Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization
This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant...
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ftpubmed:oai:pubmedcentral.nih.gov:10719324 2024-01-14T10:05:49+01:00 Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization Youssef, Heba Kamel, Salah Hassan, Mohamed H. 2023-12-13 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10719324/ http://www.ncbi.nlm.nih.gov/pubmed/38092942 https://doi.org/10.1038/s41598-023-49176-0 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10719324/ http://www.ncbi.nlm.nih.gov/pubmed/38092942 http://dx.doi.org/10.1038/s41598-023-49176-0 © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . Sci Rep Article Text 2023 ftpubmed https://doi.org/10.1038/s41598-023-49176-0 2023-12-17T02:04:32Z This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO. Text Beluga Beluga whale Beluga* PubMed Central (PMC) Scientific Reports 13 1 |
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Article Youssef, Heba Kamel, Salah Hassan, Mohamed H. Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
topic_facet |
Article |
description |
This paper proposes a plan to manage energy consumption in residential areas using the demand response method, which allows electricity users to contribute to the reliability of the power system by controlling their usage. Due to the growing population, the residential sector consumes a significant amount of energy, and the objectives of this study are to lower electricity costs and the peak to average ratio, as well as reduce the amount of imported electricity from the grid. The study aims to maximize profit by properly utilizing renewable energy sources and addressing energy trading. The manta ray foraging optimization (MRFO) and long term memory MRFO (LMMRFO) algorithms are used to solve this problem. Firstly, the validation of the proposed LMMRFO technique is confirmed by seven benchmark functions and compared its results with the results of the well-known optimization algorithms including hunter prey optimization, gorilla troops optimizer, beluga whale optimization, and the original MRFO algorithm. Then, the performance of the LMMRFO is checked on the optimization of smart home energy management. In the suggested approach, a smart home decides whether to purchase or sell electricity from the commercial grid based on the cost, demand, and production of electricity from its own microgrid, which consists of a wind turbine and solar panels. Energy storage systems support the stable and dependable functioning of the power system since the solar panel and wind turbine only occasionally produce electricity. Through various case studies, the proposed plan is tested and found to be effective in reducing electricity costs and the peak to average ratio while maximizing profit. Furthermore, a comparative study is conducted to demonstrate the legality and effectiveness of LMMRFO and MRFO. |
format |
Text |
author |
Youssef, Heba Kamel, Salah Hassan, Mohamed H. |
author_facet |
Youssef, Heba Kamel, Salah Hassan, Mohamed H. |
author_sort |
Youssef, Heba |
title |
Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
title_short |
Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
title_full |
Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
title_fullStr |
Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
title_full_unstemmed |
Smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
title_sort |
smart home energy management and power trading optimization using an enhanced manta ray foraging optimization |
publisher |
Nature Publishing Group UK |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10719324/ http://www.ncbi.nlm.nih.gov/pubmed/38092942 https://doi.org/10.1038/s41598-023-49176-0 |
genre |
Beluga Beluga whale Beluga* |
genre_facet |
Beluga Beluga whale Beluga* |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10719324/ http://www.ncbi.nlm.nih.gov/pubmed/38092942 http://dx.doi.org/10.1038/s41598-023-49176-0 |
op_rights |
© The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
op_doi |
https://doi.org/10.1038/s41598-023-49176-0 |
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Scientific Reports |
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13 |
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