Investigating communication and security challenges in power microgrids and designing a secure communication network using appropriate protocols and encryption techniques of artificial intelligence technology
Keywords:
Communication network, microgrids, in power grid, artificial intelligenceAbstract
With the expansion of the use of renewable energy sources and the need for smart energy distribution systems, microgrids have become one of the vital components of power systems. Considering the increasing importance of power microgrids in modern energy systems and their key role in increasing efficiency and reducing dependence on large power grids, designing a reliable communication network for the utilization of these microgrids is crucial. In this research, a comprehensive framework for simulating, designing, and evaluating the communication network of power microgrids is presented. First, power microgrids were simulated using the Python programming language to enable analysis of the behavior and performance of these systems under different conditions. Then, a communication network based on artificial intelligence algorithms was designed and developed, which ensures the ability to coordinate and manage microgrids optimally. Next, in order to investigate the stability and security of the designed communication network, various types of cyber attacks were simulated. These attacks included data intrusion, disruption of communications, and various cyber-destruction scenarios. Also, smart defense strategies were developed to counter these attacks and their effectiveness in maintaining the performance of the communication network and preventing negative impacts on microgrids and the main power grid during outage conditions was evaluated.
The results show that the designed communication network is not only efficient in managing and utilizing microgrids, but also has the ability to resist cyber attacks and maintain system stability. This research can be used as a basis for developing smart and secure systems in energy management and power microgrids and provide an effective solution to address security and stability challenges in power systems.
References
Ahmed, S., Ali, A., Ciocia, A., & D'Angola, A. (2024). Technological Elements behind the Renewable Energy Community: Current Status, Existing Gap, Necessity, and Future Perspective-Overview. https://doi.org/10.3390/en17133100
Cai, X., Nan, X., Gao, B., & Yuan, J. (2023). Distributed Event-Triggered Secondary Control of Microgrids With Quantization Communication. https://ieeexplore.ieee.org/document/9925620
Cornerstone, O. (2024). The future of learning: Building agile and adaptable workforces. Cornerstone OnDemand
Gaurav, S., & Kumar, C. (2022). Coordinated Control of EV Charging Stations in Smart Transformer based Microgrid.
Hao, Z., Atakan, A., Brandić, I., & Erol-Kantarci, M. (2021). Multiagent Bayesian Deep Reinforcement Learning for Microgrid Energy Management Under Communication Failures. https://arxiv.org/abs/2111.11868
Hu, J., & Ma, H. (2023). Distributed Real-time Optimal Power Flow Strategy for DC Microgrid Under Stochastic Communication Networks. https://www.researchgate.net/publication/374069625_Distributed_Real-time_Optimal_Power_Flow_Strategy_for_DC_Microgrid_Under_Stochastic_Communication_Networks
Huang, H., Poor, H. V., Davis, K. R., Overbye, T. J., Layton, A., Goulart, A. E., & Zonouz, S. (2024). Toward Resilient Modern Power Systems: From Single-Domain to Cross-Domain Resilience Enhancement. https://doi.org/10.1109/JPROC.2024.3405709
Leung, K.-C., Zhu, X., Ding, H., & He, Q. (2023). Energy Management for Renewable Microgrid Cooperation: Theory and Algorithm. https://www.researchgate.net/publication/369787563_Energy_Management_for_Renewable_Microgrid_Cooperation_Theory_and_Algorithm
Liu, X. K., Wang, S. Q., Chi, M., Liu, Z. W., & Wang, Y. W. (2024). Resilient Secondary Control and Stability Analysis for DC Microgrids Under Mixed Cyber Attacks. https://ieeexplore.ieee.org/document/10092457
Mannini, R., Eynard, J., & Grieu, S. (2022). A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids. https://doi.org/10.3390/en15197009
Niknejad, P., Rahmani, F., Barzegaran, M., & Vanfretti, L. (2021). A time-sensitive networking-enabled synchronized three-phase and phasor measurement-based monitoring system for microgrids.
Reddy, G. P., Kumar, Y. V. P., & Chakravarthi, M. (2022). Communication Technologies for Interoperable Smart Microgrids in Urban Energy Community: A Broad Review of the State of the Art, Challenges, and Research Perspectives. https://doi.org/10.3390/s22155881
Reddy, G. P., Kumar, Y. V. P., Chakravarthi, M. K., & Flah, A. (2022). Refined Network Topology for Improved Reliability and Enhanced Dijkstra Algorithm for Optimal Path Selection during Link Failures in Cluster Microgrids. https://doi.org/10.3390/su141610367
Serban, I., Céspedes, S., Marinescu, C., Azurdia-Meza, C. A., Gómez, J., & Sáez Hueichapan, D. (2020). Communication Requirements in Microgrids: A Practical Survey. https://www.researchgate.net/publication/339566854_Communication_Requirements_in_Microgrids_A_Practical_Survey
Utkarsh, K., Srinivasan, D., Trivedi, A., Zhang, W., & Reindl, T. (2019). Distributed Model-Predictive Real-Time Optimal Operation of a Network of Smart Microgrids. https://doi.org/10.1109/TSG.2018.2810897
Vaishnav, V., Jain, A., & Sharma, D. (2023). Auxiliary Network-Enabled Attack Detection and Resilient Control of Islanded AC Microgrid. https://arxiv.org/abs/2401.00180
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2026 Abolfazl Taleghani (Author); Sepehr Soltani
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
