüdisüli, Martin & Bach, Christian & Bauer, Christian & Beloin-Saint-Pierre, Didier & Elber, Urs & Georges, Gil & Limpach, Robert & Pareschi, Giacomo & Kannan, Ramachandran & Teske, Sinan L., 2022. "Prospective life-cycle assessment of greenhouse gas emissions of electricity-based mobility options," Applied Energy, Elsevier, vol. 306(PB).
    5. Haihong Bian & Quance Ren & Zhengyang Guo & Chengang Zhou & Zhiyuan Zhang & Ximeng Wang, 2024. "Predictive Model for EV Charging Load Incorporating Multimodal Travel Behavior and Microscopic Traffic Simulation," Energies, MDPI, vol. 17(11), pages 1-23, May.
    6. Tikka, Ville & Haapaniemi, Jouni & Räisänen, Otto & Honkapuro, Samuli, 2022. "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning," Applied Energy, Elsevier, vol. 328(C).
    7. Zhou, Yuekuan & Liu, Xiaohua & Zhao, Qianchuan, 2024. "A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid," Renewable Energy, Elsevier, vol. 221(C).
    8. Liu, Yuechen Sophia & Tayarani, Mohammad & Gao, H. Oliver, 2022. "An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand," Energy, Elsevier, vol. 258(C).
    9. Lukáš Dvořáček & Martin Horák & Michaela Valentová & Jaroslav Knápek, 2020. "Optimization of Electric Vehicle Charging Points Based on Efficient Use of Chargers and Providing Private Charging Spaces," Energies, MDPI, vol. 13(24), pages 1-28, December.
    10. Huang, Wenxin & Wang, Jianguo & Wang, Jianping & Zeng, Haiyan & Zhou, Mi & Cao, Jinxin, 2024. "EV charging load profile identification and seasonal difference analysis via charging sessions data of charging stations," Energy, Elsevier, vol. 288(C).
    11. Liao, Peng & Tang, Tie-Qiao & Liu, Ronghui & Huang, Hai-Jun, 2021. "An eco-driving strategy for electric vehicle based on the powertrain," Applied Energy, Elsevier, vol. 302(C).
    12. Chemudupaty, Raviteja & Hornek, Timothée & Pavić, Ivan & Potenciano Menci, Sergio, 2025. "Optimizing trading of electric vehicle charging flexibility in the continuous intraday market under user and market uncertainties," Applied Energy, Elsevier, vol. 381(C).
    13. Krzysztof Zagrajek, 2021. "A Survey Data Approach for Determining the Probability Values of Vehicle-to-Grid Service Provision," Energies, MDPI, vol. 14(21), pages 1-38, November.
    14. Simolin, Toni & Rauma, Kalle & Viri, Riku & Mäkinen, Johanna & Rautiainen, Antti & Järventausta, Pertti, 2021. "Charging powers of the electric vehicle fleet: Evolution and implications at commercial charging sites," Applied Energy, Elsevier, vol. 303(C).
    15. Liu, Xiaochen & Fu, Zhi & Qiu, Siyuan & Zhang, Tao & Li, Shaojie & Yang, Zhi & Liu, Xiaohua & Jiang, Yi, 2023. "Charging private electric vehicles solely by photovoltaics: A battery-free direct-current microgrid with distributed charging strategy," Applied Energy, Elsevier, vol. 341(C).
    16. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    17. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    18. Krzysztof Zagrajek & Józef Paska & Łukasz Sosnowski & Konrad Gobosz & Konrad Wróblewski, 2021. "Framework for the Introduction of Vehicle-to-Grid Technology into the Polish Electricity Market," Energies, MDPI, vol. 14(12), pages 1-30, June.
    19. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    20. Loris Di Natale & Luca Funk & Martin Rüdisüli & Bratislav Svetozarevic & Giacomo Pareschi & Philipp Heer & Giovanni Sansavini, 2021. "The Potential of Vehicle-to-Grid to Support the Energy Transition: A Case Study on Switzerland," Energies, MDPI, vol. 14(16), pages 1-24, August.

    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:energy:v:313:y:2024:i:c:s0360544224036119. 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.journals.elsevier.com/energy .

    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.