This study develops a decision support model based on a genetic algorithm (GA) to select renewable energy technologies considering sustainability criteria. The model integrates environmental, technical, and economic indicators to support energy planning in complex and uncertain scenarios.
The methodology implements a genetic algorithm to optimize the configuration of renewable technologies in a residential energy system. The evaluation incorporates real demand profiles, as well as standardized indicators such as carbon footprint, land use, operating and maintenance costs (LCOE), capacity factor, and reliability. The evaluation also includes a battery energy storage system (BESS). Sensitivity analyses were performed using the Morris and Sobol methods. The statistical significance and stability of the solutions were assessed using the non-parametric Friedman test and the Jaccard index, respectively.
The findings of the study indicated that wind energy and hybrid systems with BESS achieved the most balanced performance under different weighting schemes. The model demonstrated convergence in fewer than 50 generations, yielding consistent solutions across multiple iterations. The findings revealed that environmental criteria exerted the greatest influence on the results, and statistical tests confirmed significant differences between the evaluation approaches.
This study presents a robust and adaptable tool for the sustainable assessment of energy technologies, particularly useful in contexts where traditional decision-making methods are limited by complexity or a lack of structured information.
