IoT-based optimal demand side management and control scheme for smart microgrid

Renewable energy resources (RESs) are highly speared to cover colossal electricity demand. Smart microgrids (SMGs) are engaged with demand-side management (DSM) to save more energy and maximize energy efficiency. Voltage and frequency regulations, CO2 emission, peak-to-average ratio (PAR), RES stochastic nature, and load dynamics are still considered the most SMG challenges. New control and management approaches are needed to overcome these challenges. Internet of Things (IoT) is accomplished to provide adaptive monitoring of energy consumption and ensure an economical and secure operation of the SMG. This paper proposes an advanced DSM and control strategy for an efficient energy management system (EMS) in SMG. An optimal cost-effective EMS operation is firstly introduced based on a two-level genetic algorithm (GA) optimization problem and augmented with the time-of-use pricing (ToU) principle. Secondly, the SMG voltage and frequency are optimally regulated using an improved PID-based mixed sensitivity H-infinity (PID-MSH8) control scheme while operating in islanded mode. The proposed DSM and control strategy harness the immense IoT aptitudes to ensure an economic and secure operation of the SMG. Finally, a SMG lab-scale prototype is experimentally implemented to realize and validate the proposed DSM and control strategy. The experimental results prove the efficacy of the proposed EMS and the effectiveness of the SMG operation. The results confirm the proposed scheme's capability to get an optimal DSM scheme with reducing the SMG energy cost, emission cost, and PAR. The proposed control scheme accomplishes the SMG voltage and frequency regulations in adherence to IEEE Standard-1547 and provides customers' quality of service while customizing voltage for regulating DSM.

Automated summary

Renewable energy resources are utilized to meet huge electricity demand, with smart microgrids implementing demand-side management for energy savings and efficiency maximization. However, challenges such as voltage and frequency regulations, CO2 emissions, and stochastic nature of renewable energy sources still persist. Innovative control strategies are required to address these challenges, with Internet of Things technology providing adaptive monitoring for economic and secure microgrid operation. A proposed advanced demand-side management and control strategy, incorporating genetic algorithms and PID-based control schemes, aims to optimize energy management and regulate voltage and frequency efficiently in a microgrid setting. Experimentation with a lab-scale prototype demonstrates the effectiveness of the proposed strategy in reducing energy costs, emissions, and peak-to-average ratio while ensuring quality of service for customers.