Hybrid chaotic ABC-CSO and GK-LIP techniques for smart grid competence and security

The integration of Internet of Things (IoT) technologies in the context of smart grid services holds the promise of transforming energy management and monitoring processes. However, this integration brings forth a myriad of challenges, including complexities in hardware integration, data accessibility, quality of service, security, and privacy concerns. In the proposed research, a HRES-based smart grid system is developed with Cloud IoT’s potential for enhancing power networks’ efficiency, emphasizing the need for tailored network utilization and high-quality service support based on user requirements. However, this advancement comes with increased privacy threats. To tackle these challenges, this research proposes a hybrid approach, amalgamating Chaotic Artificial Bee Colony and Chicken Swarm Optimization (ABC-CSO) algorithms to determine the shortest paths among nodes. Moreover, a robust encryption technique, group key-based Lagrange interpolation polynomial (GK-LIP) algorithm, has been devised to ensure secure data transfer to the cloud. The validation of the developed smart grid system is conducted using MATLAB, allowing for a thorough examination of its performance under various scenarios and conditions. The proposed hybrid chaotic ABC-CSO results in 97.21% accuracy in predicting the shortest path, with GK-LIP approach signifying reduced encryption and decryption time of 0.103 s and 0.012 s, in contrast to state-of-the-art approaches. Additionally, the system’s real-time status is assessed through the AdaFruit IoT webpage. This methodology not only enhances data security but also detects and prevents unauthorized access, marking a significant stride in safeguarding sensitive information in the framework of smart grid services.