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The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature

The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, a...

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Main Authors: Mauludin, Mochamad Subchan, Khairudin, Moh., Asnawi, Rustam, Mustafa, Wan Azani, Toha, Siti Fauziah
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://irep.iium.edu.my/114163/7/114163_The%20advancement%20of%20artificial%20intelligence%27s.pdf
http://irep.iium.edu.my/114163/
https://doi.org/10.37934/araset.50.2.279293
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spelling my.iium.irep.1141632024-08-30T08:00:16Z http://irep.iium.edu.my/114163/ The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature Mauludin, Mochamad Subchan Khairudin, Moh. Asnawi, Rustam Mustafa, Wan Azani Toha, Siti Fauziah TK Electrical engineering. Electronics Nuclear engineering The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modellingutilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources. Semarak Ilmu Publishing 2024-08-20 Article PeerReviewed application/pdf en http://irep.iium.edu.my/114163/7/114163_The%20advancement%20of%20artificial%20intelligence%27s.pdf Mauludin, Mochamad Subchan and Khairudin, Moh. and Asnawi, Rustam and Mustafa, Wan Azani and Toha, Siti Fauziah (2024) The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature. Journal of Advanced Research in Applied Sciences and Engineering Technology, 50 (2). pp. 279-293. ISSN 2462-1943 https://doi.org/10.37934/araset.50.2.279293
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Mauludin, Mochamad Subchan
Khairudin, Moh.
Asnawi, Rustam
Mustafa, Wan Azani
Toha, Siti Fauziah
The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
description The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modellingutilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.
format Article
author Mauludin, Mochamad Subchan
Khairudin, Moh.
Asnawi, Rustam
Mustafa, Wan Azani
Toha, Siti Fauziah
author_facet Mauludin, Mochamad Subchan
Khairudin, Moh.
Asnawi, Rustam
Mustafa, Wan Azani
Toha, Siti Fauziah
author_sort Mauludin, Mochamad Subchan
title The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
title_short The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
title_full The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
title_fullStr The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
title_full_unstemmed The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
title_sort advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature
publisher Semarak Ilmu Publishing
publishDate 2024
url http://irep.iium.edu.my/114163/7/114163_The%20advancement%20of%20artificial%20intelligence%27s.pdf
http://irep.iium.edu.my/114163/
https://doi.org/10.37934/araset.50.2.279293
_version_ 1809136382198677504
score 13.211869

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