Advances and Challenges in Distributed Model Predictive Control for Microgrids: A
Comprehensive Review and Future Directions
As renewable energy integration becomes increasingly prevalent, microgrids stand at the
forefront of decentralized energy management solutions. This presentation delves into
Distributed Model Predictive Control (DMPC), a highly promising control strategy that
optimizes microgrid operation by managing distributed generation and addressing real-time
fluctuations in energy supply and demand. DMPC’s ability to enhance the operational
efficiency of microgrids while handling uncertainties has garnered significant attention in
recent years.
This review offers a thorough overview of the current advancements in DMPC for
microgrids, focusing on its practical applications, including renewable energy integration,
demand response, and grid islanding. Key benefits, such as scalability and robustness against
system disturbances, are discussed, alongside the limitations, including communication
constraints, synchronization issues, and the challenge of managing large-scale microgrid
networks. Furthermore, the presentation highlights innovative approaches such as consensus
algorithms, dual decomposition, and decentralized optimization methods designed to address
these challenges.
Looking ahead, the discussion extends to emerging research directions, including data-driven
methods, the integration of machine learning and artificial intelligence into DMPC
frameworks, and the role of advanced energy storage systems in further enhancing microgrid
efficiency. Finally, the presentation will suggest future research directions, aiming to propel
the field forward through the development of more scalable, resilient, and economically
viable DMPC strategies.
This comprehensive review serves as a guide for practitioners and researchers interested in
the future of DMPC, emphasizing both the technological advancements and the practical
barriers that need to be addressed for widespread adoption in modern power systems.
