Design and Hardware Chip Implementation of Wireless Network Topology for Smart Grid

Volume 2, Issue 1,  Article Number: 251001 (2025) 

Payal Rani1,* | Lalit Kumar1  

1Research Scholar, Department of Electrical Engineering, School of Engineering & Technology, Shri Venkateshwara University, Gajraula, U.P (India)

*Corresponding Author: payalrani105@gmail.com

Received: 24 December 2024 | Revised: 04 January 2025

Accepted: 06 January 2025 | Published Online: 08 January 2025

DOI: https://doi.org/10.5281/zenodo.15008478

© 2025 The Authors, under a Creative Commons license, Published by Scholarly Publication

Abstract

This study mainly examines the establishment of wireless networks using hardware chips that stand a chance of improving the communication of smart grid by providing high reliability, low latency as well as data confidentiality. In the suggested setup, a hierarchical architecture together with central gateway, cluster heads and sensor nodes, is exploited to allow data aggregation and thus reduce transmission overhead. Among IoT devices, wireless technologies like Zigbee and LoRa are used to enhance communication quality, thanks to the fault tolerant technique that makes the system more reliable. By studying the theoretical groundwork and the necessary hardware needed in the smart grid communication, the research is intended to identify the most significant obstacles that make current energy transfer networks relatively ineffective.

Keywords

Embedded System Design, Energy Efficiency, FPGA, Low-Power Wireless Protocols, Microcontroller Integration, Optimization of Network Topologies, Smart Grid Communication, WSNs

References

  1. Razzak, A., Islam, M. T., Roy, P., Razzaque, M. A., Hassan, M. R., & Hassan, M. M. (2024). Leveraging deep Q-learning to maximize consumer quality of experience in smart grid. Energy, 290, 130165.

[View Article]     [Google Scholar]

  1. Liu, F., Dong, T., Liu, Q., Liu, Y., & Li, S. (2024). Combining fuzzy clustering and improved long short-term memory neural networks for short-term load forecasting. Electric Power Systems Research, 226, 109967.

[View Article]     [Google Scholar]

  1. Tan, K. M., Babu, T. S., Ramachandaramurthy, V. K., Kasinathan, P., Solanki, S. G., & Raveendran, S. K. (2021). Empowering smart grid: A comprehensive review of energy storage technology and application with renewable energy integration. Journal of Energy Storage, 39, 102591.

[View Article]     [Google Scholar]

  1. Chamandoust, H., Hashemi, A., & Bahramara, S. (2021). Energy management of a smart autonomous electrical grid with a hydrogen storage system. International journal of hydrogen energy, 46, 17608-17626.

[View Article]     [Google Scholar]

  1. Shi, H., Fang, L., Chen, X., Gu, C., Ma, K., Zhang, X., … & Lim, E. G. (2024). Review of the opportunities and challenges to accelerate mass‐scale application of smart grids with large‐language models. IET Smart Grid, 7, 737-759.

[View Article]     [Google Scholar]

  1. Sheng-Wei, M., & Jian-Quan, Z. (2013). Mathematical and control scientific issues of smart grid and its prospects. Acta Automatica Sinica, 39, 119-131.

[View Article]     [Google Scholar]

  1. Chan, M., Estève, D., Escriba, C., & Campo, E. (2008). A review of smart homes—Present state and future challenges. Computer methods and programs in biomedicine, 91, 55-81.

[View Article]     [Google Scholar]

  1. Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews, 59, 710-725.

[View Article]     [Google Scholar]

  1. Bayindir, R., Colak, I., Fulli, G., & Demirtas, K. (2016). Smart grid technologies and applications. Renewable and sustainable energy reviews, 66, 499-516.

[View Article]     [Google Scholar]

  1. Anees, A., Dillon, T., Wallis, S., & Chen, Y. P. P. (2021). Optimization of day-ahead and real-time prices for smart home community. International Journal of Electrical Power & Energy Systems, 124, 106403.

[View Article]     [Google Scholar]

  1. Lu, R., & Hong, S. H. (2019). Incentive-based demand response for smart grid with reinforcement learning and deep neural network. Applied Energy, 236, 937-949.

[View Article]     [Google Scholar]

  1. Sharifi, A., & Yamagata, Y. (2015). A conceptual framework for assessment of urban energy resilience. Energy Procedia, 75, 2904-2909.

[View Article]     [Google Scholar]

  1. Deihimi, A., & Showkati, H. (2012). Application of echo state networks in short-term electric load forecasting. Energy, 39, 327-340.

[View Article]     [Google Scholar]

  1. Zhang, J., Wei, Y. M., Li, D., Tan, Z., & Zhou, J. (2018). Short term electricity load forecasting using a hybrid model. Energy, 158, 774-781.

[View Article]     [Google Scholar]

  1. Samad, T., & Kiliccote, S. (2012). Smart grid technologies and applications for the industrial sector. Computers & Chemical Engineering, 47, 76-84.

[View Article]     [Google Scholar]

  1. Yu, M., Lu, R., & Hong, S. H. (2016). A real-time decision model for industrial load management in a smart grid. Applied energy, 183, 1488-1497.

[View Article]     [Google Scholar]

  1. Marzband, M., Yousefnejad, E., Sumper, A., & Domínguez-García, J. L. (2016). Real time experimental implementation of optimum energy management system in standalone microgrid by using multi-layer ant colony optimization. International Journal of Electrical Power & Energy Systems, 75, 265-274.

[View Article]     [Google Scholar]

  1. Hafeez, G., Alimgeer, K. S., & Khan, I. (2020). Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Applied Energy, 269, 114915.

[View Article]     [Google Scholar]

  1. Li, W., & Zhang, X. (2014). Simulation of the smart grid communications: Challenges, techniques, and future trends. Computers & Electrical Engineering, 40, 270-288.

[View Article]     [Google Scholar]

  1. Nan, S., Zhou, M., & Li, G. (2018). Optimal residential community demand response scheduling in smart grid. Applied Energy, 210, 1280-1289.

[View Article]     [Google Scholar]

  1. Faheem, M., Shah, S. B. H., Butt, R. A., Raza, B., Anwar, M., Ashraf, M. W., … & Gungor, V. C. (2018). Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Computer Science Review, 30, 1-30.

[View Article]     [Google Scholar]

  1. Das, L., Munikoti, S., Natarajan, B., & Srinivasan, B. (2020). Measuring smart grid resilience: Methods, challenges and opportunities. Renewable and Sustainable Energy Reviews, 130, 109918.

[View Article]     [Google Scholar]

  1. Sharma, K., & Saini, L. M. (2015). Performance analysis of smart metering for smart grid: An overview. Renewable and Sustainable Energy Reviews, 49, 720-735.

[View Article]     [Google Scholar]

  1. Tuballa, M. L., & Abundo, M. L. (2016). A review of the development of Smart Grid technologies. Renewable and Sustainable Energy Reviews, 59, 710-725.

[View Article]     [Google Scholar]

  1. Malik, F. H., & Lehtonen, M. (2016). A review: Agents in smart grids. Electric Power Systems Research, 131, 71-79.

[View Article]     [Google Scholar]

  1. Bigerna, S., Bollino, C. A., & Micheli, S. (2016). Socio-economic acceptability for smart grid development–a comprehensive review. Journal of Cleaner Production, 131, 399-409.

[View Article]     [Google Scholar]

  1. Hussain, M., & Gao, Y. (2018). A review of demand response in an efficient smart grid environment. The Electricity Journal, 31, 55-63.

[View Article]     [Google Scholar]

  1. Einaddin, A. H., & Yazdankhah, A. S. (2020). A novel approach for multi-objective optimal scheduling of large-scale EV fleets in a smart distribution grid considering realistic and stochastic modeling framework. International Journal of Electrical Power & Energy Systems, 117, 105617.

[View Article] [Google Scholar]

Cite This Article

P. Rani and L. Kumar, “Design and Hardware Chip Implementation of Wireless Network Topology for Smart Grid,” Radius: Journal of Science and Technology 2(1) (2025) 251001. https://doi.org/10.5281/zenodo.15008478

Rights & Permission

This is an open access article published under the Creative Commons Attribution (CC BY) International License, which allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. No permission is needed to reuse this content under the terms of the license.

For uses not covered above, please contact the Scholarly Publication Rights Department.