Advanced AI approaches for the modeling and optimization of microgrid energy systems
Abstract The present study examines AI techniques to reduce the cost and CO2 emissions for designing and controlling microgrid at minimum cost and providing a power supply to a residential complex of 100 units. Three AI techniques, Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-96145-w |
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| author | Mohammed Amine Hoummadi Badre Bossoufi Mohammed Karim Ahmed Althobaiti Thamer A. H. Alghamdi Mohammed Alenezi |
| author_facet | Mohammed Amine Hoummadi Badre Bossoufi Mohammed Karim Ahmed Althobaiti Thamer A. H. Alghamdi Mohammed Alenezi |
| author_sort | Mohammed Amine Hoummadi |
| collection | DOAJ |
| description | Abstract The present study examines AI techniques to reduce the cost and CO2 emissions for designing and controlling microgrid at minimum cost and providing a power supply to a residential complex of 100 units. Three AI techniques, Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO), are employed to optimize the optimal composition of energy sources based on solar energy and wind energy, battery storage, and load profiles. GA, from natural selection, is constantly seeking the best configuration. ABC models honeybee foraging behavior to achieve efficient exploration, and ACO models ant colony decision-making to achieve optimal energy configuration. These AI models maximize the use of renewable energy, reduce wastage, and improve microgrid resilience and responsiveness to supply and demand fluctuations. Experiments demonstrate the revolutionary potential of AI to control microgrids. The optimization achieves the lowest electricity cost of 0.037 USD/kWh, a reduction by 67% from Fez’s reference cost (0.115 USD/kWh) and guarantees a supply of power. These results illustrate the ability of AI to power cheap and clean energy systems. |
| format | Article |
| id | doaj-art-be4917cf49514e2ca2b16c01b08f2218 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-be4917cf49514e2ca2b16c01b08f22182025-08-20T03:10:09ZengNature PortfolioScientific Reports2045-23222025-04-0115112310.1038/s41598-025-96145-wAdvanced AI approaches for the modeling and optimization of microgrid energy systemsMohammed Amine Hoummadi0Badre Bossoufi1Mohammed Karim2Ahmed Althobaiti3Thamer A. H. Alghamdi4Mohammed Alenezi5LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah UniversityLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah UniversityLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah UniversityDepartment of Electrical Engineering, College of Engineering, Taif UniversityWolfson Centre for Magnetics, School of Engineering, Cardiff UniversityWolfson Centre for Magnetics, School of Engineering, Cardiff UniversityAbstract The present study examines AI techniques to reduce the cost and CO2 emissions for designing and controlling microgrid at minimum cost and providing a power supply to a residential complex of 100 units. Three AI techniques, Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO), are employed to optimize the optimal composition of energy sources based on solar energy and wind energy, battery storage, and load profiles. GA, from natural selection, is constantly seeking the best configuration. ABC models honeybee foraging behavior to achieve efficient exploration, and ACO models ant colony decision-making to achieve optimal energy configuration. These AI models maximize the use of renewable energy, reduce wastage, and improve microgrid resilience and responsiveness to supply and demand fluctuations. Experiments demonstrate the revolutionary potential of AI to control microgrids. The optimization achieves the lowest electricity cost of 0.037 USD/kWh, a reduction by 67% from Fez’s reference cost (0.115 USD/kWh) and guarantees a supply of power. These results illustrate the ability of AI to power cheap and clean energy systems.https://doi.org/10.1038/s41598-025-96145-wEnergy optimizationRenewable integrationAlgorithmic energy managementMicrogridTurbine |
| spellingShingle | Mohammed Amine Hoummadi Badre Bossoufi Mohammed Karim Ahmed Althobaiti Thamer A. H. Alghamdi Mohammed Alenezi Advanced AI approaches for the modeling and optimization of microgrid energy systems Scientific Reports Energy optimization Renewable integration Algorithmic energy management Microgrid Turbine |
| title | Advanced AI approaches for the modeling and optimization of microgrid energy systems |
| title_full | Advanced AI approaches for the modeling and optimization of microgrid energy systems |
| title_fullStr | Advanced AI approaches for the modeling and optimization of microgrid energy systems |
| title_full_unstemmed | Advanced AI approaches for the modeling and optimization of microgrid energy systems |
| title_short | Advanced AI approaches for the modeling and optimization of microgrid energy systems |
| title_sort | advanced ai approaches for the modeling and optimization of microgrid energy systems |
| topic | Energy optimization Renewable integration Algorithmic energy management Microgrid Turbine |
| url | https://doi.org/10.1038/s41598-025-96145-w |
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