**5. Discussion**

Due to the extensive material (caused by many steps in the proposed research framework) presented in the previous section, comments about the obtained results are given in this section instead of in the previous one. The comments below are sorted, concerning the specific framework step referred to in Sections 4.2–4.6. The results obtained by investigation of the different optimization models and used workflow (Section 4.2) indicate that the simplest proposed model (Opt 1) gives the best results. The Opt 1 model finds optimal allocations of DGs, fixed values of DG power factors, and the trained ANN that generates the production profile of the controllable DG. Comparing results of single-objective and multiobjective optimizations (Table 4), it can be stated that the single-objective approach finds a better solution than two-objective optimization for the WS objective. In the case of single-objective optimization with the Wloss function, the solution is the same as that obtained by the two-objective optimization. Comparing the solutions obtained for different optimization models (Opt 1, Opt 2, and Opt 3) in Tables 4–6, unexpected results occur. As mentioned before, the best results are obtained for the Opt 1 model, but the authors

expected models Opt 2 and Opt 3 to be better. In the Opt 2 and Opt 3 models, the DG power factors are also controlled (variable in time); the hypothesis was that this scenario would give better solutions. There is a need for further research to determine the reason for these unexpected results. Comparing the results in Tables 7 and 8 with those in Tables 4–6, it can be concluded that the initial solution has a low impact on the solution quality. The results presented in Tables 10–12 emphasize the importance of the input data resolution. Except for the difference in objective function absolute amounts, the important difference is in the optimal DG allocations obtained for different data resolution. The results in Table 13 show the possibility of the proposed framework application in the case of using real data with a resolution higher than that used in the optimization model tuning. The input of different data into ANN give close obtained objective values.

As mentioned before in the Introduction section, there are a few research studies that considered all three optimization problem issues (time changes of load and production profiles, co-simulation approach, together with optimization of the optimal allocation and power control of DGs) simultaneously. It is difficult to compare the research results with the existing studies directly because different objective functions and distribution power networks are used in the literature. Ref. [6,7] considered variable load and production profiles without a co-simulation approach, and only optimal allocation of DGs was solved. In Ref. [6], the example of the microgrid (from the literature) is used to present the results of the DG power outputs optimization (with DG locations given in advance). The objective functions are the minimization of operational costs and pollutants emission. The presented results show conflicted objective values in ranges of USD 760–870 and 960–1115 kg for the operational costs and the emission amount, respectively. In [7], the objective function is losses minimization, and the proposed method is applied to the example of the IEEE 14 bus test network. The obtained results show a decrease in the network losses to about 38% of losses for the basic case (without installed DGs). Refs. [9,11] deal with the co-simulation approach to find the optimal allocation of DGs. Ref. [9] solves the optimal allocation problem (without considering the DGs power management) using constant loads, and the proposed method is applied on the IEEE 37 node test network. The objectives are minimizing the nodal voltage variations and installation costs of DGs. The results show a reduction in value of the objective function to about 64% of its initial value (without DGs). In [11], the objective function is minimizing the network power losses, and the constant load and DG outputs are considered. The proposed procedure is applied to the IEEE 123 bus distribution network. The obtained results give about 79% power loss reduction of the initial losses (without DGs). As stated above in the Introduction section, the research study presented in [20] is closest to the research presented here. In [20], the variable load and DG production profiles, as well as optimal allocation and power management, are considered. The external simulation tool is used to calculate energy loss as the objective function. The presented method is applied to a power distribution network consisting of 69 buses. The presented results show power loss reductions in ranges (depending on numbers of DGs) of 63–69% and 89–98% of the initial power losses (with no DGs) for constant load and the unity power factor and constant load and the optimized power factor, respectively. In the scenario, with variable load, the energy loss reduction is in the range (depend on objectives impacts in the objective function) of 72–95% of the initial energy loss. Ref. [20] also considers the active energy infeed from the upstream network, and the obtained results for this objective are in the range of 60–90%, reducing the basic case value (without installed DGs). Because the study [20] considers similar problem aspects as those in this research (the initial data and tested network are not the same), the research presented here can be relatively compared with [20]. The results presented here (Sections 4.2–4.6) show the next obtained values. The range (depending on the applied optimization model Opt 1–Opt 3) of energy loss reduction is 60–79% of values with no installed DGs. The reduced exchanged apparent energy is in the range of 74–94% of the amount without DGs. The proposed method shows an energy loss reduction for 15 min resolution data of 77% of the initial

value. This shows the applicability of the method in the case of using input data different from the data used in the optimization procedure.

Further research will be directed to investigation procedures for the estimation of DG power factor controls to additionally increase the optimal solution quality.
