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Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems

Author

Listed:
  • Joaquín Luque

    (Department of Electronic Technology, University of Seville, 41004 Seville, Spain)

  • Benedikt Tepe

    (Chair of Electrical Energy Storage Technology, Department of Energy and Process Engineering, TUM School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich, Germany)

  • Diego Larios

    (Department of Electronic Technology, University of Seville, 41004 Seville, Spain)

  • Carlos León

    (Department of Electronic Technology, University of Seville, 41004 Seville, Spain)

  • Holger Hesse

    (Department of Mechanical Engineering, Institute for Energy and Propulsion Technologies (IEAT), Kempten University of Applied Sciences, 87435 Kempten, Germany)

Abstract

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

Suggested Citation

  • Joaquín Luque & Benedikt Tepe & Diego Larios & Carlos León & Holger Hesse, 2023. "Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems," Energies, MDPI, vol. 16(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5548-:d:1200107
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    References listed on IDEAS

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    1. Bilal Naji Alhasnawi & Basil H. Jasim & Maria Dolores Esteban & Josep M. Guerrero, 2020. "A Novel Smart Energy Management as a Service over a Cloud Computing Platform for Nanogrid Appliances," Sustainability, MDPI, vol. 12(22), pages 1-47, November.
    2. Holger C. Hesse & Michael Schimpe & Daniel Kucevic & Andreas Jossen, 2017. "Lithium-Ion Battery Storage for the Grid—A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids," Energies, MDPI, vol. 10(12), pages 1-42, December.
    3. Resch, Matthias & Bühler, Jochen & Klausen, Mira & Sumper, Andreas, 2017. "Impact of operation strategies of large scale battery systems on distribution grid planning in Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1042-1063.
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