Hybrid-Oriented Intelligent Operational and Architectural Foundations of IoT-Enabled Smart Grids: A System-Level Review and Challenge-Oriented Comparative Synthesis
The rapid digitalization of energy systems and the increasing integration of distributed energy re-sources, renewable energy technologies, and prosumer-oriented infrastructures have accelerated the development of IoT-enabled Smart Grids as a foundation for intelligent and adaptive energy management. Modern Smart Grids increasingly depend on the coordinated interaction of IoT ar-chitectures, artificial intelligence, distributed analytics, and decentralized control mechanisms to ensure reliability, scalability, and real-time operational flexibility. Despite extensive research activ-ity, existing studies remain predominantly technology-centric, focusing on isolated architectural layers or individual intelligent methods without providing a unified system-level perspective on their coordinated operation and interoperability. This article presents a system-level integrative review and challenge-oriented comparative synthesis of intelligent operational and architectural foundations of IoT-enabled Smart Grids. The study analyzes data-driven, model-driven, knowledge-driven, agent-based, and hybrid-oriented intelligent paradigms within multi-layer IoT energy infrastructures. In addition, the research establishes a cross-layer mapping between Smart Grid operational challenges, enabling technologies, and corresponding analytical approaches while identifying interoperability constraints, scalability limitations, and coordination challenges associ-ated with decentralized energy ecosystems. The conducted synthesis demonstrates that hy-brid-oriented intelligent approaches represent the most promising direction for future Smart Grid evolution due to their ability to integrate AI, ML, digital twins, semantic reasoning, and decen-tralized multi-agent coordination within unified IoT architectures. The presented results provide a conceptual foundation for the prospective development of adaptive, interoperable, scalable, and explainable Smart Grid ecosystems integrating decentralized computing, distributed energy re-source coordination, vehicle-to-grid interaction, and intelligent cyber–physical orchestration.
