Issue |
E3S Web Conf.
Volume 591, 2024
International Conference on Renewable Energy Resources and Applications (ICRERA-2024)
|
|
---|---|---|
Article Number | 01015 | |
Number of page(s) | 12 | |
Section | Battery Management System and Power Quality | |
DOI | https://doi.org/10.1051/e3sconf/202459101015 | |
Published online | 14 November 2024 |
Adaptive Energy Management System for Enhancing Efficiency and Reliability in Hybrid Renewable Energy Systems
1 Mechanical Department,Vishwakarma Institute of Technology Pune India
2 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
3 Department of Computer Engineering & Applications, GLA University, Mathura,rohit.agrwal@gla.ac.in
4 Professor,Department of ECE,New Prince Shri Bhavani College of Engineering and Technology, Chennai - 600073, Tamil nadu,India,principal@newprinceshribhavani.com
5 Assistant Professor,Department of MECH,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127.,v.janakiraman_mech@psvpec.in
6 Professor and Head, Mechanical Engineering,Dr. D. Y. Patil Institute of Technology, Pimpri,
7 Department of Mathematics, School of Liberal Arts and Sciences Mohan Babu University, Tirupati, Andhra Pradesh, India Pune.sherje.nitin@gmail.com
The growing adoption of renewable energy sources and the increasing complexity of power grids necessitate advanced Energy Management Systems (EMS) capable of optimizing power distribution, improving efficiency, and ensuring system reliability. This paper presents an adaptive EMS framework designed for hybrid renewable energy systems, integrating advanced optimization algorithms and real- time data analytics to manage energy flow effectively. By leveraging predictive control and machine learning, the proposed EMS dynamically allocates energy resources, balances load demand, and mitigates power fluctuations arising from the variability of renewable sources. The system’s performance is evaluated through simulations, demonstrating significant improvements in energy efficiency, grid stability, and cost reduction. The study highlights the importance of adaptive EMS in managing hybrid energy systems and supporting the transition towards a more sustainable and resilient energy future.
Key words: Energy Management System / hybrid renewable energy / optimization / grid stability / machine learning
© The Authors, published by EDP Sciences, 2024
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