a review of energy management of multi-microgrid power systems in the presence of uncertainty of distributed generation resources

Uncertainties in renewable generation and load demand are challenges to the efficient operation of renewable energy systems. this paper aims to review energy management systems for multi-microgrid networks under uncertainty. this review primarily focuses on uncertainty modeling approaches for multi-microgrid energy management, while also providing an integrated discussion on communication architectures, hierarchical control strategies, and market-oriented coordination. therefore, the paper serves as a hybrid survey—comprehensive in structure yet focused in its analytical depth on uncertainty and distributed optimization frameworks. it provides an overview of optimization techniques and control strategies to address issues related to uncertainty in renewable generation, load demand, and network dynamics. this paper is organized as follows: we first introduce the concept of multi-microgrid networks, discuss the relevance of efficient energy management systems in such systems, then we analyze the impact of uncertainty on the economic and environmental performance of multi-microgrid networks and present approaches to mitigate their effects. the novelty of this review is to provide an integrated conceptual framework of uncertainty modeling, distributed optimization and machine-learning-based forecasting, as well as a critical comparative synthesis of recent studies (2020–2024). specifically, 50 recent studies are analyzed, with probabilistic and robust optimization methods representing nearly 45% of the reviewed works, and machine-learning-based forecasting models accounting for 30%. comparative analysis reveals that hybrid ml–robust frameworks achieve the best trade-off between cost-efficiency and reliability, highlighting trends and gaps in current research.