Hybrid Modeling Approaches in Energy Internet: Bridging Cyber, Physical, and Social Realms

Effective modeling approaches are required for the management and operation of the Energy Internet due to the system's complexity and mobility. New hybrid models that link the Energy Internet's digital, real-world, and social components are the primary focus of this article. We propose a novel approach that integrates adaptive reinforcement learning, social behavior analysis, and multi-agent coordination optimization to enhance the system's performance, robustness, and long-term sustainability. We intend to address issues that arise when several factors, such as evolving technology, user behavior, and social interactions, converge by employing these mixed modeling approaches. Because of this, the Energy Internet setting will run more efficiently and effectively. Through extensive simulations and case studies, we demonstrate that our proposed method outperforms conventional ones. Our main focus is on how technology can enhance energy management, better allocate resources, and back user-centered and socially inclusive policies. To ensure the seamless operation of the Energy Internet environment's cyber, physical, and social components, the results highlight the significance of mixed modeling methodologies.