| Issue |
E3S Web Conf.
Volume 619, 2025
3rd International Conference on Sustainable Green Energy Technologies (ICSGET 2025)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 13 | |
| Section | Innovations in Power Systems and Grid Infrastructure | |
| DOI | https://doi.org/10.1051/e3sconf/202561902003 | |
| Published online | 12 March 2025 | |
Utilization in Microgrids through Advanced Predictive Algorithms
1 Department of Electronics and Comunications, New Horizon College of Engineering, Bangalore, India.
2 Lovely Professional University, Phagwara, India.
3 Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.
4 Department of Medical Laboratory Technology, College of Medical Technologies, The Islamic University Najaf, Iraq.
5 Lloyd Law College, Plot No. 11, Knowledge Park II, Greater Noida, Uttar Pradesh 201310
6 Department of Electrical and Electronics Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India
* Corresponding Author: aravindake@gmail.com
The inclusion of renewable energy sources into the nominal circuit of residential microgrids poses several issues due to the stochastic nature of renewable resources. This paper examines a full-scale DSM plan for a grid-integrated residential microgrid environment focusing on improved energy usage profiles, cost-efficiency, and integration of renewables. However, in contrast to the conventional load management, this approach consists of real time demand response and energy storage system, which makes the grid more flexible and reliable. One of the main results of calculations, based on data collected from living lab environments within the GSBP in Benguerir Morocco and performed in Matlab, is the range of a monthly energy saving of about 59% coupled with a monthly use of renewable energy of about 23%. The study goes further in explaining a more generalized application of AI predictive models to demand response and non-storage techniques for reliability. Overall, the results suggest that it is still possible to gain additional levels of energy savings and grid stability – proving that such an approach can be considered as highly scalable and more universally applicable to other residential and urban microgrids. Future work will analyse how cybersecurity measures can be implemented and how the system can be adjusted according to various energy markets.
Key words: Demand-Side Management (DSM) / Real-Time Demand Response / Renewable Energy Utilization / Energy Storage Systems / Residential Microgrids / AI-Based Load Forecasting
© The Authors, published by EDP Sciences, 2025
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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