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Predictive Energy Storage Management with Redox Flow Batteries in Demand-Driven Microgrids

Author

Listed:
  • Dario Benavides

    (Faculty of Systems, Electronics and Industrial Engineering, Universidad Técnica de Ambato, Ambato 180206, Ecuador
    Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain)

  • Paul Arévalo-Cordero

    (Department of Electrical Engineering, University of Jaén, 23700 Linares, Spain
    Department of Electrical Engineering, Electronics, and Telecommunications (DEET), Universidad de Cuenca, Cuenca 010101, Ecuador)

  • Danny Ochoa-Correa

    (Department of Electrical Engineering, Electronics, and Telecommunications (DEET), Universidad de Cuenca, Cuenca 010101, Ecuador)

  • David Torres

    (Faculty of Systems, Electronics and Industrial Engineering, Universidad Técnica de Ambato, Ambato 180206, Ecuador)

  • Alberto Ríos

    (Faculty of Systems, Electronics and Industrial Engineering, Universidad Técnica de Ambato, Ambato 180206, Ecuador)

Abstract

Accurate demand forecasting contributes to improved energy efficiency and the development of short-term strategies. Predictive management of energy storage using redox flow batteries is presented as a robust solution for optimizing the operation of microgrids from the demand side. This study proposes an intelligent architecture that integrates demand forecasting models based on artificial neural networks and active management strategies based on the instantaneous production of renewable sources within the microgrid. The solution is supported by a real-time monitoring platform capable of analyzing data streams using continuous evaluation algorithms, enabling dynamic operational adjustments and active methods for predicting the storage system’s state of charge. The model’s effectiveness is validated using performance indicators such as RMSE, MAPE, and MSE, applied to experimental data obtained in a specialized microgrid laboratory. The results also demonstrate substantial improvements in energy planning and system operational efficiency, positioning this proposal as a viable strategy for distributed and sustainable environments in modern electricity systems.

Suggested Citation

  • Dario Benavides & Paul Arévalo-Cordero & Danny Ochoa-Correa & David Torres & Alberto Ríos, 2025. "Predictive Energy Storage Management with Redox Flow Batteries in Demand-Driven Microgrids," Sustainability, MDPI, vol. 17(19), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8915-:d:1766584
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    References listed on IDEAS

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