IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v384y2025ics0306261925001825.html
   My bibliography  Save this article

A Bayesian approach to modeling fast chargers functionality for grid frequency support

Author

Listed:
  • Mousavizade, Mirsaeed
  • Garmabdari, Rasoul
  • Bai, Feifei
  • Taghizadeh, Foad
  • Sanjari, Mohammad J.
  • Alahyari, Arman
  • Hossain, Md. Alamgir
  • Mahmoudian, Ali
  • Lu, Junwei

Abstract

As governments around the world commit to achieving net zero emissions, the upcoming years will witness a significant increase in fully electric vehicles (EVs), predominantly supported by DC fast charging (DCFC) infrastructure. While DCFCs are primarily used to provide rapid and convenient charging, their widespread adoption highlights the need to integrate them into power system operations, such as frequency control. Thus, it is crucial to estimate the level of support that DCFCs can provide for frequency control, especially in future EV adoption plans subject to data uncertainty. Existing methods, including individual EV modeling and clustering-based approaches, fall short due to high data requirements and incompatibility with transmission network models. Aggregated modeling and averaging techniques, while simpler to apply, overlook critical factors such as EV owner preferences and primarily focus on the vehicles rather than the chargers. Additionally, these methods are primarily designed for low-power chargers intended for prolonged charging sessions with more predictable plug-in patterns and are not suitable for DCFCs, where the behavior of EV owners is more dynamic and subject to higher uncertainties. EV owners are also more sensitive to meeting their expectations when using DCFCs rather than low-power chargers. To address these limitations, this paper developed a Bayesian probabilistic equivalent capacity model for DCFCs. This model uniquely incorporates deep discharge vulnerability, mobility requirements, and owner preferences, providing a comprehensive assessment of DCFC frequency support. A novel concept called mileage loss (ML) is also introduced, enabling DCFCs to contribute to frequency control. It also allows system operators and EV aggregators to analyze the risk-taking and risk-averse behaviors of EV owners in vehicle-to-grid (V2G) mode. Furthermore, the proposed model is validated using the Australian frequency regulation framework, demonstrating its scalability and applicability. The case study results demonstrate that the proposed modeling method can achieve a high level of accuracy, with an estimation precision of up to 97.7 % for the aggregated DCFC power capacity.

Suggested Citation

  • Mousavizade, Mirsaeed & Garmabdari, Rasoul & Bai, Feifei & Taghizadeh, Foad & Sanjari, Mohammad J. & Alahyari, Arman & Hossain, Md. Alamgir & Mahmoudian, Ali & Lu, Junwei, 2025. "A Bayesian approach to modeling fast chargers functionality for grid frequency support," Applied Energy, Elsevier, vol. 384(C).
    • Handle: RePEc:eee:appene:v:384:y:2025:i:c:s0306261925001825
    DOI: 10.1016/j.apenergy.2025.125452
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925001825
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125452?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. Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    2. Baumgarte, Felix & Kaiser, Matthias & Keller, Robert, 2021. "Policy support measures for widespread expansion of fast charging infrastructure for electric vehicles," Energy Policy, Elsevier, vol. 156(C).
    3. Irfan, Muhammad & Deilami, Sara & Huang, Shujuan & Tahir, Tayyab & Veettil, Binesh Puthen, 2024. "Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach," Applied Energy, Elsevier, vol. 366(C).
    4. Xiaohong Dong & Yang Ma & Xiaodan Yu & Xiangyu Wei & Yanqi Ren & Xin Zhang, 2023. "Secondary Frequency Regulation Control Strategy with Electric Vehicles Considering User Travel Uncertainty," Energies, MDPI, vol. 16(9), pages 1-18, April.
    5. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    6. Parlikar, Anupam & Schott, Maximilian & Godse, Ketaki & Kucevic, Daniel & Jossen, Andreas & Hesse, Holger, 2023. "High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation," Applied Energy, Elsevier, vol. 333(C).
    7. Meng, Jian & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qu, Bo, 2016. "Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system," Applied Energy, Elsevier, vol. 162(C), pages 966-979.
    8. Bampos, Zafeirios N. & Laitsos, Vasilis M. & Afentoulis, Konstantinos D. & Vagropoulos, Stylianos I. & Biskas, Pantelis N., 2024. "Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods," Applied Energy, Elsevier, vol. 360(C).
    9. Zhang, Hao & Lei, Nuo & Chen, Boli & Li, Bingbing & Li, Rulong & Wang, Zhi, 2024. "Modeling and control system optimization for electrified vehicles: A data-driven approach," Energy, Elsevier, vol. 310(C).
    10. Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    11. Zhang, Hao & Chen, Boli & Lei, Nuo & Li, Bingbing & Chen, Chaoyi & Wang, Zhi, 2024. "Coupled velocity and energy management optimization of connected hybrid electric vehicles for maximum collective efficiency," Applied Energy, Elsevier, vol. 360(C).
    12. Cai, Sinan & Matsuhashi, Ryuji, 2022. "Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization," Applied Energy, Elsevier, vol. 319(C).
    13. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    14. Ping, Zuowei & Li, Xiuting & He, Wei & Yang, Tao & Yuan, Ye, 2020. "Sparse learning of network-reduced models for locating low frequency oscillations in power systems," Applied Energy, Elsevier, vol. 262(C).
    Full references (including those not matched with items on IDEAS)

    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. Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    2. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    3. Wang, Zongfei & Sasse, Jan-Philipp & Trutnevyte, Evelina, 2025. "Home or workplace charging? Spatio-temporal flexibility of electric vehicles within Swiss electricity system," Energy, Elsevier, vol. 320(C).
    4. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    5. Benedek Kiss & Jose Dinis Silvestre & Rita Andrade Santos & Zsuzsa Szalay, 2021. "Environmental and Economic Optimisation of Buildings in Portugal and Hungary," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    6. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    7. 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).
    8. Boyu Xiang & Zhengyang Zhou & Shukun Gao & Guoping Lei & Zefu Tan, 2024. "A Planning Method for Charging Station Based on Long-Term Charging Load Forecasting of Electric Vehicles," Energies, MDPI, vol. 17(24), pages 1-20, December.
    9. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    10. Huang, Chunjun & Zong, Yi & You, Shi & Træholt, Chresten & Zheng, Yi & Wang, Jiawei & Zheng, Zixuan & Xiao, Xianyong, 2023. "Economic and resilient operation of hydrogen-based microgrids: An improved MPC-based optimal scheduling scheme considering security constraints of hydrogen facilities," Applied Energy, Elsevier, vol. 335(C).
    11. Ascione, Fabrizio & De Masi, Rosa Francesca & de Rossi, Filippo & Ruggiero, Silvia & Vanoli, Giuseppe Peter, 2016. "Optimization of building envelope design for nZEBs in Mediterranean climate: Performance analysis of residential case study," Applied Energy, Elsevier, vol. 183(C), pages 938-957.
    12. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    13. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
    14. Ceran, Bartosz, 2019. "The concept of use of PV/WT/FC hybrid power generation system for smoothing the energy profile of the consumer," Energy, Elsevier, vol. 167(C), pages 853-865.
    15. Feng Liu & Xingjun Huang & Longxiao Li, 2024. "The impact of green consumers on electric vehicle charging station diffusion based on complex network evolutionary game," Energy & Environment, , vol. 35(8), pages 3981-4002, December.
    16. Niemelä, Tuomo & Kosonen, Risto & Jokisalo, Juha, 2016. "Cost-optimal energy performance renovation measures of educational buildings in cold climate," Applied Energy, Elsevier, vol. 183(C), pages 1005-1020.
    17. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    18. Alessandro Di Giorgio & Emanuele De Santis & Lucia Frettoni & Stefano Felli & Francesco Liberati, 2023. "Electric Vehicle Fast Charging: A Congestion-Dependent Stochastic Model Predictive Control under Uncertain Reference," Energies, MDPI, vol. 16(3), pages 1-16, January.
    19. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    20. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, 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:appene:v:384:y:2025:i:c:s0306261925001825. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.