Review on recent control system strategies in Microgrid - Almihat, Mohamed G Moh; Munda, Josiah L. (Edelweiss Applied Science and Technology, 2024)
Microgrids (MGs) are integral to the evolving global energy landscape, facilitating the integration of renewable energy sources such as solar and wind while enhancing grid stability and resilience. This review presents a comprehensive analysis of control strategies in MG systems, addressing both conventional and advanced methodologies. We explore traditional control methods, such as droop control and Proportional Integral Derivative (PID) controllers, for their simplicity and scalability, but acknowledge their limitations in handling non-linearities and real-time adaptation. Model Predictive Control (MPC), Adaptive Sliding Mode Control (ASMC), and Artificial Neural Networks (ANN) are some of the more advanced techniques that make systems more flexible, better at managing energy, and stable even when operations change quickly. The review further delves into the role of the Internet of Things (IoT), predictive analytics, and real-time monitoring technologies in MGs, emphasizing their importance in enhancing energy efficiency, ensuring real-time control, and improving system security. The review places emphasis on energy management systems (EMS), which optimize supply and demand balance, reduce uncertainty, and enable seamless integration of distributed energy resources (DERs). The paper also highlights emerging trends such as blockchain, AI-driven controls, and deep learning for MG optimization, security, and scalability. Concluding with future research directions, the paper underscores the need for more robust control frameworks, advanced storage technologies, and enhanced cybersecurity measures, ensuring that MGs continue to play a pivotal role in the transition to a decentralized, low-carbon energy future.