## 6 Conclusions and Future Works  
This paper studied the problem regarding the optimal placement and sizing of D-STATCOMs for electrical distribution networks with radial and meshed configurations. The optimization problem was formulated using an MINLP model, where the binary variables were associated with the nodes where the D-STATCOMs must be placed. The continuous variables were related to the power flow solution, i.e., the voltages, currents, and power variables. The objective of the MINLP model was to reduce the expected annual energy loss costs after installing D-STATCOMs across the distribution network. The main contribution of this research is the consideration of three different load profiles to classify the distribution grid as a function of energy usage (residential, industrial, and commercial). To solve the MINLP model, a master-slave optimization strategy was proposed. The master stage was entrusted with defining the nodes where the D-STATCOMs will be located and their corresponding sizes. In the slave stage, an efficient power flow formulation was implemented, which allowed calculating the expected annual grid operating costs. Numerical results in the IEEE 33-bus grid with radial and meshed configurations confirmed the effectiveness of the proposed solution methodology in comparison with the MINLP solvers available in the GAMS software. An important result when comparing radial and meshed distribution topologies is that, even if the proposed optimization methodology is independent of the grid configuration, the expected annual operating costs are drastically affected by the grid configuration. The final objective function value was about USD/year 108,249.36, whereas, for the meshed topology, it was about USD/year 77,870.17, i.e., a difference of 30,379.19 dollars per year of operation. These differences are clearly explained by the nature of the meshed topol \begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Location (Nodes) and Sizing (Mvar)** & **Cost (USDS/Year)** \\ \hline Baseline case & — & 86,882.81 \\ XPRESS & [13 (0.2000), 16 (0.0453), 32 (0.3923)] & 79,535.02 \\ SBB, DICOPT, and LINDO & [13 (0.0960), 16 (0.0531), 32 (0.4480)] & 79,350.36 \\ \hline Proposed algorithm & [32 (0.2023), 30 (0.3944), 14 (0.1462)] & 77,870.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Best results obtained for the baseline case; the XPRESS, SBB, DICOPT, and LINDO optimizers; and the proposed methodology. ogy, where the voltage profiles are improved and a better distribution of the load flows is presented. The main limitations of the proposed master-slave optimization method are its dependence on the experience of the programmer with the whole algorithm, including the selection of the algorithm parameters, in addition the fact that a statistical analysis is required to determine the average performance of the methodology regarding the objective function values and the processing times. Another important limitation of our proposal corresponds to its extension to power systems: even if the SSA approach can define the location and size of the reactive power compensators, the power flow employed in this research is only applicable to distribution networks without voltage-controlled nodes, which is not the case of power systems, where multiple power generates control voltage profiles in different nodes of the network. This implies the need for replacing the power flow solver with one that can deal with multiple voltage-controlled sources.