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Kartik Bapusaheb Mhaske,
S. K. Agrawal,
- Student, Department of Electrical Engineering , Sanjivani College of Engineering , Kopargaon, Maharashtra, India
- Assistant Professor, Department of Electrical Engineering , Sanjivani College of Engineering , Kopargaon, Maharashtra, India
Abstract
This project focuses on creating a smart energy management system for AC microgrids using IoT technology. The goal is to help microgrids operate more efficiently, save energy, and improve security, especially in areas with limited resources. The system uses IoT devices to collect real-time data on electricity generation, storage, and usage. This data is used to balance supply and demand, cut waste, and maintain grid stability. Security measures are built into guard against cyber threats. The result is a reliable, cost-effective, and safe way to manage power in small-scale AC microgrids. Furthermore, real- time monitoring provides greater coordination between energy sources, storage units, and electrical demands. The system facilitates better use of renewable energy resources, rapid detection of abnormal operating circumstances, and effective energy scheduling. These features can save operating costs, boost system resilience, improve power quality, and encourage sustainable energy management. All things considered, the suggested IoT-based strategy provides a practical answer for smart, safe, and dependable microgrid operation in contemporary distributed energy systems. For upcoming smart grid applications, it also facilitates ongoing monitoring and well-informed energy management choices. Demand response, distributed generation, predictive maintenance, secure communication, and adaptive control may all be supported by this method, which increases flexibility and allows microgrids to react swiftly to shifting loads and the availability of renewable energy.
Keywords: AC Microgrid, Energy Management System (EMS), Internet of Things (IoT), Smart Grid, Optimization
References
1. Sedhom BE, El-Saadawi MM, El Moursi MS, Hassan MA, Eladl AA. IoT-based optimal demand side management and control scheme for smart microgrid. International Journal of Electrical Power & Energy Systems. 2021 May 1;127:106674.
2. Pardeshi DB, Maurya S, Rasal RS, Narayane DA. Energy optimization of industrial drive at Sanjivani sugar factory. International Journal of Engineering, Science and Technology. 2022 Aug 30;14(3):29-37.
3. Wang L, Sun N. Next-Generation energy Management: Particle Density algorithm for residential microgrid optimization. Egyptian Informatics Journal. 2025 Mar 1;29:100611.
4. Dhayaneswaran Y, Ashokkumar L. A study on energy conservation in textile industry. Journal of The Institution of Engineers (India): Series B. 2013 Mar;94(1):53-60.
5. William P, Gupta A, Darwante NK, Gondkar SS, Verma A, Verma V. Applications of internet of things in smart grid intelligent systems. In2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) 2022 Nov 24 (pp. 1175-1179). IEEE.
6. Abbasi M, Abbasi E, Li L, Aguilera RP, Lu D, Wang F. Review on the microgrid concept, structures, components, communication systems, and control methods. Energies. 2023 Jan 1;16(1):484.
7. Guerrero JM, Chandorkar M, Lee TL, Loh PC. Advanced control architectures for intelligent microgrids—Part I: Decentralized and hierarchical control. IEEE Transactions on Industrial Electronics. 2012 Apr 16;60(4):1254-62.
8. Siano P. Demand response and smart grids—A survey. Renewable and sustainable energy reviews. 2014 Feb 1;30:461-78.
9. Mazhar T, Irfan HM, Khan S, Haq I, Ullah I, Iqbal M, Hamam H. Analysis of cyber security attacks and its solutions for the smart grid using machine learning and blockchain methods. Future Internet. 2023 Feb 19;15(2):83.
10. Ali IA, Bukhari WA, Adnan M, Kashif MI, Danish A, Sikander A. Security and privacy in IoT-based Smart Farming: a review. Multimedia Tools and Applications. 2025 May;84(16):15971-6031.
11. Lasseter RH. Microgrids. In2002 IEEE power engineering society winter meeting. Conference proceedings (Cat. No. 02CH37309) 2002 Jan 27 (Vol. 1, pp. 305-308). IEEE.
12. Wu Y, Wu Y, Guerrero JM, Vasquez JC, Palacios-García EJ, Guan Y. IoT-enabled microgrid for intelligent energy-aware buildings: A novel hierarchical self-consumption scheme with renewables. Electronics. 2020 Mar 25;9(4):550.
13. Erol-Kantarci M, Mouftah HT. Energy-efficient information and communication infrastructures in the smart grid: A survey on interactions and open issues. IEEE Communications Surveys & Tutorials. 2014 Jul 22;17(1):179-97.
14. Wang T, O’Neill D, Kamath H. Dynamic control and optimization of distributed energy resources in a microgrid. IEEE transactions on smart grid. 2015 Jun 11;6(6):2884-94.
15. Sankhwar P. Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States. Sustainable Energy Research. 2024 Oct 3;11(1):35.
16. Loor-Duque JJ, Castro-Arias S, Astudillo León JP, Zambrano-Caicedo CJ, Reyes-Chacón IG, Vizcaíno P, Cárdenas LL, Morocho-Cayamcela ME. Lightweight Machine Learning- Based QoS Optimization for Multi-UAV Emergency Communications in FANETs. Drones. 2026 Apr 30;10(5):336.
Trends in Electrical Engineering
| Volume | 16 | |
| 03 | ||
| Received | 31/07/2026 | |
| Accepted | 05/10/2026 | |
| Published | 04/11/2026 | |
| Publication Time | 96 Days |