Role of net-zero renewable-based transportation systems in smart cities toward enhancing cultural diversity: Realistic model in digital twin

This paper introduces an advanced framework for energy and data transactions, aiming to optimize the sustainable management in smart cities. The prominence of smart cities in power systems has grown recently, offering comprehensive monitoring and accessibility. However, this advancement also brings about increased susceptibility to cyber-attacks. To address this concern, we present an integrated smart city encompassing the smart grid (SG), transportation comprising subway and vehicles, microgrid (MG), and energy hub (EH). Within this proposed model, we employ an enhanced intrusion detection system (IDS) utilizing Generative Adversarial Networks (GAN) through deep learning to fortify the safety of information transactions inside the smart city. This involves incorporating a security layer into the smart city design, effectively thwarting cyber hackers' attempts to access system information. This paper develops an efficient energy management schedule. To achieve this, we introduce a novel optimization method deploying the honey bee mating optimization technique for the optimum allocation of charging stations. The grid randomness is addressed using the point estimate method (PEM) with a 2 m-scheme. The validity and effectiveness of the proposed framework are proved, showcasing its ability to enhance the security and efficiency of smart city operations. This research advocates for the application of a Cyber-Physical Digital Twin to explore optimal energy management within a Smart City while ensuring a safe and secure framework. The proposed methodology involves the creation of a comprehensive digital replica incorporating real-time data, cybersecurity measures, optimization algorithms, and machine learning. This digital twin serves as a dynamic simulation environment, enabling stakeholders to analyze, simulate, and refine energy management strategies in a risk-free setting. The study emphasizes the importance of continuous improvement, iterative testing, and adaptability to real-world data for ensuring the relevance and effectiveness of the digital twin in guiding Smart Cities toward efficient, secure, and resilient energy management.