Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids
The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to...
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MDPI AG
2025-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/9/2384 |
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| author | Alessandro Niccolai Silvia Trimarchi Lisa Francesca Barbazza Alessandro Gandelli Riccardo Zich Francesco Grimaccia Sonia Leva |
| author_facet | Alessandro Niccolai Silvia Trimarchi Lisa Francesca Barbazza Alessandro Gandelli Riccardo Zich Francesco Grimaccia Sonia Leva |
| author_sort | Alessandro Niccolai |
| collection | DOAJ |
| description | The growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to identify efficient scheduling plans and improve performance. In this paper, a new approach based on evolutionary algorithms (EAs) is designed, implemented, and tested on a real microgrid architecture to evaluate its effectiveness. The proposed approach effectively combines heuristic information with the optimization capabilities of EAs, achieving excellent results with reasonable computational effort. The proposed system is highly flexible, making it applicable to different network architectures and various objective functions. In this work, the optimization algorithm directly manages the microgrid Energy Management System, allowing for a large number of degrees of freedom that can be exploited to achieve highly competitive solutions. This method was compared with a standard scheduling approach, and an average improvement of 11.87% in fuel consumption was achieved. After analyzing the differences between the solutions obtained, the importance of the features introduced with this new approach was demonstrated. |
| format | Article |
| id | doaj-art-e68d4943651e46689d27ec00f925009a |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-e68d4943651e46689d27ec00f925009a2025-08-20T03:52:57ZengMDPI AGEnergies1996-10732025-05-01189238410.3390/en18092384Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in MicrogridsAlessandro Niccolai0Silvia Trimarchi1Lisa Francesca Barbazza2Alessandro Gandelli3Riccardo Zich4Francesco Grimaccia5Sonia Leva6Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyDepartment of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, ItalyThe growing penetration of renewable energy sources into electricity grids, along with the problems linked to the electrification of rural areas, has drawn more attention to the development of microgrids. Their Energy Management Systems (EMSs) can be based on evolutionary optimization algorithms to identify efficient scheduling plans and improve performance. In this paper, a new approach based on evolutionary algorithms (EAs) is designed, implemented, and tested on a real microgrid architecture to evaluate its effectiveness. The proposed approach effectively combines heuristic information with the optimization capabilities of EAs, achieving excellent results with reasonable computational effort. The proposed system is highly flexible, making it applicable to different network architectures and various objective functions. In this work, the optimization algorithm directly manages the microgrid Energy Management System, allowing for a large number of degrees of freedom that can be exploited to achieve highly competitive solutions. This method was compared with a standard scheduling approach, and an average improvement of 11.87% in fuel consumption was achieved. After analyzing the differences between the solutions obtained, the importance of the features introduced with this new approach was demonstrated.https://www.mdpi.com/1996-1073/18/9/2384microgridcomputational intelligenceenergy management systemsevolutionary optimizationbattery management systems |
| spellingShingle | Alessandro Niccolai Silvia Trimarchi Lisa Francesca Barbazza Alessandro Gandelli Riccardo Zich Francesco Grimaccia Sonia Leva Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids Energies microgrid computational intelligence energy management systems evolutionary optimization battery management systems |
| title | Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids |
| title_full | Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids |
| title_fullStr | Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids |
| title_full_unstemmed | Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids |
| title_short | Effective Customization of Evolutionary Algorithm-Based Energy Management System Optimization for Improved Battery Management in Microgrids |
| title_sort | effective customization of evolutionary algorithm based energy management system optimization for improved battery management in microgrids |
| topic | microgrid computational intelligence energy management systems evolutionary optimization battery management systems |
| url | https://www.mdpi.com/1996-1073/18/9/2384 |
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