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, parti
The world transportation sector is transitioning from traditional fuel vehicles to electric vehicles (EV) in order to minimize carbon emissions from vehicles. Furthermore, for a cleaner environment, it is better to charge the EV using clean power such as solar photovoltaic (PV) systems. However, PV generating an insignificant sum of power which is not sufficient to apply in a specific application. To solve this issue, it is essential to improve the energy conversion efficiency by the Maximum Power Point Tracking (MPPT). In the literature, various algorithms have been used to enhance the MPPT efficiency which still have different limitations. Therefore, this study is developed to improve the MPPT for solar car system charge using Fuzzy Logic Controller (FLC) as the input of the voltage and current of PV systems is variable. Moreover, to overcome the FLC limitations and enhance the performance of the controller, the FLC-based Backtracking Search Algorithm (BSA) optimization method is dev