A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration
This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10479 |
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| author | Fadi Agha Kassab Rusber Rodriguez Berk Celik Fabrice Locment Manuela Sechilariu |
| author_facet | Fadi Agha Kassab Rusber Rodriguez Berk Celik Fabrice Locment Manuela Sechilariu |
| author_sort | Fadi Agha Kassab |
| collection | DOAJ |
| description | This article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these stages. Key findings emphasize the importance of optimal sizing to minimize costs and reduce carbon dioxide (CO<sub>2</sub>) emissions while ensuring system reliability. In a pedagogical manner, this review highlights the integrated methodologies that simultaneously address sizing and energy management and the potential of emerging technologies, such as smart grids and electric vehicles, to enhance energy efficiency and sustainability. This study outlines the importance of accurate load modeling and carefully selecting models for renewable energy sources and energy storage systems, including degradation models, to achieve long-term operational efficiency and sustainability in microgrid design and operation. Future research should focus on developing multi-objective optimization techniques and incorporating cutting-edge technologies for improved microgrid planning and operation. |
| format | Article |
| id | doaj-art-4e15ccfe498d4dc6bfda323cc6de2d09 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4e15ccfe498d4dc6bfda323cc6de2d092025-08-20T01:53:49ZengMDPI AGApplied Sciences2076-34172024-11-0114221047910.3390/app142210479A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable IntegrationFadi Agha Kassab0Rusber Rodriguez1Berk Celik2Fabrice Locment3Manuela Sechilariu4Avenues, Centre Pierre Guillaumat, Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, FranceAvenues, Centre Pierre Guillaumat, Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, FranceAvenues, Centre Pierre Guillaumat, Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, FranceAvenues, Centre Pierre Guillaumat, Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, FranceAvenues, Centre Pierre Guillaumat, Université de Technologie de Compiègne, CS 60 319, 60203 Compiègne, FranceThis article comprehensively reviews strategies for optimal microgrid planning, focusing on integrating renewable energy sources. The study explores heuristic, mathematical, and hybrid methods for microgrid sizing and optimization-based energy management approaches, addressing the need for detailed energy planning and seamless integration between these stages. Key findings emphasize the importance of optimal sizing to minimize costs and reduce carbon dioxide (CO<sub>2</sub>) emissions while ensuring system reliability. In a pedagogical manner, this review highlights the integrated methodologies that simultaneously address sizing and energy management and the potential of emerging technologies, such as smart grids and electric vehicles, to enhance energy efficiency and sustainability. This study outlines the importance of accurate load modeling and carefully selecting models for renewable energy sources and energy storage systems, including degradation models, to achieve long-term operational efficiency and sustainability in microgrid design and operation. Future research should focus on developing multi-objective optimization techniques and incorporating cutting-edge technologies for improved microgrid planning and operation.https://www.mdpi.com/2076-3417/14/22/10479microgridrenewable energy integrationenergy managementoptimal sizinghybrid methodologiesoptimization algorithms |
| spellingShingle | Fadi Agha Kassab Rusber Rodriguez Berk Celik Fabrice Locment Manuela Sechilariu A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration Applied Sciences microgrid renewable energy integration energy management optimal sizing hybrid methodologies optimization algorithms |
| title | A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration |
| title_full | A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration |
| title_fullStr | A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration |
| title_full_unstemmed | A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration |
| title_short | A Comprehensive Review of Sizing and Energy Management Strategies for Optimal Planning of Microgrids with PV and Other Renewable Integration |
| title_sort | comprehensive review of sizing and energy management strategies for optimal planning of microgrids with pv and other renewable integration |
| topic | microgrid renewable energy integration energy management optimal sizing hybrid methodologies optimization algorithms |
| url | https://www.mdpi.com/2076-3417/14/22/10479 |
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