| Issue |
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
|
|
|---|---|---|
| Article Number | 01005 | |
| Number of page(s) | 10 | |
| Section | Battery Management System and Power Quality | |
| DOI | https://doi.org/10.1051/e3sconf/202459101005 | |
| Published online | 14 November 2024 | |
AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing
1 Department of Computer Engineering & Applications, GLA University, Mathura
2 Assistant Professor,Department of ECE,Prince Shri Venkateshwara Padmavathy Engineering College - 127.,m.shalini_ece@psvpec.in, Chennai
3 Asst Professor,Department of IT,New Prince Shri Bhavani College of Engineering and Technology kanmani.s@newprinceshribhavani.com Chennai - 600073, Tamil nadu,India.
4 Department of Computer Engineering,Vishwakarma Institute of Technology Pune India swati.jadhav@vit.edu
5 Department 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.com
6 Department of Mechanical engineering, Dr. D. Y. Patil Institute of Techology, vasundhara.sutar@dypvp.edu.in , Pimrpi, Pune
7 Associate Professor, AAA College of Engineering & Technology srisenthil2011@gmail.com, Sivakasi,India.
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.
Key words: AI-driven EMS / microgrids / renewable energy integration / load balancing / machine learning
© The Authors, published by EDP Sciences, 2024
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