## 1 Introduction
One aspect that has attracted the most attention is the definition of an energy management strategy (EMS), whether at the design or microgrid operation stage. For example, [PERSON] et al. [13] developed a comprehensive review on energy management in microgrids with ESS. This review discusses recent advances in microgrid architecture, energy management models, solution methodologies, and future research directions. The microgrid architecture is classified into alternating current (AC), direct current (DC), hybrid AC/DC, and multi-energy microgrids. Each type is analyzed in terms of efficiency, flexibility, and resilience and their ability to integrate ESS and renewable sources. This research also evaluates different approaches to energy management in microgrids, including mathematical programming, adaptive dynamic programming, and deep reinforcement learning. It covers a wide range of ESS technologies, highlighting their crucial role in the reliable and stable operation of microgrids in grid-connected and  
Figure 1: Overview of information processing in [11] for accurate energy planning of an isolated rural microgrid. (**a**) Division of the study region into subareas; (**b**) Layers recording the characteristics of the subareas. islanded modes. It identifies gaps in problem formulation, especially in low carbon and resilience management applications, and suggests the need for more efficient computational solutions considering information security. It also highlights the need for further resilience management and information security research. In the same regard, [PERSON] [14] conducted a review of surveys of microgrid EMS. This review synthesized previous studies, providing an overview of the advances and challenges in microgrid energy management and examining different structures and schemes, including centralized, decentralized, and distributed approaches. It also reviewed optimization techniques used in energy management, such as mathematical programming, heuristic methods, and machine learning models. It also focused on the challenges of integrating renewable energy, cyber security, and microgrid control strategies. This review guides researchers interested in energy management in microgrids, covering aspects of power flow optimization, reliability assessment, and the application of advanced control techniques. Meanwhile, [PERSON] et al. [15] focused on analyzing the state-of-the-art of EMS for microgrids. This review covers a comprehensive analysis of EMS, applied optimization methods, and implementation challenges associated with microgrids in grid-connected systems. It discusses management strategies for a microgrid's main components, including charging, generation, and ESS. It reviews optimization approaches, such as classical, meta-heuristic, and artificial intelligence-based methods, to improve the operational efficiency of microgrids and reduce costs. This research also addresses the technical, social, economic, and regulatory challenges facing microgrids, highlighting the need for robust regulatory frameworks and policies that facilitate the effective integration of microgrids and deepen the operational efficiency, integration of renewable sources, and resilience of microgrids in future scenarios. [PERSON] et al. [2] conducted a comprehensive review on the management of microgrid components, network structures, operation modes, ESS, load types, problem modeling, resolution procedures, and other relevant aspects from the perspective of microgrid management. They highlighted the importance of optimized energy management and control methods for microgrid operation, addressing energy balance, cost optimization, and energy loss reduction. The review also analyzed the effect on the operation of generators, ESS, and loads, and addressed structural configurations of microgrids, such as AC, DC, and hybrid networks. It explored advanced mathematical modeling techniques  
Figure 2: Basic steps in the energy planning of a microgrid.