Energy management of smart grid equipped by an Internet of Things (IoT) configuration: A DDFAT methodology

Abstract

In this manuscript, an Energy Management System (EMS) with Internet of Things (IoT) framework in distribution system (DS) based on hybrid technique is proposed. The proposed method is the combination of Dynamic Differential Annealed Optimization (DDAO) and Feedback Artificial Tree (FAT) algorithm; therefore called as DDFAT method. The main intention of proposed method is “to optimize the power control and DS resources by constantly tracking the data into communication framework based on IoT”. In this work, the DS is inter connected to the data acquisition method that is the Internet of things uses single IP address resulting in mesh wireless network devices. The internet of things based communication scheme used to facilitate the growth of demand response (DR) for energy management (EM) DS. The transmitting data is carried out by DDFAT method. In this way, the IoT distribution scheme increases the network flexibility and offers optimal utilization of the accessible resources. Moreover, the DDFAT method is reliable to meet global supply along energy demand. The DDFAT method is implemented in MATLAB Simulink platform under three test cases and its performance is analysed with the existing improved artificial bee colony (IABC), squirrel optimization with gravitational search–assisted neural network (SOGSNN), particle swarm optimization (PSO)–assisted artificial neural network (ANN), Fruit fly Optimization algorithm (FOA), and grasshopper optimization algorithm (GOAPSNN) methods.