## 4 Microgrid Energy Management Approaches
### Optimization Based Approach
#### 4.2.1 Metaheuristic Approaches  
Metaheuristic method-based energy management used in microgrid systems is mainly categorized into three major groups: evolutionary algorithms and other metaheuristic algorithms, such as GA and simulated annealing. Evolutionary algorithms, inspired by biological evolution processes such as mutation, recombination, selection, and reproduction, have found extensive applications in computational research. The study in [91] introduces an innovative approach, which proposed a Niching Evolutionary algorithm to optimize the allocation of RES and ESS within isolated microgrids. The research presented in [92] proposed an enhanced memetic algorithm for microgrid demand-side management in microgrids. Another noteworthy contribution is detailed in [93], where the water cycle algorithm is applied to the energy management of microgrids. This approach minimizes operational costs and emissions by efficiently coordinating distributed generation units alongside ESS. Lastly, the study in [94] explores using a GA, the most commonly encountered evolutionary algorithm in the literature, for optimizing day-ahead demand scheduling under uncertainty. This research highlights the algorithm's robustness in dealing with the stochastic nature of microgrid energy management, further underlining the critical role of evolutionary algorithms in advancing smart city initiatives.  
Swarm intelligence draws inspiration from the natural world's social living creatures, which exhibit coordinated behaviors in groups without central control. These phenomena, observable in entities such as ant colonies engaging in foraging, birds moving in unison, animals grouping for migration, hawks coordinating in pursuit of prey, and fish swimming in schools, serve as the foundation for algorithms designed to tackle intricate challenges through collective effort. Among these, the PSO algorithm stands out for its widespread application. This algorithm mimics the social behavior of birds and fish to navigate toward optimal solutions in a multidimensional space, which effectively addresses various computational and optimization problems. The study in [95] presents a probabilistic approach methodology for the enhancement of energy management in microgrids equipped with DER and ESS. The study introduces a self-adaptive modified q-PSO algorithm optimizing the energy management process by adapting to changes dynamically. Thus, it ensures efficient and reliable microgrid operations under diverse conditions. In [96], the study introduces an innovative approach for the energy management of isolated microgrids using a multi-layer ant colony optimization algorithm. This algorithm is designed to minimize electricity production costs by optimizing both day-ahead and real-time scheduling. In [97], the adaptive modified firefly algorithm is introduced for a scenario-based stochastic optimization framework for a microgrid including WT, PV, micro-turbines, FC, and ESS. Beyond swarm optimization and evolutionary algorithm-based metaheuristic techniques, several other metaheuristic algorithms have been employed to enhance the energy management of the microgrid. The study in [98] examines a microgrid in Oshawa, Ontario, Canada, by employing variable load models, which utilize the interior search algorithm to optimize hour-by-hour scheduling for day-ahead power scheduling issues. The two-stage scenario-based optimization approach is used for forecasting within a coordinated scheduling model, as detailed in [99].