A Comprehensive Review of Optimal Scheduling of Distributed Energy Resources with an IoT-Enabled Smart Energy Management Device

Abstract

The rapid growth of distributed energy resources (DERs), including solar photovoltaics, wind turbines, energy storage systems, and electric vehicles, has transformed modern power systems into decentralized and dynamic networks. Simultaneously, the integration of the Internet of Things (IoT) has enabled intelligent energy management systems capable of real-time monitoring, control, and optimization. This review examines recent advancements in optimal scheduling of DERs using IoT-enabled smart energy management devices, focusing on objectives such as cost reduction, emission minimization, load balancing, and grid stability. Traditional centralized scheduling approaches are often inadequate for handling the uncertainty and variability of renewable energy sources, leading to the adoption of advanced optimization techniques such as particle swarm optimization, genetic algorithms, ant colony optimization, and hybrid methods, along with machine learning-based predictive models. IoT devices facilitate seamless communication between distributed components, while edge computing enhances responsiveness and reduces latency. Applications across residential, commercial, industrial, and smart city environments demonstrate the effectiveness of these approaches. Despite significant progress, challenges such as scalability, interoperability, data security, and computational complexity remain, highlighting the need for further research toward efficient and sustainable smart energy systems.