Recent Advances in Optimal Scheduling of Distributed Energy Resources with an IoT-Enabled Smart Energy Management Device: A Systematic Review

Abstract

The transformation of modern power systems into decentralized and intelligent energy networks has increased the need for efficient coordination of distributed energy resources (DERs) such as solar, wind, storage systems, and controllable loads. However, the intermittent nature of renewable generation and dynamic demand patterns make optimal scheduling a complex challenge that traditional centralized methods struggle to address. This review examines recent advances in optimal scheduling techniques within IoT-enabled smart energy management systems, covering mathematical optimization methods like mixed-integer and nonlinear programming, metaheuristic approaches such as particle swarm and genetic algorithms, and emerging machine learning models including reinforcement learning and neural networks for predictive decision-making. IoT plays a crucial role by enabling real-time data collection through smart sensors, facilitating communication, and supporting adaptive control strategies. Integration with edge and cloud computing further enhances system scalability and responsiveness. Applications across residential, commercial, and industrial energy systems demonstrate improvements in cost reduction, peak load management, energy efficiency, and emission control. The review highlights a growing trend toward hybrid optimization techniques that combine classical and AI-based methods to handle system complexity more effectively. Despite these advancements, challenges related to cybersecurity, data privacy, interoperability, and computational demands remain significant. Overall, the integration of advanced optimization with IoT technologies is essential for developing efficient, flexible, and sustainable next-generation energy management systems.