## 1 Introduction
Studies also recognized the relevance of hybrid AC/DC microgrid configurations, which facilitate the integration of diverse energy sources and storage technologies, offering greater operational flexibility and improved fault tolerance [4; 16; 17]. Previous reviews, such as [PERSON] et al., have addressed smart microgrid configurations with storage and renewable generation, providing broad insights into system architectures. In contrast, this study adopts a structured methodology to synthesize the latest advances in intelligent control, predictive analytics, and optimization technologies [18]. Progress in energy storage technologies has further strengthened microgrid resilience and performance. While BESSs remain widely adopted due to their fast response times and modular design, their economic competitiveness is constrained by high initial costs and limited service life [5; 12; 14]. To address long-duration storage requirements and seasonal variability, hydrogen-based storage solutions and pumped hydro storage (PHS) have been explored, each with distinct advantages and limitations [6; 19; 20; 21]. Hybrid storage systems that combine BESS with hydrogen and PHS technologies have gained attention for their capacity to handle both short-term fluctuations and long-term balancing needs [7; 22; 23]. With the rapid evolution of digital technologies, AI, machine learning (ML), and digital twins have become central to microgrid control and optimization. AI-based forecasting methods, including deep learning and reinforcement learning algorithms, have demonstrated superior predictive accuracy for renewable generation and demand, enabling more precise energy dispatch [8; 24; 25; 26]. Digital twins offer real-time virtual representations of physical systems, facilitating advanced scenario analysis, predictive maintenance, and resilience evaluations under diverse operational conditions [9; 27; 28; 29]. The growing reliance on cloud-based platforms for microgrid management has also brought cybersecurity and data privacy to the forefront of current research. Several studies have raised concerns regarding system vulnerabilities to cyberattacks, underscoring the need for multi-layered security frameworks and standardized communication protocols [10; 30; 31]. Blockchain technologies are being examined as decentralized solutions for secure energy transactions and peer-to-peer trading platforms, providing integrity and transparency without centralized intermediaries [11; 23; 32]. The integration of AI-driven threat detection systems alongside blockchain platforms is viewed as a key approach to enhancing the security and resilience of future microgrids [33; 34]. Alongside these technical developments, recent studies have explored the economic and financial aspects of microgrid deployment. The high capital requirements associated with advanced control platforms, energy storage, and renewable integration have led to the examination of alternative business models and financing schemes, including Energy-as-a-Service (EaaS), green bonds, and public-private partnerships [35; 36; 37]. Proposals to incorporate resilience metrics into financial assessments aim to quantify the long-term benefits of microgrid investments, particularly in the context of growing climate and operational risks [38; 39]. These strategies are essential to support the scalability and long-term viability of microgrid projects, particularly in remote and underserved areas. Despite these advancements, important research gaps remain, particularly regarding interoperability frameworks and the development of unified control architectures capable of integrating diverse technologies from multiple vendors [22; 40]. Although explainable AI (XAI) and federated learning offer potential for enhancing transparency and privacy in AI-driven control systems, their practical implementation in microgrid environments is still limited and requires further investigation [41; 42].