## 1 Introduction  
Microgrids (MGs) are distributed energy systems that can operate autonomously or be interconnected to the primary power grid, efficiently managing energy generation, storage, and consumption within a defined electrical community [1, 2]. These local grids could integrate diverse distributed energy resources (DER), including photovoltaic (PV) systems, wind turbines (WTs), and small hydroelectric power plants. Integrating renewable energy sources is a crucial feature of microgrids, introducing specific challenges due to the intermittency of these sources [3, 4]. A microgrid design typically considers minimizing the loss of power supply probability (LPSP) [5]. Furthermore, microgrids could integrate load-shedding schemes, where non-critical loads can be selectively disconnected when the demand exceeds the supply capacity [6, 7]. Energy management is crucial in microgrid operation to meet energy demands appropriately. It refers to controlling and optimizing energy generation, storage, and consumption to meet the community's needs. Therefore, detailed and focused energy management, coupled with an adequate energy storage system (ESS), is critical to the successful operation of microgrids, especially in non-interconnected regions where reliability and autonomy are critical. Given the complexity and importance of these systems, it is essential to pay close attention to the design and operation of a microgrid. One of the primary stages in this process is energy planning, which includes selecting energy sources and sizing the sources chosen as a core step [8]. It also involves strategically configuring the microgrid components and balancing energy production and consumption to minimize initial investments, operating costs, maintenance, and replacement costs [9]. Microgrid energy planning has gained considerable attention in the scientific community and among power system stakeholders due to its potential beneficial impacts on efficiency, emissions, and carbon footprint reduction. It is a crucial element in the design of sustainable energy infrastructures [10]. Numerous strategies have been developed for microgrid energy planning, which is helpful but also needs to be clarified, as the variety of methods makes it difficult to determine a suitable method for each situation. The choice of energy planning methodology depends mainly on the design objectives and the specific needs being addressed. Complex methodologies could consider much information to provide exact results for particular cases. For example, Figure 1 presents a breakdown of the data used in [11] for the energy planning of a microgrid, where a detailed scan of a remote rural region was performed. The region was divided into small subareas; each matrix position represented the subarea's latitude and longitude location. The hypermatrix was composed of layers recording the characteristics of each point, such as the location of houses, terrain altitude, solar potential, wind potential, hydraulic potential, and ecologically protected zones. This information matrix was used to develop the energy planning of an isolated microgrid to power the houses enclosed in the study region. This type of study typically generates high-accuracy results. However, they entail a high degree complexity and require a lot of resources to survey the data and reach an optimal solution. The energy planning of a microgrid generally involves these steps: (i) the selection of energy sources, (ii) the sizing of these sources, and (iii) the definition of the energy management strategy. The level of detail in each phase might vary depending on the design objective [12]. Researchers have developed methodologies that combine these steps in different ways, either sequentially, simultaneously, or in a hybrid approach. Figure 2 shows the basic steps of microgrid energy planning and the order in which they could be addressed sequentially or simultaneously. Given the importance of guiding energy planning methodologies, several researchers have conducted reviews in this area.