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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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    1. Ardakani, F.J. & Ardehali, M.M., 2014. "Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types," Energy, Elsevier, vol. 65(C), pages 452-461.
    2. Carmeli, Maria Stefania & Castelli-Dezza, Francesco & Mauri, Marco & Marchegiani, Gabriele & Rosati, Daniele, 2012. "Control strategies and configurations of hybrid distributed generation systems," Renewable Energy, Elsevier, vol. 41(C), pages 294-305.
    3. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    4. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
    5. Gutiérrez-Alcaraz, G. & Galván, E. & González-Cabrera, N. & Javadi, M.S., 2015. "Renewable energy resources short-term scheduling and dynamic network reconfiguration for sustainable energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 256-264.
    6. Eghtedarpour, N. & Farjah, E., 2012. "Control strategy for distributed integration of photovoltaic and energy storage systems in DC micro-grids," Renewable Energy, Elsevier, vol. 45(C), pages 96-110.
    7. Kurohane, Kyohei & Uehara, Akie & Senjyu, Tomonobu & Yona, Atsushi & Urasaki, Naomitsu & Funabashi, Toshihisa & Kim, Chul-Hwan, 2011. "Control strategy for a distributed DC power system with renewable energy," Renewable Energy, Elsevier, vol. 36(1), pages 42-49.
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    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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