Secure Multi-Zone Optimization of Demand Response in 5G-Enabled Smart Grids with Trust-Aware Communication
Sajjad Rabbani1, Rao Muhammad Asif1, Mai Alduailij2, Adnan Yousaf1,*, Upinder Kaur3, Salil Bharany4, Ateeq Ur Rehman5,*
1 Department of Electrical Engineering, Superior University, Lahore, Pakistan
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia
3 Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India
4 Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, India
5 School of Computing, Gachon University, Seongnam-si, Republic of Korea
* Corresponding Author: Adnan Yousaf. Email: 
; Ateeq Ur Rehman. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.086500
Received 01 June 2026; Accepted 03 August 2026; Published online 20 September 2026
Abstract
The implementation of 5G communication in smart grids facilitates real-time demand response (DR) management, but at the same time creates several problems concerning cyber security and communication reliability, as well as user trust. Currently, DR methods mainly aim at energy optimization and neglect the combined effect of communication performance, cyber threats, and trust-aware participation. In this work, a secure multi-zone optimization model for a 5G-enabled smart grid is proposed that jointly optimizes communication efficiency, cybersecurity mechanisms and DR control within a single framework. The system is divided into five operational zones: Utility layer, Secure 5G core and edge, Access and gateway layer, Local control layer, Consumer layer. The communication- and security-aware optimization formulation is defined to optimize the latency, packet error rate, security overhead and DR shortfall, while maximizing reliability, fairness, resilience and secure user participation. Besides, a trust-aware iterative optimization algorithm is proposed to adaptively align the authentication, intrusion detection, clustering, secure communication and DR activation under different network conditions and attacks. The simulation results illustrate that the proposed approach significantly enhances the performance of the system, with low latency (≈8 ms), a low packet error rate (≈0.03), high signal quality, and high DR participation in comparison with the benchmark approaches. The framework is also capable of supporting effective operation under cyberattack scenarios and in the presence of different levels of trust, making it a very effective framework for secure, reliable and scalable DR management in next-generation smart grids.
Keywords
Demand response optimization; cybersecurity-aware optimization; trust-aware communication; cyber-physical energy systems
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