Energies 2017, 10(7), 884; https://doi.org/10.3390/en10070884
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 1
, José Carlos Moreno 1, Paulo Renato da Costa Mendes 2, Julio Elias Normey-Rico 2 and José Luis Guzmán 1
, José Carlos Moreno 1, Paulo Renato da Costa Mendes 2, Julio Elias Normey-Rico 2 and José Luis Guzmán 11
Department of Informatics, Agrifood Campus of International Excellence ceiA3, CIESOL Research Center on Solar Energy, University of Almería, 04120 Almería, Spain
2
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
Received: 1 June 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 30 June 2017
(This article belongs to the Collection Smart Grid)
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-TextKeywords:
modeling; forecasting; energy hubs; neural networks; model predictive control
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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.
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