## 3 Microgrid Sizing Approaches
### Heuristic Approaches  
In the domain of heuristic optimization for microgrid configurations, several studies have employed a variety of algorithms to optimize their design and operational efficiencies. For instance, the work in [41] demonstrated the use of GA to size both grid-connected and isolated microgrids, aiming to optimize the LCOE with configurations including PV panels and BESS. In a similar study presented in [55], GA was applied to optimize a microgrid comprising PV, WT, BESS, and loads. The primary objectives of this optimization were to minimize greenhouse gas emissions, reduce the life cycle cost (LCC), and decrease non-renewable energy consumption. Further research [56] explored multiple microgrid configurations to find a balance between factors such as greenhouse gas emissions versus global cost and microgrid autonomy versus global cost. The study in [57] targeted the sizing of an autonomous AC microgrid to minimize energy and installation costs while enhancing reliability by reducing the LPSP. In addition, ref. [58] aimed at reducing energy and installation costs and maximizing reliability through GA for determining the capacity of BESS, PV, and other components. Lastly, the research presented in [59] discussed integrating life cycle analysis into the hybrid microgrid design process to optimize design and minimize environmental impact while ensuring technical and economic feasibility. The study in [39] critically evaluated a range of metaheuristic algorithms for reducing the NPV of a microgrid system that incorporates residential, commercial, and EV loads, in addition to PV panels, WTs, and BESS. Among these algorithms, the Meth-Flame optimization algorithm stood out for its superior performance, while the Equilibrium optimizer was noted for its relatively lower efficiency. Another study [60] applied the Multi-Objective PSO technique to size the microgrid to minimize the dependency on non-renewable energy imports and the system's annualized cost. The outcomes were illustrated via a Pareto front by highlighting the trade-offs between the two objectives. Furthermore, in [61], a two-step methodology was employed for the optimization and analysis of a standalone hybrid system consisting of PV, WT, BESS, and a diesel generator, which was specifically designed to fulfill the electricity demands of a remote village, Fanisau, in northern Nigeria. This approach, executed in the MATLAB (Version 9.3) environment using a GA solver, aimed to efficiently and economically cater to the unique energy needs of the region. The optimized hybrid renewable energy system proposed for Fanisau included 273 PV modules, 148 batteries, and a 100.31-W diesel generator, capable of producing 200,792 kWh annually at a total annualized cost of $43,807 USD with a cost of energy $0.25 USD/kWh. In [62], the study applies the Non-Dominated Sorting GA II (NSGA-II) for microgrid sizing, aiming to minimize annualized costs, emissions, and energy imports from the electrical grid. The study elucidates optimal configurations via the Pareto front and employs the Topsis method for decision analysis. The study in [63] outlines the development of a hybrid renewable energy system optimization using the PSO algorithm-based Monte Carlo simulation to minimize total annual costs, accounting for resource and load uncertainties. It focuses on microgrid components such as WT, PV panels, and BESS. The findings demonstrate the algorithm's efficiency in enhancing system reliability and cost-effectiveness for off-grid energy solutions. In [64], the grasshopper optimization algorithm (GOA) is used to optimize an autonomous microgrid comprising PV panels, WT, BESS, and a diesel generator. The objective function minimizes the LPSP and the LCOE. The main numerical results demonstrate the GOA efficiency over PSO and Cuckoo Search algorithms. GOA achieves a reduction in system capital cost by 14% and 19.3%, respectively. The optimal configuration includes 26 PV panels, 4 WTs, and a 40 kW BESS.