| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 05004 | |
| Number of page(s) | 7 | |
| Section | Information Secutity | |
| DOI | https://doi.org/10.1051/e3sconf/202338705004 | |
| Published online | 15 May 2023 | |
Intelligent Energy Management System for Smart Grids Using Machine Learning Algorithms
1 Assistant Professor, SRM Institute of Science & Technology, Kattankulathur, India
2 New Prince Shri Bhavani College Of Engineering a ndTechnology, Approved by AICTE, Affilated to Anna University, Chennai, India
3 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
4 Assistant Professor, Mohammed Sathak College of Engineering & Technology, India
5 Assistant Professor, Prince Dr.K.Vasudevan College of Engineering and Technology, Chennai - 127
* Correspondingauthor: srisenthil2011@gmail.com
Smart grid technology is rapidly advancing and providing various opportunities for efficient energy management. To achieve the full potential of smart grids, intelligent energy management systems (IEMS) are required that can optimally manage and control the distributed energy resources (DERs). In this paper, proposed an IEMS using the Deep Reinforcement Learning (DRL) algorithm to manage the energy consumption and production in a smart grid. The proposed methodology aims to minimize the energy cost while maintaining the stability and reliability of the grid. The performance of the proposed IEMS is evaluated on a simulated smart grid, and the results show that it can effectively manage the energy resources while minimizing the energy cost.
Key words: Smart Grids / Energy Management / Distributed Energy Resources / Deep Reinforcement Learning / Optimization
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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