## 1 Introduction
Studies into the field of microgrid sizing have been reported in the literature, which can be categorized into three groups: software tools (such as HOMER, RETScreen, HOGA, and IHOGA) [5]; deterministic methods (including iterations, numerical, linear programming, graphical construction, probabilistic and analytical); and metaheuristic algorithms (e.g., GA, PSO, GOA, and GWO) [4]. Of the three, software tools are user-friendly but limited in their capacity to pick components. Deterministic methods work efficiently but may be stuck at local optima. Several times, global best optimal solutions may be unable to be reached [2]. Over the past ten years, scientists have developed and used various metaheuristic algorithms for the purpose of microgrid sizing. GA, PSO, and DE, for example, are popular methods among computer scientists and experts from various fields, as they are flexible, offer a solution that surpasses that of deterministic algorithms, and avoid local optima traps [4]. Despite the fact that a particular metaheuristic algorithm can yield optimal results for one specific objective function, the same algorithm will create substandard results for other objectives based on the no free lunch theorem [6]. Motivated by the aforementioned factors, scientists have been exploring novel algorithms to address microgrid sizing problems [4]. In remote communities, poor load demand coordination and irregular renewables can lead to higher energy costs. To address this issue, the power system industry has implemented various demand-side management (DSM) strategies such as \"load shaping\", \"load shifting\", \"energy conservation\", \"load building\", and \"peak clipping\" (see Figure 1). These strategies can help to reduce energy costs by improving the coordination of load demand and incorporating renewable energy sources more effectively [7] as follows:  
1. **Peak clipping:** Its purpose is to reduce the strain on energy systems during peak demand periods. This is typically achieved by limiting the use of appliances during peak hours or by offering customers incentives to shift their energy consumption away from peak times;
2. **Valley filling:** The aim of valley filling is to maximize the utilization of facilities by promoting energy consumption during off-peak hours. To achieve this, governments can incentivize customers to schedule such activities as charging and loading during off-peak times. By doing this, customers will be able to take full advantage of available resources, resulting in more efficient use of energy;
3. **Load shifting:** To reduce overall energy consumption, load shifting from peak to off-peak times is recommended. Customers can store thermal heat during the off-peak hours, which can then be used to maintain a comfortable temperature throughout the day. Furthermore, household chores, such as laundry and dishwashing, can be completed at night to avoid peak loading;
4. **Energy conservation:** Its goal is to reduce energy consumption by investing in more energy-efficient devices. This can help lower load demand, thus leading to more efficient energy use overall;
5. **Load building:** Smart grids enable the use of load building and flexible loads through the improved network connection. This, in turn, increases the grid's responsiveness through improved load sharing and the application of energy storage systems;
6. **Flexible load shape:** By adjusting the flexible load demand, the reliability conditions necessary for system reliability can be determined, thus improving the system's reliability. In rural areas, the load consumption pattern of most people remains fairly consistent throughout the day. Since priority loads, such as lamps, fans, and televisions, are usually used in the evening, and their usage cannot be altered, rural off-grid villagers cannot participate in peak clipping or load shifting at this time. However, Energy-Conservation-based Demand Management can still help reduce peak loads without resorting to peak clipping or load shifting. Figure 1: \(Cont\).