Backtracking Search Algorithm for Microgrid Energy Scheduling in Day Ahead Market

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.