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

Perez Yeptho (), Antonio E. Saldaña-González, Mònica Aragüés-Peñalba and Sara Barja-Martínez
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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

Energies, 2025, vol. 18, issue 16, 1-19

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.

Keywords: power flow; machine learning; energy storage; distributed energy resources; grid congestion; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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