A new internet of things enabled trust distributed demand side management system

Abstract

Renewable energy resources (RESs) have come under significant focus to cover the massive demand for electricity. Microgrids are coupled with Demand Side Management (DSM) for saving energy and amplifying energy efficacy. CO2 discharges, frequency and voltage protocols, RES stochastic nature, Peak to Average Ratio (PAR), and load dynamics are still termed as the most significant challenges for smart microgrids. Novel regulation and management methodologies are required for overpowering them. Internet of Things (IoT) is able to offer adaptive supervision of energy intake and warrant a cost-effective and safe functioning of the smart microgrids. In this study, a new DSM system called Real-Time Electricity Scheduling (RTES) for domestic home energy management is introduced for running the microgrids. The proposed management system seeks to curtail cost payments by optimally programming smart devices and enhancing the consumption of energy. The system comprises two portions: software and hardware. The hardware comprises a Base Station Unit (BSU) and many Terminal Unit (TU). The software comprises the Wi-Fi network programming along with the system protocol. In this paper, Message Queue Telemetry Transportation (MQTT) broker was constructed on TU board and BSU. The proposed technique raises effectual use of energy, thus raising the viability of IoT empowered households in smart cities. The approach impulsively reacts to protective home equipment, power factor correction, and price-centred demand response programmes to contest the key concern of the demand response programs, i.e., constraints in terms of customer’s knowledge to react upon obtaining demand response signals. Lastly, a smart microgrids model is experimentally employed to accomplish and corroborate the proposed DSM and control approach. The outcomes of the experiment have substantiated the efficacy and viability of the presented approach and the competence of proposed technique for energy management functioning in various modes. The outcomes endorse the proposed method’s ability to obtain an optimum DSM scheme by decreasing the smart microgrids’ emission cost, energy cost, and PAR, along with power factor correction.