Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review
Hybrid energy systems (HES) provide an effective solution to the growing global energy demand while addressing the limitations of conventional sources and environmental challenges. By integrating renewable and conventional energy sources, these systems enhance reliability, reduce costs, and improve...
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| Format: | Article |
| Language: | English |
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Renewable Energy Development Center (CDER)
2024-10-01
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| Series: | Revue des Énergies Renouvelables |
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| Online Access: | https://revue.cder.dz/index.php/rer/article/view/1296 |
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| author | Walid Bensalmi Ahmed Belhani Abdellatif Bouzid-Daho |
| author_facet | Walid Bensalmi Ahmed Belhani Abdellatif Bouzid-Daho |
| author_sort | Walid Bensalmi |
| collection | DOAJ |
| description | Hybrid energy systems (HES) provide an effective solution to the growing global energy demand while addressing the limitations of conventional sources and environmental challenges. By integrating renewable and conventional energy sources, these systems enhance reliability, reduce costs, and improve efficiency. However, the variability of renewable resources such as solar and wind makes HES design more complex. This paper explores various design and sizing methods for HES, focusing on combining clean sources, including wind and solar, with conventional energy options. Through advanced optimization techniques, including artificial intelligence (AI), the study demonstrates how AI can identify optimal configurations to ensure system reliability while minimizing costs. The paper also highlights the crucial role of HES in providing energy to remote and underserved areas with limited access. This work serves as a comprehensive introduction for researchers and engineers interested in HES sizing, offering insights into technical challenges and optimization strategies, and contributing to the advancement of sustainable energy systems. |
| format | Article |
| id | doaj-art-fd94bbc10e45496eb55c7c810aa378e8 |
| institution | OA Journals |
| issn | 1112-2242 2716-8247 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Renewable Energy Development Center (CDER) |
| record_format | Article |
| series | Revue des Énergies Renouvelables |
| spelling | doaj-art-fd94bbc10e45496eb55c7c810aa378e82025-08-20T02:11:17ZengRenewable Energy Development Center (CDER)Revue des Énergies Renouvelables1112-22422716-82472024-10-0185 – 10085 – 10010.54966/jreen.v1i3.12961296Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive reviewWalid Bensalmi0Ahmed Belhani1Abdellatif Bouzid-Daho2Laboratory of Satellites, Artificial Intelligence, Cryptography, Internet of Things (LSIACIO), Constantine 1, Constantine, AlgeriaLaboratory of Satellites, Artificial Intelligence, Cryptography, Internet of Things (LSIACIO), Constantine 1, Constantine, AlgeriaLaboratoire Vision Artificielle et Automatique de Systèmes (LVAAS), Department of Biomedical Engineering, Tizi-Ouzou, AlgeriaHybrid energy systems (HES) provide an effective solution to the growing global energy demand while addressing the limitations of conventional sources and environmental challenges. By integrating renewable and conventional energy sources, these systems enhance reliability, reduce costs, and improve efficiency. However, the variability of renewable resources such as solar and wind makes HES design more complex. This paper explores various design and sizing methods for HES, focusing on combining clean sources, including wind and solar, with conventional energy options. Through advanced optimization techniques, including artificial intelligence (AI), the study demonstrates how AI can identify optimal configurations to ensure system reliability while minimizing costs. The paper also highlights the crucial role of HES in providing energy to remote and underserved areas with limited access. This work serves as a comprehensive introduction for researchers and engineers interested in HES sizing, offering insights into technical challenges and optimization strategies, and contributing to the advancement of sustainable energy systems.https://revue.cder.dz/index.php/rer/article/view/1296hybrid energy systemsadvanced optimization algorithmsoptimal sizingphotovoltaicwind energy |
| spellingShingle | Walid Bensalmi Ahmed Belhani Abdellatif Bouzid-Daho Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review Revue des Énergies Renouvelables hybrid energy systems advanced optimization algorithms optimal sizing photovoltaic wind energy |
| title | Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review |
| title_full | Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review |
| title_fullStr | Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review |
| title_full_unstemmed | Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review |
| title_short | Exploring Advanced Methodologies for Hybrid Energy System Sizing through Artificial Intelligence Techniques: a comprehensive review |
| title_sort | exploring advanced methodologies for hybrid energy system sizing through artificial intelligence techniques a comprehensive review |
| topic | hybrid energy systems advanced optimization algorithms optimal sizing photovoltaic wind energy |
| url | https://revue.cder.dz/index.php/rer/article/view/1296 |
| work_keys_str_mv | AT walidbensalmi exploringadvancedmethodologiesforhybridenergysystemsizingthroughartificialintelligencetechniquesacomprehensivereview AT ahmedbelhani exploringadvancedmethodologiesforhybridenergysystemsizingthroughartificialintelligencetechniquesacomprehensivereview AT abdellatifbouziddaho exploringadvancedmethodologiesforhybridenergysystemsizingthroughartificialintelligencetechniquesacomprehensivereview |