A new Internet of Things based optimization scheme of residential demand side management system

Abstract

The steady increase in the energy demand and the growing carbon footprint has forced electricity-based utilities to shift from their use of non-renewable energy sources to renewable energy sources. Furthermore, there has been an increase in the integration of renewable energy sources in the electric grid. Hence, one needs to manage the energy consumption needs of the consumers, more effectively. Consumers can connect all the devices and houses to the internet by using Internet of Things (IoT) technology. In this study, the researchers have developed and proposed a novel 2-stage hybrid method that schedules the power consumption of the houses possessing a distributed energy generation and storage system. Stage 1 modeled the non-identical Home Energy Management Systems (HEMSs) that can contain the DGS like WT and PV. The HEMS organise the controllable appliances after taking into consideration the user preferences, electricity prices and the amount of energy produced /stored. The set of optimal consumption schedules for every HEMS was estimated using a BPSO and BSA. On the other hand, Stage 2 includes a Multi-Agent-System (MAS) based on the IoT. The system comprises two portions: software and hardware. The hardware comprises the Base Station Unit (BSU) and many Terminal Units (TUs).

A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

“…The suggested algorithm arranges devices to bring the load curve of the consumption schedule closer to the load curve of the desired load curve [35][36][37][38]:…”

“…The off-peak power limit (W L 1 ) and on-peak power limit W L 2 are calculated so that fair load distribution at the customer's end can be realized via a house load powermanagement scheduler [35][36][37][38].…”

“…The data are transmitted between neighbors using the IoT platform, and then processed in a cloud computing layer. The proposed IoE communications network is made up of five layers: the agent layer, the IoT layer, the network layer, and the layer of processing data [38,[41][42][43][44].…”

“…CONNECT, CONNAC, PUBLISH, PUBACK, SUBSCRIBE, SUBACK, and other control packets communicate information about the quality of service (QoS), topic, and payload of the transmission. Figure 6b depicts the MQTT communication components [38].…”

“…where C E is cost of energy at time T. P grid (t) is grid power, ∆t denotes interval time between two time instants, and τ(t) is the price of energy at time instant t [38,41].…”