Distributed Model Predictive Control of Microgrids: A Review on Recent Developments

The increasing penetration of distributed renewable energy resources and the growing complexity of modern power systems have driven significant advances in microgrid control. Among various control paradigms, distributed model predictive control (DMPC) has emerged as a promising framework to achieve optimal, scalable, and resilient microgrid operation. This review synthesizes peer‐reviewed studies that explicitly implement DMPC in microgrid settings. Interestingly, most of these publications (around 70%) appeared within the last 5 years, underscoring the rapid growth and increasing research attention in this domain. Building on this body of work, this paper provides a comprehensive review of recent developments in this field, covering fundamental concepts, architectural frameworks, communication topologies, and coordination algorithms. The survey systematically categorizes DMPC implementations by structural type, analyzes their ability to address key control objectives, such as voltage and frequency regulation, power sharing, and economic dispatch, and highlights how practical communication constraints are managed. In addition, the paper reviews the state of stability analysis, validation methodologies, and algorithmic trends observed in the literature. Based on this analysis, open research challenges and future directions are identified, including the integration of learning‐based models, enhancement of cybersecurity and privacy preservation, improvement of scalability, and advancement of multi‐microgrid coordination. This review aims to support both academic research and practical deployment of DMPC as a key enabler for future smart and flexible energy systems.