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Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids
Energy advancement and innovation have generated several challenges for large modernized cities, such as the increase in energy demand, causing the appearance of the small power grid with a local source of supply, called the Microgrid. A Microgrid operates either connected to the national centralize...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604070/ https://www.ncbi.nlm.nih.gov/pubmed/31212670 http://dx.doi.org/10.3390/s19112642 |
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author | Barros, Eric Bernardes C. Filho, Dionísio Machado L. Batista, Bruno Guazzelli Kuehne, Bruno Tardiole Peixoto, Maycon Leone M. |
author_facet | Barros, Eric Bernardes C. Filho, Dionísio Machado L. Batista, Bruno Guazzelli Kuehne, Bruno Tardiole Peixoto, Maycon Leone M. |
author_sort | Barros, Eric Bernardes C. |
collection | PubMed |
description | Energy advancement and innovation have generated several challenges for large modernized cities, such as the increase in energy demand, causing the appearance of the small power grid with a local source of supply, called the Microgrid. A Microgrid operates either connected to the national centralized power grid or singly, as a power island mode. Microgrids address these challenges using sensing technologies and Fog-Cloudcomputing infrastructures for building smart electrical grids. A smart Microgrid can be used to minimize the power demand problem, but this solution needs to be implemented correctly so as not to increase the amount of data being generated. Thus, this paper proposes the use of Fog computing to help control power demand and manage power production by eliminating the high volume of data being passed to the Cloud and decreasing the requests’ response time. The GridLab-d simulator was used to create a Microgrid, where it is possible to exchange information between consumers and generators. Thus, to understand the potential of the Fog in this scenario, a performance evaluation is performed to verify how factors such as residence number, optimization algorithms, appliance shifting, and energy sources may influence the response time and resource usage. |
format | Online Article Text |
id | pubmed-6604070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66040702019-07-19 Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids Barros, Eric Bernardes C. Filho, Dionísio Machado L. Batista, Bruno Guazzelli Kuehne, Bruno Tardiole Peixoto, Maycon Leone M. Sensors (Basel) Article Energy advancement and innovation have generated several challenges for large modernized cities, such as the increase in energy demand, causing the appearance of the small power grid with a local source of supply, called the Microgrid. A Microgrid operates either connected to the national centralized power grid or singly, as a power island mode. Microgrids address these challenges using sensing technologies and Fog-Cloudcomputing infrastructures for building smart electrical grids. A smart Microgrid can be used to minimize the power demand problem, but this solution needs to be implemented correctly so as not to increase the amount of data being generated. Thus, this paper proposes the use of Fog computing to help control power demand and manage power production by eliminating the high volume of data being passed to the Cloud and decreasing the requests’ response time. The GridLab-d simulator was used to create a Microgrid, where it is possible to exchange information between consumers and generators. Thus, to understand the potential of the Fog in this scenario, a performance evaluation is performed to verify how factors such as residence number, optimization algorithms, appliance shifting, and energy sources may influence the response time and resource usage. MDPI 2019-06-11 /pmc/articles/PMC6604070/ /pubmed/31212670 http://dx.doi.org/10.3390/s19112642 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barros, Eric Bernardes C. Filho, Dionísio Machado L. Batista, Bruno Guazzelli Kuehne, Bruno Tardiole Peixoto, Maycon Leone M. Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids |
title | Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids |
title_full | Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids |
title_fullStr | Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids |
title_full_unstemmed | Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids |
title_short | Fog Computing Model to Orchestrate the Consumption and Production of Energy in Microgrids |
title_sort | fog computing model to orchestrate the consumption and production of energy in microgrids |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6604070/ https://www.ncbi.nlm.nih.gov/pubmed/31212670 http://dx.doi.org/10.3390/s19112642 |
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