## 6 Discussion  
The LV radial network represents a real radial feeder with associated consumers and sources created as current sources that generate harmonics. The metaheuristic optimization method PSO was executed using an algorithm to determine the optimal settings of active filters in the LV network. The algorithm mentioned above in the optimization process was performed 24 times for 24 time intervals; that is, it monitored hourly load changes over one day in each test network node. The load changes were related to all consumer and source models with the corresponding harmonic spectra. The created variability during the day represented different states of the created LV network, which considers the possible future transformation of electrical networks, the inevitable increase in the integration of consumers with more pronounced nonlinearity, and the variability of the production of DGs. The observed cases are defined based on the results obtained during the evaluation of the optimization method. More precisely, the observed cases occurred when the test network was in an underloaded state, a normal state, an overload state, and a state of the test network when the values of _IHD_ and _THD_V were within limits prescribed by the standard, i.e., when no optimization process is required. The obtained results of APF layouts in the test network using the algorithms were performed in a 24 h interval and are shown in Table 3. The defined algorithm for the optimal location of the active filter used a maximum of two active filters. However, the algorithm could not be performed in certain parts of the day; i.e., at those times, it was not possible to add an active filter to the test network due to overload (labeled OL in the table). Figure 13: _THD_V values at 2:00 p.m. before and after the optimization process: (**a**) for all buses of the test network; (**b**) _THD_V values in phase a on all buses of the test network. When an overloaded state occurs, and it is impossible to integrate APFs, there are some other potential solutions. One possible solution is replacing existing PV systems with multifunctional devices (MFRES) that generate harmonic currents. Such systems should also include battery energy system storage (BESS) to reduce harmonics throughout the day. In addition to the above, it is possible to integrate only BESS with MFREs, which would replace existing PV sources and reduce the harmonic load of the observed network. Apart from replacing existing systems, possible solutions are load shifting, peak shaving, etc. Results obtained by the algorithm for determining the optimal location of the APF, which was achieved by defining the line with the highest third harmonic content after performing a harmonic calculation of power flows and, consequently, the corresponding node, do not have a unique solution. The obtained solutions define the crucial fact that the influence of the harmonic spectrum depends on the change in load and that the optimal location of the APF is a function of the load, which is of a variable nature. Suppose the branches in the test network are observed in both tariff periods and satisfy all the states in the network that occur in 24 h and the values defined by the norm. In \begin{table}
\begin{tabular}{c c c c c} \hline
**Time** & \begin{tabular}{c} **PCC Loads** \\ **Before** \\ **Optimization** \\ \end{tabular} & \begin{tabular}{c} **Location** \\ **(Bus Number)** \\ **for First APF** \\ \end{tabular} & \
\begin{tabular}{c} **Location** \\ **(Bus Number)** \\ **for Second APF** \\ \end{tabular} &
\begin{tabular}{c} **PCC Load** \\ **After** \\ **Optimization** \\ \end{tabular} \\ \hline  
& 25 & 97.80 \\ \hline \end{tabular}
\end{table} 
Table 3: Total result overview. that case, it is necessary to integrate APFs in each network branch. The above conclusion comes from the fact that in 21% of cases, the algorithm could not integrate APFs due to the state of the network, or in 16.67% of cases, the algorithm, due to the load level, could integrate only one APF.