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The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management

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  • César Hernández-Hernández

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

  • Francisco Rodríguez

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

  • José Carlos Moreno

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

  • Paulo Renato Da Costa Mendes

    (Department of Automation and Systems (DAS), Federal University of Santa Catarina, Federal University of Santa Catarina, Florianópolis-SC CEP 88040-970, Brazil)

  • Julio Elias Normey-Rico

    (Department of Automation and Systems (DAS), Federal University of Santa Catarina, Federal University of Santa Catarina, Florianópolis-SC CEP 88040-970, Brazil)

  • José Luis Guzmán

    (Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain)

Abstract

Electricity load forecasting, optimal power system operation and energy management play key roles that can bring significant operational advantages to microgrids. This paper studies how methods based on time series and neural networks can be used to predict energy demand and production, allowing them to be combined with model predictive control. Comparisons of different prediction methods and different optimum energy distribution scenarios are provided, permitting us to determine when short-term energy prediction models should be used. The proposed prediction models in addition to the model predictive control strategy appear as a promising solution to energy management in microgrids. The controller has the task of performing the management of electricity purchase and sale to the power grid, maximizing the use of renewable energy sources and managing the use of the energy storage system. Simulations were performed with different weather conditions of solar irradiation. The obtained results are encouraging for future practical implementation.

Suggested Citation

  • César Hernández-Hernández & Francisco Rodríguez & José Carlos Moreno & Paulo Renato Da Costa Mendes & Julio Elias Normey-Rico & José Luis Guzmán, 2017. "The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management," Energies, MDPI, vol. 10(7), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:884-:d:103203
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    References listed on IDEAS

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    Cited by:

    1. Ramos-Teodoro, Jerónimo & Rodríguez, Francisco & Berenguel, Manuel & Torres, José Luis, 2018. "Heterogeneous resource management in energy hubs with self-consumption: Contributions and application example," Applied Energy, Elsevier, vol. 229(C), pages 537-550.
    2. Sébastien Bissey & Sébastien Jacques & Jean-Charles Le Bunetel, 2017. "The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing," Energies, MDPI, vol. 10(11), pages 1-24, October.
    3. Andrzej Pawłowski & José Luis Guzmán & Manuel Berenguel & Francisco G. Acíen & Sebastián Dormido, 2018. "Application of Predictive Feedforward Compensator to Microalgae Production in a Raceway Reactor: A Simulation Study," Energies, MDPI, vol. 11(1), pages 1-17, January.
    4. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).

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