AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing
The increasing adoption of microgrids, particularly with renewable energy sources, necessitates advanced energy management systems (EMS) that can efficiently handle dynamic power demands and supply fluctuations. This paper proposes an AI-driven EMS model specifically designed for optimizing energy d...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
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| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/121/e3sconf_icrera2024_01005.pdf |
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| author | Bhardwaj Diwakar M Shalini Kanmani Jebaseeli S. Jadhav Swati Alabdeli Haider Sutar Vasundhara Senthil Kumar R. |
| author_facet | Bhardwaj Diwakar M Shalini Kanmani Jebaseeli S. Jadhav Swati Alabdeli Haider Sutar Vasundhara Senthil Kumar R. |
| author_sort | Bhardwaj Diwakar |
| collection | DOAJ |
| description | The increasing adoption of microgrids, particularly with renewable energy sources, necessitates advanced energy management systems (EMS) that can efficiently handle dynamic power demands and supply fluctuations. This paper proposes an AI-driven EMS model specifically designed for optimizing energy distribution and load balancing within microgrids. The system leverages machine learning algorithms to predict energy demand and adapt the power allocation in real-time, ensuring efficient integration of renewable resources while maintaining grid stability. A simulation of the proposed system demonstrates significant improvements in energy efficiency and stability when compared to traditional EMS approaches. This research highlights the importance of intelligent systems in achieving sustainable and reliable microgrid operations. |
| format | Article |
| id | doaj-art-f6855aac0c4142ab95d834dc4035f9c6 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-f6855aac0c4142ab95d834dc4035f9c62024-11-21T11:32:00ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015910100510.1051/e3sconf/202459101005e3sconf_icrera2024_01005AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load BalancingBhardwaj Diwakar0M Shalini1Kanmani Jebaseeli S.2Jadhav Swati3Alabdeli Haider4Sutar Vasundhara5Senthil Kumar R.6Department of Computer Engineering & Applications, GLA UniversityAssistant Professor,Department of ECE,Prince Shri Venkateshwara Padmavathy Engineering College - 127.,m.shalini_ece@psvpec.inAsst Professor,Department of IT,New Prince Shri Bhavani College of Engineering and Technology kanmani.s@newprinceshribhavani.comDepartment of Computer Engineering,Vishwakarma Institute of TechnologyDepartment of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq Department of computers Techniques engineering, College of technical engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq Department of computers Techniques engineering, College of technical engineering, The Islamic University of Babylon, Babylon, Iraq haideralabdeli@gmail.comDepartment of Mechanical engineering, Dr. D. Y. Patil Institute of Techology, vasundhara.sutar@dypvp.edu.in Associate Professor, AAA College of Engineering & Technology srisenthil2011@gmail.comThe increasing adoption of microgrids, particularly with renewable energy sources, necessitates advanced energy management systems (EMS) that can efficiently handle dynamic power demands and supply fluctuations. This paper proposes an AI-driven EMS model specifically designed for optimizing energy distribution and load balancing within microgrids. The system leverages machine learning algorithms to predict energy demand and adapt the power allocation in real-time, ensuring efficient integration of renewable resources while maintaining grid stability. A simulation of the proposed system demonstrates significant improvements in energy efficiency and stability when compared to traditional EMS approaches. This research highlights the importance of intelligent systems in achieving sustainable and reliable microgrid operations.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/121/e3sconf_icrera2024_01005.pdfai-driven emsmicrogridsrenewable energy integrationload balancingmachine learning |
| spellingShingle | Bhardwaj Diwakar M Shalini Kanmani Jebaseeli S. Jadhav Swati Alabdeli Haider Sutar Vasundhara Senthil Kumar R. AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing E3S Web of Conferences ai-driven ems microgrids renewable energy integration load balancing machine learning |
| title | AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing |
| title_full | AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing |
| title_fullStr | AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing |
| title_full_unstemmed | AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing |
| title_short | AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing |
| title_sort | ai driven energy management systems for microgrids optimizing renewable energy integration and load balancing |
| topic | ai-driven ems microgrids renewable energy integration load balancing machine learning |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/121/e3sconf_icrera2024_01005.pdf |
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