IoT-enabled microgrids for energy management and demand response: A review and future trends

The rapid advancement of digital energy infrastructures has accelerated the transition toward internet of things (IoT)-enabled microgrids capable of autonomous monitoring, coordination, and optimization of distributed resources. By integrating sensing, communication, and intelligent control, microgrids can enhance local energy management, support demand-side flexibility, and interact effectively with higher-level grid operations. This paper presents a comprehensive review of recent developments in IoT-assisted energy management strategies within microgrids, with particular emphasis on demand response (DR) mechanisms and price-aware operational scheduling. The text looks at various applications within residential sectors, such as the coordination of smart appliances, distributed energy generation, and the integration of electric vehicles (EVs). It emphasizes the role that bidirectional charging technologies, including vehicle-to-grid (V2G) and grid-to-vehicle (G2V), play in promoting cost-effective and reliable operations. Existing studies have extensively investigated IoT-integrated DR through real-time load monitoring, automated appliance scheduling, dynamic pricing, EV coordination, and optimization-based energy management. However, the practical deployment of these approaches remains constrained by heterogeneous communication protocols and devices, the secure collection and processing of large volumes of real-time data, and the computational and operational complexity associated with coordinating multiple distributed resources. The review assesses communication architectures, optimization methods, and control platforms that support scalable and interoperable microgrid operations. It discusses key challenges related to interoperability, data management, and system complexity. Additionally, it explores emerging trends such as AI-driven decision-making, decentralized control paradigms, and adaptive IoT frameworks, which are promising directions for future research and implementation.