查看文章

Various techniques have been used in the optimization of hybrid renewable energy systems (HRES). The most effective are metaheuristic algorithms based on artificial intelligence (AI) because of their ability to handle various parameters such as multiple objectives, parallelism features that allow for simultaneous evaluation of multiple schemes, and their ability to obtain optimal results that are systematic and deterministic. However, in the application of optimization algorithms regarding the Nonfree lunch theorem, one algorithm performs better than the other in obtaining the best fitness function with convergence time. Therefore, the slime mould algorithm (SMA) prowess was tested in HRES optimization against two conflicting multi-objectives. The best fitness function for the total annual cost (TAC) was obtained with minimum convergence time, while the relationship between TAC and loss of load probability (LOLP) is shown to be proportionate to one another. The SMA provided optimal sizing of solar PV, hydro turbine, and biogas generator to meet the load requirement of the study area. In addition, the result shows that SMA is more promising in terms of convergence time.