*3.9. Optimization Techniques*

Different optimization techniques are generally applied to maximize the power output of each particular source, minimize electricity costs, or maximize storage systems. Figure 4 presents the most commonly employed optimization techniques and algorithms presented in the literature review. Main advantages and disadvantages are briefly presented in Table 2.

Various techniques have been used by different researches. Energy managemen<sup>t</sup> and the optimization of control in a MG can have one or more objective functions. These functions can vary depending on the optimization problem presented. This can result in a mono-objective or multi-objective problem, which can include the minimization of costs (operation and maintenance cost, fuel cost, and degradation cost of storage elements such as batteries or capacitors), minimization of the emissions and minimization of the unmet load. Table 3 shows a comparison between the different optimization and managemen<sup>t</sup> methods used in the MGs. Different researchers have proposed metaheuristic techniques to solve the problem of optimization due to multi-constraints, multi-dimensional, and highly nonlinear combinatorial problems. Other authors presented stochastic dynamic programming methods for optimizing the energy managemen<sup>t</sup> problem with multidimensional objectives. Game theory has been proposed for some researchers to solve problems with conflicting objective functions.

**Figure 4.** Optimization techniques in microgrid energy management.


**Table 2.** Comparative analysis of optimization mathematical models.


**Table 3.** Analysis of microgrid optimization techniques.


**Table 3.** *Cont.*


**Table 3.** *Cont.*


**Table 3.** *Cont.*
