IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i16p4416-d1727598.html
   My bibliography  Save this article

Evaluation of XGBoost and ANN as Surrogates for Power Flow Predictions with Dynamic Energy Storage Scenarios

Author

Listed:
  • Perez Yeptho

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Department d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain)

  • Antonio E. Saldaña-González

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Department d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain)

  • Mònica Aragüés-Peñalba

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Department d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain)

  • Sara Barja-Martínez

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments, Department d’Enginyeria Elèctrica, Universitat Politècnica de Catalunya, Av. Diagonal 647, 08028 Barcelona, Spain)

Abstract

Power flow analysis is essential for managing power systems, helping grid operators ensure reliability and efficiency. This paper explores the use of machine learning (ML) techniques as surrogates for computationally intensive power flow calculations to evaluate the effects of distributed energy resources, such as battery energy storage systems (BESSs), on grid performance. In this paper, a case study is presented where XGBoost (eXtreme Gradient Boosting) and Artificial Neural Networks (ANNs) are trained to simulate power flows in a medium-voltage grid in Norway. The impact of BESS units on line loading, transformer loading, and bus voltages is estimated across thousands of configurations, with results compared in terms of simulation time, error metrics, and robustness. In this paper it is proven that while ML models require considerable data and training time, they offer speed-up factors of up to 45×, depending on the predicted parameter. The proposed methodology can also be used to assess the impact of other grid-connected assets, such as small-scale solar plants and electric vehicle chargers, whose presence in distribution networks continues to grow.

Suggested Citation

  • Perez Yeptho & Antonio E. Saldaña-González & Mònica Aragüés-Peñalba & Sara Barja-Martínez, 2025. "Evaluation of XGBoost and ANN as Surrogates for Power Flow Predictions with Dynamic Energy Storage Scenarios," Energies, MDPI, vol. 18(16), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:16:p:4416-:d:1727598
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/16/4416/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/16/4416/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Utama, Christian & Meske, Christian & Schneider, Johannes & Ulbrich, Carolin, 2022. "Reactive power control in photovoltaic systems through (explainable) artificial intelligence," Applied Energy, Elsevier, vol. 328(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. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    2. Gao, Yuan & Hu, Zehuan & Yamate, Shun & Otomo, Junichiro & Chen, Wei-An & Liu, Mingzhe & Xu, Tingting & Ruan, Yingjun & Shang, Juan, 2025. "Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems," Applied Energy, Elsevier, vol. 384(C).
    3. Bozhen Jiang & Qin Wang & Shengyu Wu & Yidi Wang & Gang Lu, 2024. "Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review," Energies, MDPI, vol. 17(6), pages 1-17, March.
    4. Luo, Haizhi & Wang, Chenglong & Li, Cangbai & Meng, Xiangzhao & Yang, Xiaohu & Tan, Qian, 2024. "Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China," Applied Energy, Elsevier, vol. 360(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:18:y:2025:i:16:p:4416-:d:1727598. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.