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

Hernández-Hernández, César; Rodríguez, Francisco; Moreno, José Carlos; Da Costa Mendes, Paulo Renato; Normey-Rico, Julio Elias; Guzmán, José Luis

doi:10.3390/en10070884

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

César Hernández-Hernández

^1,*,

Francisco Rodríguez

José Carlos Moreno

Paulo Renato Da Costa Mendes

Julio Elias Normey-Rico

² and

José Luis Guzmán

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

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

Author to whom correspondence should be addressed.

Academic Editor: Frede Blaabjerg

Energies 2017, 10(7), 884; https://doi.org/10.3390/en10070884

Received: 1 June 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017

(This article belongs to the Collection Smart Grid)

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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. View Full-Text

Keywords: modeling; forecasting; energy hubs; neural networks; model predictive control

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MDPI and ACS Style

Hernández-Hernández, C.; Rodríguez, F.; Moreno, J.C.; Da Costa Mendes, P.R.; Normey-Rico, J.E.; Guzmán, J.L. The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management. Energies 2017, 10, 884. https://doi.org/10.3390/en10070884

AMA Style

Hernández-Hernández C, Rodríguez F, Moreno JC, Da Costa Mendes PR, Normey-Rico JE, Guzmán JL. The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management. Energies. 2017; 10(7):884. https://doi.org/10.3390/en10070884

Chicago/Turabian Style

Hernández-Hernández, César, Francisco Rodríguez, José C. Moreno, Paulo R. Da Costa Mendes, Julio E. Normey-Rico, and José L. Guzmán. 2017. "The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management" Energies 10, no. 7: 884. https://doi.org/10.3390/en10070884

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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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