## 4 Optimization Techniques
### Optimization Techniques Regarding Design and Topology  
The most important part of a power system is its optimal planning and configuration. The optimization of designing a power system comprises the selection of the best technique or combination of schemes from all system design and equipment parameters. It also considers the constraints of the environment, various resources, and system operation. The latest studies in this field have been summarized below. In [56], the availability and cost of equipment at the design phase are chosen as the major constraints, and multi-objective particle swarm optimization (MOPSO) and genetic algorithm approaches are compared in light of scenarios of basic and maximum renewable energy resources integration. The results demonstrate that MOPSO has superior performance related to decreasing cost and improving availability. In [57], the authors propose a mixed-integer coordinated model for generation and storage optimization in a hybrid microgrid. It employs the benders decomposition algorithm to find the optimal solution. It is required to incorporate the attenuation effect of these energy storage devices in the existing model. In [58], the authors work on the optimal installation of the DC feeders to minimize the cost of installed distributed energy resources, converters, distribution lines, and other economic and security constraints. The results emphasize the installation of energy resources in the same AC-DC type power network to avoid converters and save cost. The DC lines installation cost gets minimized if DC loads are connected to DC buses integrated into DC feeders. In [47], the authors use a multi-objective approach employing mixed-integer nonlinear programming and Monte-Carlo technique for the remote islanded microgrid. The results demonstrate that as the fuel cost intensifies, the microgrid will be least dependent on fuel-based units. It depends more on renewable energy, hence minimizing the converter operational cost. In [59], the optimal sizing of power in a standalone microgrid has been analyzed with the help of a modified artificial bee colony (MABC). The proposed technique enhances the usage of renewable energy in the microgrid. In [60], the authors propose a novel two-stage algorithm for optimal sizing of a hybrid microgrid. The first stage deals with the optimal sizing, employing the metaheuristic technique, while the second stage caters to the required design problem with the help of a nonlinear solver. In [61], the authors propose a novel optimal sizing strategy by developing a stochastic model to curb the uncertainties and applying the optimal sizing objective function with respect to minimal operational cost. In [62], a mathematical model for sizing components in a hybrid microgrid is introduced which evaluates the load demand and proposes a system with minimal cost, keeping in view the uncertain nature of power from renewable sources. The model is applied in a town in Malaysia, and a feasible model is obtained with optimal cost. In [63], the authors employ equilibrium optimization (EO) to select optimal microgrid design for the given load at minimal cost and high stability at varying climate conditions. The results show fast convergence achieved with the EO algorithm as compared to other recent metaheuristic techniques. In [64], software HOMER is employed to optimally design a hybrid micro gird that can cater to the load demand while minimizing the overall cost, fuel consumption, and carbon dioxide emissions. Keeping in view all the references mentioned above, Table 1 incorporates a comparative analysis of all the techniques employed for each classification of optimization. It shows the latest research trends in all areas involving optimization techniques and also presents new models regarding hybrid microgrids. The majority of articles focused on developing the modified version of existing heuristic techniques to solve the concerned problem in a faster and better-formulated way.