# Energy Management in Microgrids with Renewable Energy Sources: A Literature Review
## 3 Microgrid Energy Management with Renewable Energy Generation
### 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 management 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 management 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 management 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. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimization** & **Advantages** & **Disadvantages** \\ \hline MILP & Linear programming (LP) is a fast way to solve the problems and the linear constraints result in a convex feasible region, being guaranteed in many cases to obtain the global optimum solution. & Reliability and economic stochastical analysis. Limited capabilities for applications with not differentiable and/or continuous objective functions. \\ \hline MINLP & It uses simple operations to solve complex problems. It can obtain more than one optimal solution to choose from, which is an advantage over the MILP formulation. & High number of iterations (high computational effort). \\ \hline Dynamic programming (DP) & It can split the problem into subproblems, optimizing each subproblem and therefore solving sequential problems. & Complex implementation due to high number of recursive functions. \\ \hline Genetic algorithms (GA) & Population-based evolutionary algorithms that include operations such as crossover, mutation, and selection to find the optimal solution. Adequate convergence speed. Widely used in many fields. & Crossover and mutation parameters, and population and stopping criteria parameters must be set. \\ \hline Particle swarm optimization (PSO) & Good performance in scattering and optimization problems. & High computational complexities. \\ \hline Artificial bee colony & Robust population-based algorithm simple to implementate. Adequate convergence speed. & Complex formulation. \\ \hline Artificial Fish Swarm & Few parameters, fast convergence, high accuracy, and flexibility. & Same advantages of GA but without its disadvantages (crossover and mutation). \\ \hline Bacterial foraging & Size and non-linearity of the problem does not affect much. Converge to the optimal solution where analytical methods do not converge. & Large and complex search space. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of optimization mathematical models.