The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management
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 the...
| Main Authors: | , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7418 |
| _version_ | 1789406305897676800 |
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| author | Hernández Hernández, César Rodríguez Díaz, Francisco Moreno Úbeda, José Carlos Da Costa Mendes, Paulo Renato Normey Rico, Julio Elías Guzmán Sánchez, José Luis |
| author_facet | Hernández Hernández, César Rodríguez Díaz, Francisco Moreno Úbeda, José Carlos Da Costa Mendes, Paulo Renato Normey Rico, Julio Elías Guzmán Sánchez, José Luis |
| author_sort | Hernández Hernández, César |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7418 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-74182023-04-12T19:24:52Z The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management Hernández Hernández, César Rodríguez Díaz, Francisco Moreno Úbeda, José Carlos Da Costa Mendes, Paulo Renato Normey Rico, Julio Elías Guzmán Sánchez, José Luis modeling forecasting energy hubs neural networks model predictive control 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. 2020-01-16T12:32:38Z 2020-01-16T12:32:38Z 2017-06-30 info:eu-repo/semantics/article 1996-1073 http://hdl.handle.net/10835/7418 en https://www.mdpi.com/1996-1073/10/7/884 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | modeling forecasting energy hubs neural networks model predictive control Hernández Hernández, César Rodríguez Díaz, Francisco Moreno Úbeda, José Carlos Da Costa Mendes, Paulo Renato Normey Rico, Julio Elías Guzmán Sánchez, José Luis The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management |
| title | The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management |
| title_full | The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management |
| title_fullStr | The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management |
| title_full_unstemmed | The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management |
| title_short | The Comparison Study of Short-Term Prediction Methods to Enhance the Model Predictive Controller Applied to Microgrid Energy Management |
| title_sort | comparison study of short-term prediction methods to enhance the model predictive controller applied to microgrid energy management |
| topic | modeling forecasting energy hubs neural networks model predictive control |
| url | http://hdl.handle.net/10835/7418 |
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