Optimizing the sizing of residential microgrids using a genetic algorithm as a decision support model

Purpose: This study evaluates the performance of genetic algorithms (GAs) in optimizing the sizing of wind photovoltaic systems with battery energy storage systems (BESS). The objective is to determine whether GAs can balance generation and storage, improve system autonomy, reduce operating costs and meet residential energy demand in real-world environments. Design/methodology/approach: A genetic algorithm was used as a multi-objective optimization tool to determine the optimal sizing of solar panels, wind turbines and BESS. The model considers energy demand, climate variability and resource intermittency. Metrics such as autonomy, total costs and total energy deficit (TED) were evaluated. The results were compared with the exhaustive search to validate the effectiveness of the GA. Findings: The GA identified an optimal configuration with a TED of 3.76%, an autonomy of 96.24% and an efficiency of 95% and a total cost of USD 7,500. In contrast, the exhaustive search achieved a TED of 4.3%, a range of 95.7% and an efficiency of 90% at a cost of USD 8,000. Although both methods ensure optimal performance, GA stands out for its computational efficiency and ability to balance multiple targets. Originality/value: This study not only highlights the usefulness of GAs for designing hybrid microgrids that address renewable resource intermittency and economic viability but also contributes to the sustainable development goal by promoting sustainable and affordable solutions for residential communities.