Scalable Internet of Things Enabled Intelligent Solutions for Proactive Energy Engagement in Smart Grids Predictive Load Balancing and Sustainable Power Distribution

M. SilpaRaj; R. Senthil Kumar; K. Jayakumar; M. Gopila; S. Senthil Kumar; Kazi Kutubuddin Sayyad Liyakat

doi:10.2991/978-94-6463-718-2_85

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Scalable Internet of Things Enabled Intelligent Solutions for Proactive Energy Engagement in Smart Grids Predictive Load Balancing and Sustainable Power Distribution

Authors

M. SilpaRaj¹^{, *}, R. Senthil Kumar², K. Jayakumar³, M. Gopila⁴, S. Senthil Kumar⁵, Kazi Kutubuddin Sayyad Liyakat⁶

¹Assistant Professor, Department of Computer Science and Engineering (Cyber Security), CVR College of Engineering, Hyderabad, Telangana, India

²Assistant Professor, Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Chennai, 603203, Tamil Nadu, India

³Professor, Department of Electrical and Electronics Engineering, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

⁴Assistant Professor, Department of EEE, Sona College of Technology, Salem, Tamil Nadu, India

⁵Professor, Department of EEE, New Prince Shri Bhavani College of Engineering and Technology, Chennai, Tamil Nadu, India

⁶Professor, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

^*Corresponding author. Email: silparajm@gmail.com

Corresponding Author

M. SilpaRaj

Available Online 23 May 2025.

DOI: 10.2991/978-94-6463-718-2_85 How to use a DOI?
Keywords: IoT; smart grids; predictive load balancing; sustainable power distribution; scalability; real-time deployment; hybrid AI
Abstract: IoT technologies enable smart grids to be more proactive than ever in energy engagement, allowing for predictive load balancing systems to optimize energy across the spectrum. By being implemented in a full-scale approach, being tested in real scenarios, and, having secure communication protocols running, this research overcomes the scalability issues and improves the resilience of the entire system. While existing studies are primarily focused on technical feasibility without assessing economic viability or environmental effects, this work provides an extended cost-benefit analysis for different stakeholders, explores hybrid AI techniques including performance mapping for real-time system optimization, and devises a new mechanism for real-time demand response pricing. Additionally, it also enables novel battery degradation mitigation strategies, user behavior analysis and edge AI for real-time grid management. This work will help lay the groundwork for wider adoption of scalable and secure Internet-of-Things (IoT) enabled smart grids to ensure longterm and sustainable energy dispatch by overcoming regulatory barriers for vehicle-to-grid (V2G) systems, as well as discussing cybersecurity and environmentally sustainable solutions.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
Series: Advances in Computer Science Research
Publication Date: 23 May 2025
ISBN: 978-94-6463-718-2
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-718-2_85 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - M. SilpaRaj
AU  - R. Senthil Kumar
AU  - K. Jayakumar
AU  - M. Gopila
AU  - S. Senthil Kumar
AU  - Kazi Kutubuddin Sayyad Liyakat
PY  - 2025
DA  - 2025/05/23
TI  - Scalable Internet of Things Enabled Intelligent Solutions for Proactive Energy Engagement in Smart Grids Predictive Load Balancing and Sustainable Power Distribution
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1004
EP  - 1016
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_85
DO  - 10.2991/978-94-6463-718-2_85
ID  - SilpaRaj2025
ER  -

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