## 4 Discussions  
A state estimation process analyses an electric network's operating conditions. It involves gathering all known information about the network to determine the most likely state. This process is commonly utilised in transmission networks to improve observability and optimise operating regimes. Although the estimation state process is frequently used in transmission networks, it has not been widely adopted in the distribution sector due to the lack of proper supervision and monitoring. Despite this, it can represent an essential EDN management component for DNOs. In this context, determining the optimal number of EDSs integrated into a SCADA system to perform on-site measurements used in the state estimation process of the EDNs represents a challenge both from a technical and economic point of view for DNOs. It involves developing a robust framework incorporating algorithms to model the EDN's conditions accurately. The state estimation process evaluates the EDN's state based on the components' various measurements and technical and topological parameters (branches of the feeders and transformers). The main state variables include the angle and magnitude of current and voltage in the EDN's branches and nodes. The RTUs, as parts of the SCSDA system integrated at the level of the EDSs, can provide a complete on-site measurement set. But, as long as distribution networks have a large number of EDNs, installing the RTUs integrated into the SCADA system in each EDS, even if leads at complete observability, can have a significant impact on the investments of the DNO without being able to have a recovery period within the lifecycle. Consequently, the proposed framework removes this economic disadvantage and implements the SCADA system in the optimal location at the level of the EDN, minimising the number of RTUs. The framework uses two components (observable and unobservable) in the estimation process of the EDN. The observable component belongs to the SCADA system and ensures the measurement sets from the pilot EDSs. The unobservable component offers the data sets from the EDSs without monitoring based on the C-MLR Method, which uses the hybrid clustering-regression models. The results indicate a substantial improvement in the estimation process of the loads at the level of EDSs without monitoring, highlighted through the APE and MAPE values, using the C-MLR method compared to the SLR [27] and TLPs [13] methods (see Table 7). The maximum value decreased from 30.9% (calculated for SLR method) and 14.4% (calculated for TLPs method) to 6.3%, and the median value from 11.8% (calculated for SLR method) and 9.0% (calculated for TLPs method) to 2.8%. The SLR method led to the highest variation in the APEs in the range of [4.1%, 30.9%], followed by the TLPs method with a range of [3.1%, 25.4%]. The proposed method has the least variation, in the range of [0.0%, 6.3%]. Figure 22: The PE of the aggregated values of the state variables associated with the analysed period. The MAPEs calculated for the three methods emphasise the same observations regarding decreasing from 12.8% in the SLR method to 9.9% in the TLPs method until 2.6% in the C-MLR method (proposed) compared with the real data. Using the obtained values of the powers from the EDSs with the proposed method, the estimation state of the EDNs showed that variables represented by the injected/requested active and reactive powers, total active and reactive losses, and capacitive reactive power had MAPEs below 1%. There are some limits regarding the use of the proposed framework presented in the following. One of the working assumptions refers to continued communication between the RTUs and the central server of the SCADA system from the HV/MV EDS, which supplies the EDN, without failure of the communications lines or RTUs. If a failure of the communication system causes a loss of measurement data, then the state estimation can be affected. In these conditions, a missing data module should be introduced to estimate the missing values.