## 1 Introduction  
The global energy sector is undergoing profound changes driven by the urgent need to achieve carbon neutrality, improve energy security, and support sustainable development. In this context, smart microgrids have become a foundational element for future power systems, enabling the efficient integration of distributed energy resources (DERs) and renewable energy sources (RES) while strengthening system resilience and operational flexibility [1, 2]. These localized energy systems support optimized management through advanced control methodologies and real-time decision-making platforms. The growing penetration of variable RES, particularly solar and wind, has increased the challenges related to intermittency and uncertainty, especially in isolated and weakly interconnected grids [3]. Recent developments emphasize the deployment of hybrid energy storage systems, combining battery energy storage systems (BESSs) with hydrogen-based solutions, to meet both short- and long-term storage needs, contributing to improved grid stability and economic performance [4; 5]. In parallel, the application of artificial intelligence (AI) and digital twin technologies is transforming the operational management of smart microgrids. AI-based models have shown high accuracy in forecasting renewable generation and load demand, optimizing energy dispatch strategies, and facilitating predictive maintenance [6; 7]. Additionally, digital twins enable real-time simulation and validation of control strategies, allowing informed decisions under dynamic operating conditions [8]. However, unresolved challenges remain concerning the standardization of communication protocols, the development of interoperable control architectures, and the implementation of effective cybersecurity strategies. As microgrids increasingly depend on cloud-based management platforms, ensuring data privacy and protection against cyber threats becomes a critical concern [9; 10]. Addressing these aspects is essential to encourage investment and support the global adoption of resilient, efficient, and economically sustainable microgrid solutions. To systematically analyze these technological trends and research gaps, this study applies the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, ensuring a transparent and rigorous review process [11]. The aim is to consolidate the latest developments in smart microgrid management, focusing on energy storage technologies, AI-driven control strategies, and secure communication frameworks. This review offers actionable insights for researchers, industry professionals, and policymakers committed to advancing sustainable and intelligent energy systems. The energy transition has driven a substantial rethinking of power system design and management, motivated by the need to reduce greenhouse gas emissions and improve energy security. Early studies primarily addressed the operational challenges associated with large-scale RES integration into conventional centralized grids, highlighting the volatility introduced by variable generation profiles and its implications for grid stability and reliability [1; 12]. These investigations emphasized the importance of flexibility mechanisms and advanced forecasting techniques to maintain operational balance amid growing uncertainty. Subsequent research turned toward DERs and microgrid architectures as scalable approaches to mitigate these challenges [2; 13]. In particular, the adoption of intelligent energy management systems (EMSs) enabled more dynamic and autonomous operation of decentralized grids. The integration of real-time monitoring platforms, including Supervisory Control and Data Acquisition (SCADA) and Advanced Metering Infrastructure (AMI), provided improved visibility and control over distributed assets, supporting the deployment of advanced demand response and optimization strategies [3; 14; 15].