IoT-Based Renewable Energy Integration for Urban Power Grids

Authors

  • Aditya Bhardwaj * School of Computer Science Engineering, KIIT University, Bhubaneswar, India. https://orcid.org/0000-0001-5134-2412
  • Hirad Tajvar Department of Computer Engineering, Faculty of Computer Engineering and Information Technology, Lahijan Branch, Islamic Azad University, Gilan, Iran.

https://doi.org/10.48313/siot.v3i2.248

Abstract

With urban populations on the rise, cities face increasing pressure to adopt sustainable energy solutions. Renewable energy sources like solar and wind provide environmentally friendly options, but their variability presents unique challenges for maintaining a stable power grid. This paper explores an Internet of Things (IoT)-based approach to integrating renewable energy into urban power grids, enhancing stability and efficiency through real-time monitoring, predictive analytics, and automated load balancing. By leveraging IoT sensors, smart meters, and cloud computing, this system collects and analyzes energy data to adapt to real-time demand. Results indicate a 15% reduction in carbon emissions and a 12% decrease in energy loss, showcasing the potential of IoT technology to optimize energy use in urban settings. This research highlights IoT’s role in promoting sustainable energy practices, reducing costs, and creating resilient, eco-friendly urban power grids that align with the future goals of smart cities.

Keywords:

Internet of things, Renewable energy, Smart grids, Urban power grid, Energy management, Sustainable cities

References

  1. [1] Karnouskos, S., & Terzidis, O. (2007). Towards an information infrastructure for the future internet of energy. Communication in Distributed Systems-15 itg/gi symposium (pp. 1–6). IEEE. https://www.researchgate.net/profile/Stamatis-Karnouskos/publication/224231858
  2. [2] Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted to know about smart cities: The Internet of things is the backbone. IEEE Consumer Electronics Magazine, 5(3), 60–70. https://doi.org/10.1109/MCE.2016.2556879
  3. [3] Gungor, V. C., Lu, B., & Hancke, G. P. (2010). Opportunities and challenges of wireless sensor networks in smart grid. IEEE Transactions on Industrial Electronics, 57(10), 3557–3564. https://doi.org/10.1109/TIE.2009.2039455
  4. [4] Fang, X., Misra, S., Xue, G., & Yang, D. (2011). Smart grid—The new and improved power grid: A survey. IEEE Communications Surveys & Tutorials, 14(4), 944–980. https://doi.org/10.1109/SURV.2011.101911.00087
  5. [5] Goudarzi, H., & Pedram, M. (2012). Energy-efficient virtual machine replication and placement in a cloud computing system. 2012 IEEE Fifth International Conference on Cloud Computing (pp. 750–757). IEEE. https://doi.org/10.1109/CLOUD.2012.107
  6. [6] Fakheri, S. (2022). A comprehensive review of big data applications. Big Data and Computing Visions, 2(1), 9–17. https://doi.org/10.22105/bdcv.2022.325256.1041
  7. [7] Khan, R. H., & Khan, J. Y. (2013). A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network. Computer Networks, 57(3), 825–845. https://doi.org/10.1016/j.comnet.2012.11.002
  8. [8] Aman, S., Simmhan, Y., & Prasanna, V. K. (2013). Energy management systems: State of the art and emerging trends. IEEE Communications Magazine, 51(1), 114–119. https://doi.org/10.1109/MCOM.2013.6400447
  9. [9] Singh, S., Singh, A., Sahu, A. K., & Siddiqui, N. A. (2024). Optimizing cloud performance: A comprehensive study of load balancing strategies and algorithms. Smart Internet of Things, 1(1), 1–16. https://doi.org/10.22105/siot.v1i1.34
  10. [10] Esmalifalak, M., Liu, L., Nguyen, N., Zheng, R., & Han, Z. (2014). Detecting stealthy false data injection using machine learning in smart grid. IEEE Systems Journal, 11(3), 1644–1652. https://doi.org/10.1109/JSYST.2014.2341597
  11. [11] Stojmenovic, I., & Wen, S. (2014). The fog computing paradigm: Scenarios and security issues. 2014 Federated Conference on Computer Science and Information Systems (pp. 1–8). IEEE. https://doi.org/10.15439/2014F503
  12. [12] Guerrero, J. M., Vasquez, J. C., Matas, J., De Vicuña, L. G., & Castilla, M. (2010). Hierarchical control of droop-controlled AC and DC microgrids—A general approach toward standardization. IEEE Transactions on Industrial Electronics, 58(1), 158–172. https://doi.org/10.1109/TIE.2010.2066534
  13. [13] Gholami, A., Aminifar, F., & Shahidehpour, M. (2016). Front lines against the darkness: Enhancing the resilience of the electricity grid through microgrid facilities. IEEE Electrification Magazine, 4(1), 18–24. https://doi.org/10.1109/MELE.2015.2509879

Downloads

Published

2026-06-20

Issue

Vol. 3 No. 2 (2026): Smart Internet of Things

Section

Articles

How to Cite

Bhardwaj, A. ., & Tajvar, H. . (2026). IoT-Based Renewable Energy Integration for Urban Power Grids. Smart Internet of Things, 3(2), 92-100. https://doi.org/10.48313/siot.v3i2.248

Similar Articles

1-10 of 35

You may also start an advanced similarity search for this article.