A review of control strategies for optimized microgrid operations
Abstract Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophist...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13056 |
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| _version_ | 1841546540111364096 |
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| author | Shaibu Ali Juma Sarah Paul Ayeng'o Cuthbert Z. M. Kimambo |
| author_facet | Shaibu Ali Juma Sarah Paul Ayeng'o Cuthbert Z. M. Kimambo |
| author_sort | Shaibu Ali Juma |
| collection | DOAJ |
| description | Abstract Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management. |
| format | Article |
| id | doaj-art-da511242d1f54f34802d6a293bea794d |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-da511242d1f54f34802d6a293bea794d2025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142785281810.1049/rpg2.13056A review of control strategies for optimized microgrid operationsShaibu Ali Juma0Sarah Paul Ayeng'o1Cuthbert Z. M. Kimambo2Department of Mechanical and Industrial Engineering College of Engineering and Technology University of Dar es Salaam Dar es Salaam TanzaniaDepartment of Mechanical and Industrial Engineering College of Engineering and Technology University of Dar es Salaam Dar es Salaam TanzaniaDepartment of Mechanical and Industrial Engineering College of Engineering and Technology University of Dar es Salaam Dar es Salaam TanzaniaAbstract Microgrids (MGs) have emerged as a promising solution for providing reliable and sustainable electricity, particularly in underserved communities and remote areas. Integrating diverse renewable energy sources into the grid has further emphasized the need for effective management and sophisticated control strategies. This review explores the crucial role of control strategies in optimizing MG operations and ensuring efficient utilization of distributed energy resources, storage systems, networks, and loads. To maximize energy source utilization and overall system performance, various control strategies are implemented, including demand response, energy storage management, data management, and generation‐load management. Employing artificial intelligence (AI) and optimization techniques further enhances these strategies, leading to improved energy management and performance in MGs. The review delves into the control strategies and their architectures, and highlights the significant contributions of AI and emerging technologies in advancing MG energy management.https://doi.org/10.1049/rpg2.13056artificial intelligencecontrol strategiesdistributed energy resourcesenergy managementmicrogridsoptimization techniques |
| spellingShingle | Shaibu Ali Juma Sarah Paul Ayeng'o Cuthbert Z. M. Kimambo A review of control strategies for optimized microgrid operations IET Renewable Power Generation artificial intelligence control strategies distributed energy resources energy management microgrids optimization techniques |
| title | A review of control strategies for optimized microgrid operations |
| title_full | A review of control strategies for optimized microgrid operations |
| title_fullStr | A review of control strategies for optimized microgrid operations |
| title_full_unstemmed | A review of control strategies for optimized microgrid operations |
| title_short | A review of control strategies for optimized microgrid operations |
| title_sort | review of control strategies for optimized microgrid operations |
| topic | artificial intelligence control strategies distributed energy resources energy management microgrids optimization techniques |
| url | https://doi.org/10.1049/rpg2.13056 |
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