## 5 Conclusions  
A three-layer framework for optimal placement of SCADA measurements with clustering-based EDS selection to estimate the state of the EDNs has been proposed. This framework has undergone rigorous testing to ensure its reliability and accuracy. The first layer includes the determination of the classes of the EDSs with similar features from the viewpoint of requested loads based on the K-means clustering algorithm. The second layer identifies the \"candidate\" classes and the pilot EDSs (representing the optimal solution) with the SCADA measurements placed. The optimal placement process aims to minimise the estimation errors based on the multiple linear regression models between the EDSs from the classes not included in the set of the \"candidate\" classes and the pilot EDSs. The third layer allows the state estimation of the EDNs based on the load values measured in the pilot EDEs (with the SCADA system implemented) and the other EDSs obtained through the regression models. The framework was tested in the case of a real urban EDN with 39 EDSs, where accuracy was ensured for on-site measurements in 12 of the 39 EDSs (representing 31% of EDSs integrated into the SCADA system). This solution is similar to the conclusions of the study from [3], where the integration degree in the SCADA system of the MV/LV EDSs at the level of most European DNOs is approximately 30%. The power estimation process at the level of the EDSs for the analysed period indicated a decrease in the MAPE of the proposed method about 5 times compared to the SLR method and about 4 times compared \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**State Variable**} & \multicolumn{3}{c}{**APE [\%]**} & \multirow{2}{*}{**MAPE [\%]**} \\ \cline{2-2} \cline{4-5}  & **Min** & **Max** & **Median** & \\ \hline SLR—method & 4.1 & 30.9 & 11.8 & 12.8 \\ TLP—method & 3.1 & 14.4 & 9.0 & 9.9 \\ C-MLR—method (proposed) & 0.0 & 6.3 & 2.8 & 2.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The synthesis of the results associated with the APEs and MAPEs. to the TLP method. The results of the tests revealed that the estimated MAPEs for the given period were below 1%, demonstrating the process's efficiency and the accuracy of the proposed method. The aggregated values of the energy amounts for the analysed period were also evaluated, and the errors were below 0.15%. The framework has been tested only in the EDNs without the distributed generation sources in this research stage. However, the authors are not stopping here. They are now working on an improved version that includes power injections from distributed generation sources. The degree of uncertainty at the level of the requested/injected powers is also being considered. Conceptualisation, G.G. and V.D. ; methodology, G.G. and V.D. ; software, G.G. ; validation, S.G., M.-A.B. and V.D. ; formal analysis, V.D., S.G. and M.-A.B. ; investigation, V.D., S.G. and M.-A.B. ; resources, G.G. ; data curation, G.G. ; writing--original draft preparation, G.G., V.D., S.G. and M.-A.B. ; writing--review and editing, G.G. ; visualisation, V.D. ; supervision, G.G. ; project administration, G.G. ; funding acquisition, G.G., S.G., M.-A.B. and V.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
EDN  
Electric distribution networks  
DNO  
distribution network operator  
SCADA  
supervisory, control, and acquisition system  
EDS  
Electric distribution substations  
MV  
medium voltage  
HV  
high voltage  
LV  
low voltage  
RTU  
remote terminal unit  
MAPE  
mean absolute percentage error  
APE  
average percentage error  
PE  
percentage error  
SLR  
simple linear regression  
TLP  
typical load profile  
C-MLR  
clustering-multiple linear regression