Abstract
Energy utilization and cost minimization of renewable energy microgrids (REM) is an essential and difficult optimization challenge. However, most works have not considered heuristic and non-heuristic algorithms of REM complex equality and inequality requirements. In response, a single-objective optimization model for REM scheduling is developed and presented in this study. This microgrid model uses a very efficient and well-liked meta-heuristic optimization method called the Backtracking Search Algorithm (BSA). The BSA method locates a solution by constructing a solution piece by piece, adding levels over time, and using recursive calling. It is a method of large-scale search, and unlike other meta-heuristic approaches, it has a different model structure. Therefore, the proposed solution strategy may provide desirable outcomes with sufficient computational effort. Furthermore, in this research, the suggested process evaluates the consequences of another well-known meta-heuristic, particle swarm optimization. Its results demonstrate the effectiveness and superiority of the BSA over other REM determination techniques.
