## 2 Proposed Methodology  
Speaking in high-level terms, the methodology is to first gather data of loads, PVs and EVs which will serve as inputs. Then, a real-life feeder is modeled using PowerFactory 2022 software, on which the prepared and cleaned inputs are inserted. Quasi-Dynamic analyses are run and the software's results are exported. These results are then prepared, cleaned, and divided into the training set and the test set. These datasets are used to build a Deep Neural Network architecture and train/tweak it as necessary. After a good ML model is built, it is tested using the validation set, then the model's predictions on the test set are exported back to PowerFactory, where the final comparison of the results given by DNN and PowerFactory software are made. Besides these, secondary analyses are run to make the understanding more profound, such as the effects of PV penetration level changes and tap operations number reduction. This section further explains the approach to gathering the relevant data that will later be used to train and test the DNN. The topology of the 10 kV network of this study-case is shown in Figure 1. The feeder lies in a rural mountainous area, and it has around 60 km of lines in total. In Figure 1, the primary substation is shown in a red square, the secondary substations are shown in blue triangles, and the lines are shown in yellow. There are 41 equivalent loads, each connected to a secondary substation (SS). As of now, there are no Electric Vehicle (EV) charging stations and little to no photovoltaic generation. To study the effects of high DG, nine photovoltaic systems and two EV charging stations will be added. To better understand the role of the penetration level of DGs, five different analyses were conducted, each with a relative percentage of power generation, starting at 60% Pn and ending at 140% Pn. The voltage regulation will be performed by the automatic tap changer of the HV/MV transformer, by four LVRs installed at various points in the feeder and by transformers equipped with E-OLTCTs. The primary substation has 21 tap positions, ranging from \(-\)10 to +10, each with \(\pm\)1.25%Vn per tap. LVRs and E-OLTCS have five taps with a regulation range of \(\pm\)2.5% per tap when moved from the neutral position. The feeder's model made in PowerFactory is shown in Figure 2.  
To train the DNN, we need as many training examples as possible. These training examples will have to contain input data and outputs. The inputs, called features, will be the active loads, reactive loads, charging stations loads, and photovoltaic generations. The outputs, called labels, will be the correct tap positions of each E-OLTC and LVR. The data are real and come from smart meter measurements exported hourly. The data pertain to two full years, which translates to 17,520 values for each parameter. With 109 parameters in total, there will be 1,909,680 values. This feeder has a peak load of around 2.5 MW, while the aggregate peak installed power PVs is around 1.9 MWp. That means the ratio of PV generation to the peak load is roughly 76%. However, it is important to note that the peak load of the feeder and peak generation of PVs do not occur at the same hours as these are two different characteristics which depend on the location, which means that the ratio may well go beyond 100%. Figure 1: The 10 kV feeder considered. To better assess the effect of the penetration level of DG for a fixed peak load, a characteristic against voltage constraint violations will be plotted later. Various scenarios are tested to have a clearer understanding of the individual impact of E-OLTCs and LVRs on this MV feeder. Scenario I corresponds to the actual conditions where neither the E-OLTCs nor LVRs have automatic tap changing. Scenario II is the case where LVRs have automatic tap changing but E-OLTCs do not. Scenario III is the case where E-OLTCs have automatic tap changing while LVRs do not.